Week 1 HW: Principles and Practices

Here you will see a proposal in which I attempt to overstep my bounds in the field of microbiology with my identity as an artist and designer. If any of my statements are incorrect, incomplete, or biased, I would like to point out that this is due to my inexperience in the field, and I would gladly accept your support in correcting them.

Q1. Describe a biological engineering application or tool you want to develop and why. This could be inspired by an idea for your HTGAA class project and/or something for which you are already doing in your research, or something you are just curious about.

After my master’s thesis research on pattern recognition and fractal thinking in art and design, I wanted to explore the potential application of these methods to biological anomalies. Following my mother’s diagnosis, I found papers exploring these possibilities. One of those studies proposed the use of fractal geometry to identify cellular anomalies associated with cancer (Dokukin et al., 2015). I would like to develop a tool in this area.

AFM-based fractal analysis for an early-stage cancer cell screening system which is a diagnostic tool that distinguishes normal, premalignant, and malignant cells by measuring changes in multi-fractality on the cell surface.

Q2. Describe one or more governance/policy goals related to ensuring that this application or tool contributes to an “ethical” future, like ensuring non-malfeasance (preventing harm). Break big goals down into two or more specific sub-goals.

Building on the personal and academic motivation described above, this proposed tool is an integrated software + data analysis system that quantifies the fractal properties of the cell surface at specific stages of cancer progression using AFM or similar high-resolution imaging methods. By relying on fractal indicators of anomalous (chaotic) surface behavior in early stages, it can generate signals prior to clinical disease progression, potentially providing new biomarkers for early clinical diagnosis. This offers quantitative biomonitoring capabilities beyond conventional histopathological assessment. In this section (regarding how the tool can be implemented technologically), I drew support from large language models (ChatGPT by OpenAI; Claude by Anthropic, 2026).

Q3. Describe at least three different potential governance “actions” by considering the four aspects below (Purpose, Design, Assumptions, Risks of Failure & “Success”). Try to outline a mix of actions (e.g. a new requirement/rule, incentive, or technical strategy) pursued by different “actors” (e.g. academic researchers, companies, federal regulators, law enforcement, etc). Draw upon your existing knowledge and a little additional digging, and feel free to use analogies to other domains (e.g. 3D printing, drones, financial systems, etc.).

• Purpose:
• Traditional medicine focuses on treating patients after they become ill rather than preventing disease. Morphological changes at the cellular and tissue levels are evaluated based on pathologists’ qualitative observations. This approach is largely dependent on human interpretation and cannot quantitatively capture micro-scale surface dynamics. The chance of early diagnosis is low. Here, however, there is a chance of early diagnosis.
• Design:
• An ethics committee composed of representatives from academia, industry, and the public sector. Due to the high cost of the method, democratization of access among different demographic groups in the future.
• Assumptions: If the software is open source, it may be misinterpreted in non-clinical settings (Bennett et al., 2009).
• Risks of Failure & “Success”:
• Dokukin’s study was conducted only with cervical epithelial cells; validation in different cancer types and populations is required (Dokukin et al., 2015).
• Fractal behavior has only been observed at a specific stage of development, and there is a deviation from this characteristic in more advanced stages.

Q4. Score (from 1-3 with, 1 as the best, or n/a) each of your governance actions against your rubric of policy goals. The following is one framework but feel free to make your own:

Q5. Drawing upon this scoring, describe which governance option, or combination of options, you would prioritize, and why. Outline any trade-offs you considered as well as assumptions and uncertainties. For this, you can choose one or more relevant audiences for your recommendation, which could range from the very local (e.g. to MIT leadership or Cambridge Mayoral Office) to the national (e.g. to President Biden or the head of a Federal Agency) to the international (e.g. to the United Nations Office of the Secretary-General, or the leadership of a multinational firm or industry consortia). These could also be one of the “actor” groups in your matrix.

Working with cancer patients requires that the legal process be conducted ethically. Patients must provide informed consent regarding whether their screening results will be used for research purposes, and their “right not to know” must be protected. Cell surface maps are biometric data and contain personal health information. Strong data protection protocols are required for the collection, storage, and sharing of this data. In particular, institutions such as insurance companies must be prevented from accessing this data without the patient’s consent, thereby protecting the scope of health insurance coverage or the patient’s right to work. Luigi Mangione, who was convicted in the UnitedHealthcare case, criticized the American healthcare system with a manifesto similar to that of “Unabomber” Ted Kaczynski (Kaczynski, 1995). This was a period when patients began to be excluded from health insurance coverage based on AI decisions (Mello et al., 2026).

Images

• Image1. Liver Cells, Beyza Batır, 2018
• Image2. AFM maps of adhesion of the AFM probe to the cell surface of (a) normal, (b) immortal (premalignant), and (c) cancer cells. SEM images of (d) normal, (e) immortal, and (f) cancer cells., in ‘Emergence of fractal geometry on the surface of human cervical epithelial cells during progression towards cancer’, Dokukin, M.E. et al., 2015

References

• Bennett, G. et al. (2009) ‘From synthetic biology to biohacking: Are we prepared?’, Nature Biotechnology, 27(12), pp. 1109–1111. doi:10.1038/nbt1209-1109.
• Dokukin, M.E. et al. (2015) ‘Emergence of fractal geometry on the surface of human cervical epithelial cells during progression towards cancer’, New Journal of Physics, 17(3), p. 033019. doi:10.1088/1367-2630/17/3/033019.
• Kaczynski, T. (1995) ‘Industrial society and its future’ Available at: https://web.cs.ucdavis.edu/~rogaway/classes/188/materials/Industrial%20Society%20and%20Its%20Future.pdf (Accessed: 6 February 2026).
• Mello, M.M. et al. (2026) ‘The Ai Arms Race in Health Insurance Utilization Review: Promises of efficiency and risks of supercharged flaws’, Health Affairs, 45(1), pp. 6–13. doi:10.1377/hlthaff.2025.00897.
• Todorovic, V. (2020) ‘Reimagining life (forms) with generative and Bio Art’, AI & SOCIETY, 36(4), pp. 1323–1329. doi:10.1007/s00146-020-00937-9.

Week 2 HW: DNA Read, Write, & Edit

Part 1: Benchling & In-silico Gel Art

First, I checked how to find Lambda through the database. I rewatched the Bootcamp recording by Adrian Filips and week 2 files of HTGAA2025 as well as the HTGAA2026 Recitation recordings on Benchling Basics provided by Cholpisit (Ice) Kiattisewee, and reviewed all the notes and presentations.

NHI LAmbda webpage

Biolabs Lambda webpage

After checking the NIH website and found “Nucleotide” search; downloaded “Lambda (NP_040580.1)” on it, I turned back to the given Lambda page on Biolabs database, and copied to the notepad the proper FASTA data. Because the one that I downloaded from NIH got different bp lengths (Standard is 48,502 bp).

Left: Paul Vanouse, Latent Figure Protocol Skull and Bones; Right: Space Invaders

Since I wanted my work to be in the Vanouse style, I also researched projects conducted by Vanouse and began exploring what kind of figure I wanted to create and whether this method would make it possible. I reviewed work prepared by Peggy Yin (2023) and Kevin Tang (2025) from previous years.

Left: Kevin Tang (2025); Right: Peggy Yin (2023)

The protocols I followed in the continuation of the project are as follows:

Simulate Restriction Enzyme Digestion with the following Enzymes: EcoRI HindIII BamHI KpnI EcoRV SacI SalI

https://rcdonovan.com/gel-art

Part 3: Benchling & In-silico Gel Art

3.1. Choose your protein.

In recitation, we discussed that you will pick a protein for your homework that you find interesting. Which protein have you chosen and why? Using one of the tools described in recitation (NCBI, UniProt, google), obtain the protein sequence for the protein you chose.

AAA61217.2 thyroid peroxidase [Homo sapiens] MRALAVLSVTLVMACTEAFFPFISRGKELLWGKPEESRVSSVLEESKRLVDTAMYATMQRNLKKRGILSG AQLLSFSKLPEPTSGVIARAAEIMETSIQAMKRKVNLKTQQSQHPTDALSEDLLSIIANMSGCLPYMLPP KCPNTCLANKYRPITGACNNRDHPRWGASNTALARWLPPVYEDGFSQPRGWNPGFLYNGFPLPPVREVTR HVIQVSNEVVTDDDRYSDLLMAWGQYIDHDIAFTPQSTSKAAFGGGSDCQMTCENQNPCFPIQLPEEARP AAGTACLPFYRSSAACGTGDQGALFGNLSTANPRQQMNGLTSFLDASTVYGSSPALERQLRNWTSAEGLL RVHGRLRDSGRAYLPFVPPRAPAACAPEPGNPGETRGPCFLAGDGRASEVPSLTALHTLWLREHNRLAAA LKALNAHWSADAVYQEARKVVGALHQIITLRDYIPRILGPEAFQQYVGPYEGYDSTANPTVSNVFSTAAF RFGHATIHPLVRRLDASFQEHPDLPGLWLHQAFFSPWTLLRGGGLDPLIRGLLARPAKLQVQDQLMNEEL TERLFVLSNSSTLDLASINLQRGRDHGLPGYNEWREFCGLPRLETPADLSTAIASRSVADKILDLYKHPD NIDVWLGGLAENFLPRARTGPLFACLIGKQMKALRDGDWFWWENSHVFTDAQRRELEKHSLSRVICDNTG LTRVPMDAFQVGKFPEDFESCDSITGMNLEAWRETFPQDDKCGFPESVENGDFVHCEESGRRVLVYSCRH GYELQGREQLTCTQEGWDFQPPLCKDVNECADGAHPPCHASARCRNTKGGFQCLCADPYELGDDGRTCVD SGRLPRVTWISMSLAALLIGGFAGLTSTVICRWTRTGTKSTLPISETGGGTPELRCGKHQAVGTSPQRAA AQDSEQESAGMEGRDTHRLPRAL

I chose Thyroid peroxidase (TPO) due to my hashimoto’s thyroiditis. https://www.uniprot.org/uniprotkb/P07202/entry

sp|P07202|PERT_HUMAN Thyroid peroxidase OS=Homo sapiens OX=9606 GN=TPO PE=1 SV=4 MRALAVLSVTLVMACTEAFFPFISRGKELLWGKPEESRVSSVLEESKRLVDTAMYATMQR NLKKRGILSPAQLLSFSKLPEPTSGVIARAAEIMETSIQAMKRKVNLKTQQSQHPTDALS EDLLSIIANMSGCLPYMLPPKCPNTCLANKYRPITGACNNRDHPRWGASNTALARWLPPV YEDGFSQPRGWNPGFLYNGFPLPPVREVTRHVIQVSNEVVTDDDRYSDLLMAWGQYIDHD IAFTPQSTSKAAFGGGADCQMTCENQNPCFPIQLPEEARPAAGTACLPFYRSSAACGTGD QGALFGNLSTANPRQQMNGLTSFLDASTVYGSSPALERQLRNWTSAEGLLRVHARLRDSG RAYLPFVPPRAPAACAPEPGIPGETRGPCFLAGDGRASEVPSLTALHTLWLREHNRLAAA LKALNAHWSADAVYQEARKVVGALHQIITLRDYIPRILGPEAFQQYVGPYEGYDSTANPT VSNVFSTAAFRFGHATIHPLVRRLDASFQEHPDLPGLWLHQAFFSPWTLLRGGGLDPLIR GLLARPAKLQVQDQLMNEELTERLFVLSNSSTLDLASINLQRGRDHGLPGYNEWREFCGL PRLETPADLSTAIASRSVADKILDLYKHPDNIDVWLGGLAENFLPRARTGPLFACLIGKQ MKALRDGDWFWWENSHVFTDAQRRELEKHSLSRVICDNTGLTRVPMDAFQVGKFPEDFES CDSITGMNLEAWRETFPQDDKCGFPESVENGDFVHCEESGRRVLVYSCRHGYELQGREQL TCTQEGWDFQPPLCKDVNECADGAHPPCHASARCRNTKGGFQCLCADPYELGDDGRTCVD SGRLPRVTWISMSLAALLIGGFAGLTSTVICRWTRTGTKSTLPISETGGGTPELRCGKHQ AVGTSPQRAAAQDSEQESAGMEGRDTHRLPRAL

[Example from our group homework, you may notice the particular format — The example below came from UniProt]

sp|P03609|LYS_BPMS2 Lysis protein OS=Escherichia phage MS2 OX=12022 PE=2 SV=1 METRFPQQSQQTPASTNRRRPFKHEDYPCRRQQRSSTLYVLIFLAIFLSKFTNQLLLSLL EAVIRTVTTLQQLLT

3.2. Reverse Translate: Protein (amino acid) sequence to DNA (nucleotide) sequence.

The Central Dogma discussed in class and recitation describes the process in which DNA sequence becomes transcribed and translated into protein. The Central Dogma gives us the framework to work backwards from a given protein sequence and infer the DNA sequence that the protein is derived from. Using one of the tools discussed in class, NCBI or online tools (google “reverse translation tools”), determine the nucleotide sequence that corresponds to the protein sequence you chose above.

[Example: Get to the original sequence of phage MS2 L-protein from its genome phage MS2 genome - Nucleotide - NCBI]

Lysis protein DNA sequence atggaaacccgattccctcagcaatcgcagcaaactccggcatctactaatagacgccggccattcaaacatgaggattacccatgtcgaagacaacaaagaagttcaactctttatgtattgatcttcctcgcgatctttctctcgaaatttaccaatcaattgcttctgtcgctactggaagcggtgatccgcacagtgacgactttacagcaattgcttacttaa

3.3. Codon optimization.

Once a nucleotide sequence of your protein is determined, you need to codon optimize your sequence. You may, once again, utilize google for a “codon optimization tool”. In your own words, describe why you need to optimize codon usage. Which organism have you chosen to optimize the codon sequence for and why?

[Example from Codon Optimization Tool | Twist Bioscience while avoiding Type IIs enzyme recognition sites BsaI, BsmBI, and BbsI]

Lysis protein DNA sequence with Codon-Optimization ATGGAAACCCGCTTTCCGCAGCAGAGCCAGCAGACCCCGGCGAGCACCAACCGCCGCCGCCCGTTCAAACATGAAGATTATCCGTGCCGTCGTCAGCAGCGCAGCAGCACCCTGTATGTGCTGATTTTTCTGGCGATTTTTCTGAGCAAATTCACCAACCAGCTGCTGCTGAGCCTGCTGGAAGCGGTGATTCGCACAGTGACGACCCTGCAGCAGCTGCTGACCTAA

3.4. You have a sequence! Now what?

What technologies could be used to produce this protein from your DNA? Describe in your words the DNA sequence can be transcribed and translated into your protein. You may describe either cell-dependent or cell-free methods, or both.

3.5. [Optional] How does it work in nature/biological systems?

Describe how a single gene codes for multiple proteins at the transcriptional level. Try aligning the DNA sequence, the transcribed RNA, and also the resulting translated Protein!

Part 4: Prepare a Twist DNA Synthesis Order**

This is a practice exercise, not necessarily your real Twist order! (done)

4.1. Create a Twist account and a Benchling account (done)

4.2. Build Your DNA Insert Sequence

For example, let’s make a sequence that will make E. coli glow fluorescent green under UV light by constitutively (always) expressing sfGFP (a green fluorescent protein):

In Benchling, select New DNA/RNA sequence Give your insert sequence a name and select DNA with a Linear topology (this is a linear sequence that will be inserted into a circular backbone vector of our choosing). Go through each piece of the given DNA sequences highlighted below (Promoter, RBS, Start Codon, Coding Sequence, His Tag, Stop Codon, Terminator) and paste the sequences into the Benchling file one after the other (replacing the coding sequence with your codon optimized DNA sequence of interest!). Each time you add a new piece of the sequence, make sure to annotate by right clicking over the sequence and creating an annotation that describes what each piece (e.g., Promoter, RBS, etc.) is (see image below). Promoter (e.g. BBa_J23106): TTTACGGCTAGCTCAGTCCTAGGTATAGTGCTAGC RBS (e.g. BBa_B0034 with spacers for optimal expression): CATTAAAGAGGAGAAAGGTACC Start Codon: ATG Coding Sequence (your codon optimized DNA for a protein of interest, sfGFP for example): AGCAAAGGAGAAGAACTTTTCACTGGAGTTGTCCCAATTCTTGTTGAATTAGATGGTGATGTTAATGGGCACAAATTTTCTGTCCGTGGAGAGGGTGAAGGTGATGCTACAAACGGAAAACTCACCCTTAAATTTATTTGCACTACTGGAAAACTACCTGTTCCGTGGCCAACACTTGTCACTACTCTGACCTATGGTGTTCAATGCTTTTCCCGTTATCCGGATCACATGAAACGGCATGACTTTTTCAAGAGTGCCATGCCCGAAGGTTATGTACAGGAACGCACTATATCTTTCAAAGATGACGGGACCTACAAGACGCGTGCTGAAGTCAAGTTTGAAGGTGATACCCTTGTTAATCGTATCGAGTTAAAGGGTATTGATTTTAAAGAAGATGGAAACATTCTTGGACACAAACTCGAGTACAACTTTAACTCACACAATGTATACATCACGGCAGACAAACAAAAGAATGGAATCAAAGCTAACTTCAAAATTCGCCACAACGTTGAAGATGGTTCCGTTCAACTAGCAGACCATTATCAACAAAATACTCCAATTGGCGATGGCCCTGTCCTTTTACCAGACAACCATTACCTGTCGACACAATCTGTCCTTTCGAAAGATCCCAACGAAAAGCGTGACCACATGGTCCTTCTTGAGTTTGTAACTGCTGCTGGGATTACACATGGCATGGATGAGCTCTACAAA 7x His Tag (Let’s add a 7×His tag at the C-terminus of the protein to enable protein purification from E. coli): CATCACCATCACCATCATCAC Stop Codon: TAA Terminator (e.g. BBa_B0015): CCAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTATCTGTTGTTTGTCGGTGAACGCTCTCTACTAGAGTCACACTGGCTCACCTTCGGGTGGGCCTTTCTGCGTTTATA Once you’ve completed this, click on Linear Map to preview the entire sequence. If you intend to have a TA review a sequence in the future, this is a good way to verify that all sections are annotated! This is not required for this exercise, but to share your design with others, please ensure that link sharing is turned on! (Optional) Share your final sequence link with a TA for review!

This insert sequence you built is commonly referred to as an expression cassette in molecular biology (a sequence you can drop into any vector and it’ll perform its function). Go ahead and download the FASTA file for the sequence you made. It’s helpful to visualize DNA designs using SBOL Canvas (Synthetic Biology Open Language) to convey your designs. Here’s an example of what you just annotated in Benchling: https://sbolstandard.org/

4.3. On Twist, Select The “Genes” Option

4.4. Select “Clonal Genes” option

For this demonstration, we’ll choose Clonal Genes. You’ll select clonal genes or gene fragments depending on your final project.

Historically, HTGAA projects using clonal genes (circular DNA) have reached experimental results 1-2 weeks quicker because they can be transformed directly into E. coli without additional assembly.

Gene fragments (linear DNA) offer greater design flexibility but typically require an assembly or cloning step prior to transformation. An advantage is If designed with the appropriate exonuclease protection, gene fragments can be used directly in cell-free expression.

4.5. Import your sequence

You just took an amino acid sequence of interest and converted it into DNA, codon optimized it, and built an expression cassette around it! Choose the Nucleotide Sequence option and Upload Sequence File to upload your FASTA file.

4.6. Choose Your Vector

Since we’re ordering a clonal gene, you will need to refer to Twist’s Vector Catalog to choose your circular backbone. You can think of this as taking your linear expression cassette for your protein of interest, and completing the rest of the circle!

The backbone confers many special properties like antibiotic resistance, an origin of replication, and more. Discuss with your node to decide on appropriate antibiotic options. At MIT/Harvard, you can use Ampicillin, Chloramphenicol, or Kanamycin resistance.

Twist vectors do not contain restriction sites near the insert fragment, so make sure to flank your design with cut sites if you are intending to extract this DNA insert fragment later.

For this demonstration, choose a Twist cloning vectors like pTwist Amp High Copy.

Click into your sequence and select download construct (GenBank) to get the full plasmid sequence:

Go back to your Benchling account. Inside of a folder, click the import DNA/RNA sequence button and upload the GenBank file you just downloaded.

This is the plasmid you just built with your expression cassette included. Congratulations on building your first plasmid!

Part 5: DNA Read/Write/Edit

5.1 DNA Read (i) What DNA would you want to sequence (e.g., read) and why? This could be DNA related to human health (e.g. genes related to disease research), environmental monitoring (e.g., sewage waste water, biodiversity analysis), and beyond (e.g. DNA data storage, biobank).

(ii) In lecture, a variety of sequencing technologies were mentioned. What technology or technologies would you use to perform sequencing on your DNA and why? Also answer the following questions:

1. Is your method first-, second- or third-generation or other? How so?
2. 2.- What is your input? How do you prepare your input (e.g. fragmentation, adapter ligation, PCR)? List the essential steps.
3. What are the essential steps of your chosen sequencing technology, how does it decode the bases of your DNA sample (base calling)?
4. What is the output of your chosen sequencing technology?

5.2 DNA Write

(i) What DNA would you want to synthesize (e.g., write) and why? These could be individual genes, clusters of genes or genetic circuits, whole genomes, and beyond. As described in class thus far, applications could range from therapeutics and drug discovery (e.g., mRNA vaccines and therapies) to novel biomaterials (e.g. structural proteins), to sensors (e.g., genetic circuits for sensing and responding to inflammation, environmental stimuli, etc.), to art (DNA origamis). If possible, include the specific genetic sequence(s) of what you would like to synthesize! You will have the opportunity to actually have Twist synthesize these DNA constructs! :)

See some famous examples of DNA design

(ii) What technology or technologies would you use to perform this DNA synthesis and why? Also answer the following questions:

What are the essential steps of your chosen sequencing methods? What are the limitations of your sequencing method (if any) in terms of speed, accuracy, scalability?

5.3 DNA Edit

(i) What DNA would you want to edit and why? In class, George shared a variety of ways to edit the genes and genomes of humans and other organisms. Such DNA editing technologies have profound implications for human health, development, and even human longevity and human augmentation. DNA editing is also already commonly leveraged for flora and fauna, for example in nature conservation efforts, (animal/plant restoration, de-extinction), or in agriculture (e.g. plant breeding, nitrogen fixation). What kinds of edits might you want to make to DNA (e.g., human genomes and beyond) and why?

(ii) What technology or technologies would you use to perform these DNA edits and why? Also answer the following questions:

1. How does your technology of choice edit DNA? What are the essential steps?
2. What preparation do you need to do (e.g. design steps) and what is the input (e.g. DNA template, enzymes, plasmids, primers, guides, cells) for the editing?
3. What are the limitations of your editing methods (if any) in terms of efficiency or precision?

Week 3 HW: Lab Automation

Python Script for Opentrons Artwork

I have created Space Invaders with green and red because my node Designer Cells mentioned they have only red (mrfp1) and green (sfGFP) right now. I have evil plans to create also text “Designer Cells” down the Space Invaders logo:) - done!-

Space Invaders - Opentrons Bio Art

Designer: Beyza Batır
Design: Space Invaders [mrfp1(red)+sfGFP(green)]

Google Colab

Simulation

Protocol Code

from opentrons import types

metadata = {
    'author': 'Beyza Batır',
    'protocolName': 'HTGAA Opentrons Lab',
    'description': 'SpaceInvaders',
    'source': 'HTGAA 2026 Opentrons Lab',
    'apiLevel': '2.20'
}

TIP_RACK_DECK_SLOT = 9
COLORS_DECK_SLOT = 6
AGAR_DECK_SLOT = 5
PIPETTE_STARTING_TIP_WELL = 'A1'

well_colors = {
    'A1': 'Red',
    'B1': 'Green',
}

def run(protocol):
    # --- Load labware ---
    tips_20ul = protocol.load_labware('opentrons_96_tiprack_20ul', TIP_RACK_DECK_SLOT, 'Opentrons 20uL Tips')
    pipette_20ul = protocol.load_instrument("p20_single_gen2", "right", [tips_20ul])
    temperature_module = protocol.load_module('temperature module gen2', COLORS_DECK_SLOT)
    temperature_plate = temperature_module.load_labware('opentrons_96_aluminumblock_generic_pcr_strip_200ul', 'Cold Plate')
    color_plate = temperature_plate
    agar_plate = protocol.load_labware('htgaa_agar_plate', AGAR_DECK_SLOT, 'Agar Plate')
    center_location = agar_plate['A1'].top()
    pipette_20ul.starting_tip = tips_20ul.well(PIPETTE_STARTING_TIP_WELL)

    # --- Helper functions ---
    def location_of_color(color_string):
        for well, color in well_colors.items():
            if color.lower() == color_string.lower():
                return color_plate[well]
        raise ValueError(f"No well found with color {color_string}")

    def dispense_and_detach(pipette, volume, location):
        above_location = location.move(types.Point(z=5))
        pipette.move_to(above_location)
        pipette.dispense(volume, location)
        pipette.move_to(above_location)

    # --- Coordinates from GUI ---
    mrfp1_points = [(-9.9, 38.5),(-7.7, 38.5),(-5.5, 38.5),(-3.3, 38.5),(-1.1, 38.5),(1.1, 38.5),(3.3, 38.5),(5.5, 38.5),(7.7, 38.5),(9.9, 38.5),(-16.5, 36.3),(-14.3, 36.3),(-12.1, 36.3),(-9.9, 36.3),(-7.7, 36.3),(-5.5, 36.3),(-3.3, 36.3),(-1.1, 36.3),(1.1, 36.3),(3.3, 36.3),(5.5, 36.3),(7.7, 36.3),(9.9, 36.3),(12.1, 36.3),(14.3, 36.3),(16.5, 36.3),(-20.9, 34.1),(-18.7, 34.1),(-16.5, 34.1),(-14.3, 34.1),(-12.1, 34.1),(-9.9, 34.1),(-7.7, 34.1),(-5.5, 34.1),(-3.3, 34.1),(-1.1, 34.1),(1.1, 34.1),(3.3, 34.1),(5.5, 34.1),(7.7, 34.1),(9.9, 34.1),(12.1, 34.1),(14.3, 34.1),(16.5, 34.1),(18.7, 34.1),(20.9, 34.1),(-23.1, 31.9),(-20.9, 31.9),(-18.7, 31.9),(-16.5, 31.9),(-14.3, 31.9),(-12.1, 31.9),(-9.9, 31.9),(-7.7, 31.9),(-5.5, 31.9),(-3.3, 31.9),(-1.1, 31.9),(1.1, 31.9),(3.3, 31.9),(5.5, 31.9),(7.7, 31.9),(9.9, 31.9),(12.1, 31.9),(14.3, 31.9),(16.5, 31.9),(18.7, 31.9),(20.9, 31.9),(23.1, 31.9),(-25.3, 29.7),(-23.1, 29.7),(-20.9, 29.7),(-18.7, 29.7),(-16.5, 29.7),(-14.3, 29.7),(-12.1, 29.7),(-9.9, 29.7),(-7.7, 29.7),(-5.5, 29.7),(-3.3, 29.7),(-1.1, 29.7),(1.1, 29.7),(3.3, 29.7),(5.5, 29.7),(7.7, 29.7),(9.9, 29.7),(12.1, 29.7),(14.3, 29.7),(16.5, 29.7),(18.7, 29.7),(20.9, 29.7),(23.1, 29.7),(25.3, 29.7),(-27.5, 27.5),(-25.3, 27.5),(-23.1, 27.5),(-20.9, 27.5),(-18.7, 27.5),(-16.5, 27.5),(-14.3, 27.5),(-12.1, 27.5),(-9.9, 27.5),(-7.7, 27.5),(-5.5, 27.5),(-3.3, 27.5),(-1.1, 27.5),(1.1, 27.5),(3.3, 27.5),(5.5, 27.5),(7.7, 27.5),(9.9, 27.5),(12.1, 27.5),(14.3, 27.5),(16.5, 27.5),(18.7, 27.5),(20.9, 27.5),(23.1, 27.5),(25.3, 27.5),(27.5, 27.5),(-29.7, 25.3),(-27.5, 25.3),(-25.3, 25.3),(-23.1, 25.3),(-20.9, 25.3),(-18.7, 25.3),(-16.5, 25.3),(-14.3, 25.3),(-12.1, 25.3),(-9.9, 25.3),(-7.7, 25.3),(-5.5, 25.3),(-3.3, 25.3),(-1.1, 25.3),(1.1, 25.3),(3.3, 25.3),(5.5, 25.3),(7.7, 25.3),(9.9, 25.3),(12.1, 25.3),(14.3, 25.3),(16.5, 25.3),(18.7, 25.3),(20.9, 25.3),(23.1, 25.3),(25.3, 25.3),(27.5, 25.3),(29.7, 25.3),(-31.9, 23.1),(-29.7, 23.1),(-27.5, 23.1),(-25.3, 23.1),(-23.1, 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-29.7),(-7.7, -29.7),(-5.5, -29.7),(-3.3, -29.7),(-1.1, -29.7),(1.1, -29.7),(3.3, -29.7),(5.5, -29.7),(7.7, -29.7),(9.9, -29.7),(12.1, -29.7),(14.3, -29.7),(16.5, -29.7),(18.7, -29.7),(20.9, -29.7),(23.1, -29.7),(25.3, -29.7),(-23.1, -31.9),(-20.9, -31.9),(-18.7, -31.9),(-16.5, -31.9),(-14.3, -31.9),(-12.1, -31.9),(-9.9, -31.9),(-7.7, -31.9),(-5.5, -31.9),(-3.3, -31.9),(-1.1, -31.9),(1.1, -31.9),(3.3, -31.9),(5.5, -31.9),(7.7, -31.9),(9.9, -31.9),(12.1, -31.9),(14.3, -31.9),(16.5, -31.9),(18.7, -31.9),(20.9, -31.9),(23.1, -31.9),(-20.9, -34.1),(-18.7, -34.1),(-16.5, -34.1),(-14.3, -34.1),(-12.1, -34.1),(-9.9, -34.1),(-7.7, -34.1),(-5.5, -34.1),(-3.3, -34.1),(-1.1, -34.1),(1.1, -34.1),(3.3, -34.1),(5.5, -34.1),(7.7, -34.1),(9.9, -34.1),(12.1, -34.1),(14.3, -34.1),(16.5, -34.1),(18.7, -34.1),(20.9, -34.1),(-16.5, -36.3),(-14.3, -36.3),(-12.1, -36.3),(-9.9, -36.3),(-7.7, -36.3),(-5.5, -36.3),(-3.3, -36.3),(-1.1, -36.3),(1.1, -36.3),(3.3, -36.3),(5.5, -36.3),(7.7, -36.3),(9.9, -36.3),(12.1, -36.3),(14.3, -36.3),(16.5, -36.3),(-9.9, -38.5),(-7.7, -38.5),(-5.5, -38.5),(-3.3, -38.5),(-1.1, -38.5),(1.1, -38.5),(3.3, -38.5),(5.5, -38.5),(7.7, -38.5),(9.9, -38.5)]

    sfgfp_points = [(-20.9, 23.1),(-18.7, 23.1),(18.7, 23.1),(20.9, 23.1),(-20.9, 20.9),(-18.7, 20.9),(18.7, 20.9),(20.9, 20.9),(-20.9, 18.7),(-18.7, 18.7),(18.7, 18.7),(20.9, 18.7),(-16.5, 16.5),(-14.3, 16.5),(-12.1, 16.5),(12.1, 16.5),(14.3, 16.5),(16.5, 16.5),(-16.5, 14.3),(-14.3, 14.3),(-12.1, 14.3),(12.1, 14.3),(14.3, 14.3),(16.5, 14.3),(-20.9, 12.1),(-18.7, 12.1),(-16.5, 12.1),(-14.3, 12.1),(-12.1, 12.1),(-9.9, 12.1),(-7.7, 12.1),(-5.5, 12.1),(-3.3, 12.1),(-1.1, 12.1),(1.1, 12.1),(3.3, 12.1),(5.5, 12.1),(7.7, 12.1),(9.9, 12.1),(12.1, 12.1),(14.3, 12.1),(16.5, 12.1),(18.7, 12.1),(20.9, 12.1),(-20.9, 9.9),(-18.7, 9.9),(-16.5, 9.9),(-14.3, 9.9),(-12.1, 9.9),(-9.9, 9.9),(-7.7, 9.9),(-5.5, 9.9),(-3.3, 9.9),(-1.1, 9.9),(1.1, 9.9),(3.3, 9.9),(5.5, 9.9),(7.7, 9.9),(9.9, 9.9),(12.1, 9.9),(14.3, 9.9),(16.5, 9.9),(18.7, 9.9),(20.9, 9.9),(-20.9, 7.7),(-18.7, 7.7),(-16.5, 7.7),(-14.3, 7.7),(-12.1, 7.7),(-9.9, 7.7),(-7.7, 7.7),(-5.5, 7.7),(-3.3, 7.7),(-1.1, 7.7),(1.1, 7.7),(3.3, 7.7),(5.5, 7.7),(7.7, 7.7),(9.9, 7.7),(12.1, 7.7),(14.3, 7.7),(16.5, 7.7),(18.7, 7.7),(20.9, 7.7),(-29.7, 5.5),(-27.5, 5.5),(-25.3, 5.5),(-23.1, 5.5),(-20.9, 5.5),(-18.7, 5.5),(-9.9, 5.5),(-7.7, 5.5),(-5.5, 5.5),(-3.3, 5.5),(-1.1, 5.5),(1.1, 5.5),(3.3, 5.5),(5.5, 5.5),(7.7, 5.5),(9.9, 5.5),(18.7, 5.5),(20.9, 5.5),(23.1, 5.5),(25.3, 5.5),(27.5, 5.5),(29.7, 5.5),(-29.7, 3.3),(-27.5, 3.3),(-25.3, 3.3),(-23.1, 3.3),(-20.9, 3.3),(-18.7, 3.3),(-9.9, 3.3),(-7.7, 3.3),(-5.5, 3.3),(-3.3, 3.3),(-1.1, 3.3),(1.1, 3.3),(3.3, 3.3),(5.5, 3.3),(7.7, 3.3),(9.9, 3.3),(18.7, 3.3),(20.9, 3.3),(23.1, 3.3),(25.3, 3.3),(27.5, 3.3),(29.7, 3.3),(-29.7, 1.1),(-27.5, 1.1),(-25.3, 1.1),(-23.1, 1.1),(-20.9, 1.1),(-18.7, 1.1),(-9.9, 1.1),(-7.7, 1.1),(-5.5, 1.1),(-3.3, 1.1),(-1.1, 1.1),(1.1, 1.1),(3.3, 1.1),(5.5, 1.1),(7.7, 1.1),(9.9, 1.1),(18.7, 1.1),(20.9, 1.1),(23.1, 1.1),(25.3, 1.1),(27.5, 1.1),(29.7, 1.1),(-36.3, -1.1),(-34.1, -1.1),(-31.9, -1.1),(-29.7, -1.1),(-27.5, -1.1),(-25.3, -1.1),(-23.1, -1.1),(-20.9, -1.1),(-18.7, -1.1),(-16.5, -1.1),(-14.3, -1.1),(-12.1, -1.1),(-9.9, -1.1),(-7.7, -1.1),(-5.5, -1.1),(-3.3, -1.1),(-1.1, -1.1),(1.1, -1.1),(3.3, -1.1),(5.5, -1.1),(7.7, -1.1),(9.9, -1.1),(12.1, -1.1),(14.3, -1.1),(16.5, -1.1),(18.7, -1.1),(20.9, -1.1),(23.1, -1.1),(25.3, -1.1),(27.5, -1.1),(29.7, -1.1),(31.9, -1.1),(34.1, -1.1),(36.3, -1.1),(-36.3, -3.3),(-34.1, -3.3),(-31.9, -3.3),(-29.7, -3.3),(-27.5, -3.3),(-25.3, -3.3),(-23.1, -3.3),(-20.9, -3.3),(-18.7, -3.3),(-16.5, -3.3),(-14.3, -3.3),(-12.1, -3.3),(-9.9, -3.3),(-7.7, -3.3),(-5.5, -3.3),(-3.3, -3.3),(-1.1, -3.3),(1.1, -3.3),(3.3, -3.3),(5.5, -3.3),(7.7, -3.3),(9.9, -3.3),(12.1, -3.3),(14.3, -3.3),(16.5, -3.3),(18.7, -3.3),(20.9, -3.3),(23.1, -3.3),(25.3, -3.3),(27.5, -3.3),(29.7, -3.3),(31.9, -3.3),(34.1, -3.3),(36.3, -3.3),(-36.3, -5.5),(-34.1, -5.5),(-31.9, -5.5),(-29.7, -5.5),(-27.5, -5.5),(-25.3, -5.5),(-23.1, -5.5),(-20.9, -5.5),(-18.7, -5.5),(-16.5, -5.5),(-14.3, -5.5),(-12.1, -5.5),(-9.9, -5.5),(-7.7, -5.5),(-5.5, -5.5),(-3.3, -5.5),(-1.1, -5.5),(1.1, -5.5),(3.3, -5.5),(5.5, -5.5),(7.7, -5.5),(9.9, -5.5),(12.1, -5.5),(14.3, -5.5),(16.5, -5.5),(18.7, -5.5),(20.9, -5.5),(23.1, -5.5),(25.3, -5.5),(27.5, -5.5),(29.7, -5.5),(31.9, -5.5),(34.1, -5.5),(36.3, -5.5),(-36.3, -7.7),(-34.1, -7.7),(-31.9, -7.7),(-23.1, -7.7),(-20.9, -7.7),(-18.7, -7.7),(-16.5, -7.7),(-14.3, -7.7),(-12.1, -7.7),(-9.9, -7.7),(-7.7, -7.7),(-5.5, -7.7),(-3.3, -7.7),(-1.1, -7.7),(1.1, -7.7),(3.3, -7.7),(5.5, -7.7),(7.7, -7.7),(9.9, -7.7),(12.1, -7.7),(14.3, -7.7),(16.5, -7.7),(18.7, -7.7),(20.9, -7.7),(23.1, -7.7),(31.9, -7.7),(34.1, -7.7),(36.3, -7.7),(-36.3, -9.9),(-34.1, -9.9),(-31.9, -9.9),(-23.1, -9.9),(-20.9, -9.9),(-18.7, -9.9),(-16.5, -9.9),(-14.3, -9.9),(-12.1, -9.9),(-9.9, -9.9),(-7.7, -9.9),(-5.5, -9.9),(-3.3, -9.9),(-1.1, -9.9),(1.1, -9.9),(3.3, -9.9),(5.5, -9.9),(7.7, -9.9),(9.9, -9.9),(12.1, -9.9),(14.3, -9.9),(16.5, -9.9),(18.7, -9.9),(20.9, -9.9),(23.1, -9.9),(31.9, -9.9),(34.1, -9.9),(36.3, -9.9),(-36.3, -12.1),(-34.1, -12.1),(-31.9, -12.1),(-23.1, -12.1),(-20.9, -12.1),(-18.7, -12.1),(18.7, -12.1),(20.9, -12.1),(23.1, -12.1),(31.9, -12.1),(34.1, -12.1),(36.3, -12.1),(-36.3, -14.3),(-34.1, -14.3),(-31.9, -14.3),(-23.1, -14.3),(-20.9, -14.3),(-18.7, -14.3),(18.7, -14.3),(20.9, -14.3),(23.1, -14.3),(31.9, -14.3),(34.1, -14.3),(36.3, -14.3),(-36.3, -16.5),(-34.1, -16.5),(-31.9, -16.5),(-23.1, -16.5),(-20.9, -16.5),(-18.7, -16.5),(18.7, -16.5),(20.9, -16.5),(23.1, -16.5),(31.9, -16.5),(34.1, -16.5),(36.3, -16.5),(-16.5, -18.7),(-14.3, -18.7),(-12.1, -18.7),(-9.9, -18.7),(-7.7, -18.7),(-5.5, -18.7),(5.5, -18.7),(7.7, -18.7),(9.9, -18.7),(12.1, -18.7),(14.3, -18.7),(16.5, -18.7),(-16.5, -20.9),(-14.3, -20.9),(-12.1, -20.9),(-9.9, -20.9),(-7.7, -20.9),(-5.5, -20.9),(5.5, -20.9),(7.7, -20.9),(9.9, -20.9),(12.1, -20.9),(14.3, -20.9),(16.5, -20.9)]

    # --- Patterning ---
    VOLUME = 1  # µL per dot

    # Red layer (mrfp1)
    pipette_20ul.pick_up_tip()
    for (x, y) in mrfp1_points:
        pipette_20ul.aspirate(VOLUME, location_of_color('Red'))
        target = center_location.move(types.Point(x=x, y=y, z=0))
        dispense_and_detach(pipette_20ul, VOLUME, target)
    pipette_20ul.drop_tip()

    # Green layer (sfgfp)
    pipette_20ul.pick_up_tip()
    for (x, y) in sfgfp_points:
        pipette_20ul.aspirate(VOLUME, location_of_color('Green'))
        target = center_location.move(types.Point(x=x, y=y, z=0))
        dispense_and_detach(pipette_20ul, VOLUME, target)
    pipette_20ul.drop_tip()
  ###

–

• I am using Claude as a console for errors.

• Submitted my Python file via Google form both HTGAA and Node.

Post-Lab Questions

One of the great parts about having an automated robot is being able to precisely mix, deposit, and run reactions without much intervention, and design and deploy experiments remotely.

Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications. I would like to mention from this research:

This paper is particularly relevant because it addresses a critical but often overlooked problem in laboratory automation: the gap between intended and actual liquid deposition. As demonstrated in the images captured by Chen (2026), this discrepancy becomes strikingly clear when comparing the physical petri dish under normal lighting conditions with its UV-illuminated counterpart. Under standard light, the dish appears largely as expected, with the deposited pattern barely distinguishable to the naked eye (Chen, 20256, Figure 2). However, when the same plate is examined under UV light, small, unwanted droplets become clearly visible in places where they were not present in the original design (Chen, 2026, Figures 3-4). The computer vision algorithm developed in this study successfully detects and maps these deviations, marking the spots with color codes according to their size to highlight the extent of the error.

Figure 1-4: Images captured from the Opentrons OT-2 liquid handling experiments by Yanchen Chen. Credit: Yanchen Chen (24.02.2026).

These satellite droplets arise from well-known physical phenomena in liquid handling, such as surface tension-driven splashing or residual liquid remaining on pipette tips between transfers. What makes this finding biologically significant is that in high-precision applications such as drug screening, dose-response assays, or microbial growth experiments, even a small unintended deposit can introduce a compound or organism into a zone where it was never meant to be. This cross-contamination would silently corrupt experimental results, and without a real-time quality control system, the researcher would have no way of knowing the data was compromised.

The novel contribution of this work is therefore not purely engineering: by enabling the Opentrons OT-2 to detect and flag these errors autonomously using computer vision, the system directly protects the integrity of biological experiments. This transforms the robot from a simple liquid-dispensing tool into a self-monitoring platform capable of ensuring experimental validity; a meaningful advancement for any biological application that depends on precise, contamination-free liquid handling.

References:

• Khan, S.U., Møller, V.K., Frandsen, R.J.N. et al. Real-time AI-driven quality control for laboratory automation: a novel computer vision solution for the opentrons OT-2 liquid handling robot. Appl Intell 55, 524 (2025). https://doi.org/10.1007/s10489-025-06334-3

• Shen, Y. (2026). Week 3 Lab: Lab Automation :: 2026a-yanchen-shen. [online] Available at: https://pages.htgaa.org/2026a/yanchen-shen/labs/week-03-lab-opentrons-art/index.html [Accessed 26 Feb. 2026].

Write a description about what you intend to do with automation tools for your final project. You may include example pseudocode, Python scripts, 3D printed holders, a plan for how to use Ginkgo Nebula, and more. You may reference this week’s recitation slide deck for lab automation details. While your description/project idea doesn’t need to be set in stone, we would like to see core details of what you would automate. This is due at the start of lecture and does not need to be tested on the Opentrons yet.

• Example 1: You are creating a custom fabric, and want to deposit art onto specific parts that need to be intertwined in odd ways. You can design a 3D printed holder to attach this fabric to it, and be able to deposit bio art on top. Check out the Opentrons 3D Printing Directory.

• Example 2: You are using the cloud laboratory to screen an array of biosensor constructs that you design, synthesize, and express using cell-free protein synthesis.

Echo transfer biosensor constructs and any required cofactors into specified wells. Bravo stamp in CPFS reagent master mix into all wells of a 96-well / 384-well plate. Multiflo dispense the CFPS lysate to all wells to start protein expression. PlateLoc seal the plate. Inheco incubate the plate at 37°C while the biosensor proteins are synthesized. XPeel remove the seal. PHERAstar measure fluorescence to compare biosensor responses.

Final Project Ideas

As explained in this week’s recitation, add 1-3 slides in your Node’s section of this slide deck with 3 ideas you have for an Individual Final Project. Be sure to put your name, city, and country on your slide!

Embedded slide deck of 1-3 slides with 3 ideas you have for an Individual Final Project. by naming (Beyza Batır, Izmir, Turkey)

I will upload my slides on CL powerpoint in DC Labs Student#7 section.

Brief

Reading & Resources Opentrons API Documentation: https://docs.opentrons.com/python-api/ Opentrons Artwork GUI Website: http://opentrons-art.rcdonovan.com/ Opentrons Artwork Colab: HTGAA26 Opentrons Colab Automation Equipment: HTGAA 2026 Recitation: Lab Automation, Opentrons Art, Intro to Cloud Laboratories

Extras

These extra designs were created within the “Automation Art Interface” to explore possibilities with different weights, colors, and area usage. I also created Designer Cells artworks for our node and this time corrected my mistakes (size, spacing, safe canvas margin) that did not comply with the requirements announced on HTGAA Google Colab. If needed, I can prepare Google Colab for all designs.

Week 4 HW: Protein Design I

Part A: Conceptual Questions

Answer any 9 of the following questions from Shuguang Zhang: (i.e. you can select two to skip)

A.1 How many molecules of amino acids do you take with a piece of 500 grams of meat? (on average an amino acid is ~100 Daltons)

• I will complete this homework and Final Project proposal as well..

A.2 Why do humans eat beef but do not become a cow, eat fish but do not become fish?

A.3 Why are there only 20 natural amino acids?

A.4 Can you make other non-natural amino acids? Design some new amino acids.

A.5 Where did amino acids come from before enzymes that make them, and before life started?

A.6 If you make an α-helix using D-amino acids, what handedness (right or left) would you expect?

A.7 Can you discover additional helices in proteins?

A.8 Why are most molecular helices right-handed?

A.9 Why do β-sheets tend to aggregate?

A.9.1 What is the driving force for β-sheet aggregation?

A.10 Why do many amyloid diseases form β-sheets?

A.10.1 Can you use amyloid β-sheets as materials?

A.11 Design a β-sheet motif that forms a well-ordered structure.

Part B: Protein Analysis and Visualization

In this part of the homework, you will be using online resources and 3D visualization software to answer questions about proteins. Pick any protein (from any organism) of your interest that has a 3D structure and answer the following questions:

B.1 Briefly describe the protein you selected and why you selected it.

• I B.2 Identify the amino acid sequence of your protein.
• How long is it? What is the most frequent amino acid? You can use this Colab notebook to count the frequency of amino acids.
• How many protein sequence homologs are there for your protein? Hint: Use Uniprot’s BLAST tool to search for homologs.
• Does your protein belong to any protein family?

B.3 Identify the structure page of your protein in RCSB

• When was the structure solved? Is it a good quality structure? Good quality structure is the one with good resolution. Smaller the better (Resolution: 2.70 Å)
• Are there any other molecules in the solved structure apart from protein?
• Does your protein belong to any structure classification family?

B.4 Open the structure of your protein in any 3D molecule visualization software:

• PyMol Tutorial Here (hint: ChatGPT is good at PyMol commands)
• Visualize the protein as “cartoon”, “ribbon” and “ball and stick”.
• Color the protein by secondary structure. Does it have more helices or sheets?
• Color the protein by residue type. What can you tell about the distribution of hydrophobic vs hydrophilic residues?
• Visualize the surface of the protein. Does it have any “holes” (aka binding pockets)?

Part C: Using ML-Based Protein Design Tools

In this section, we will learn about the capabilities of modern protein AI models and test some of them in your chosen protein.

C.1 Copy the HTGAA_ProteinDesign2026.ipynb notebook and set up a colab instance with GPU.

C.2 Choose your favorite protein from the PDB.

C.3 We will now try multiple things in the three sections below; report each of these results in your homework writeup on your HTGAA website:

C.4: Protein Language Modeling

Deep Mutational Scans

• Use ESM2 to generate an unsupervised deep mutational scan of your protein based on language model likelihoods.
• Can you explain any particular pattern? (choose a residue and a mutation that stands out)
• (Bonus) Find sequences for which we have experimental scans, and compare the prediction of the language model to experiment.

Latent Space Analysis

• Use the provided sequence dataset to embed proteins in reduced dimensionality.
• Analyze the different formed neighborhoods: do they approximate similar proteins?
• Place your protein in the resulting map and explain its position and similarity to its neighbors.

C.5: Protein Folding

Folding a protein

• Fold your protein with ESMFold.
• Do the predicted coordinates match your original structure?
• Try changing the sequence, first try some mutations, then large segments. Is your protein structure resilient to mutations?

C.6: Protein Generation

Inverse-Folding a protein: Let’s now use the backbone of your chosen PDB to propose sequence candidates via ProteinMPNN

• Analyze the predicted sequence probabilities and compare the predicted sequence vs the original one.
• Input this sequence into ESMFold. and compare the predicted structure to your original.

Part D: Group Brainstorm on Bacteriophage Engineering

D.1 Find a group of ~3–4 students

• We formed a group with Alya, Sydney, and Zander. Planning to have a Zoom meeting to discuss papers. D.2 Read through the Phage Reading material listed under “Reading & Resources” below.

D.3 Review the Bacteriophage Final Project Goals for engineering the L Protein:

• Increased stability (easiest)
• Higher titers (medium)
• Higher toxicity of lysis protein (hard) D.4 Brainstorm Session
• Choose one or two main goals from the list that you think you can address computationally (e.g., “We’ll try to stabilize the lysis protein,” or “We’ll attempt to disrupt its interaction with E. coli DnaJ.”).
• Write a 1-page proposal (bullet points or short paragraphs) describing:

• Which tools/approaches from recitation you propose using (e.g., “Use Protein Language Models to do in silico mutagenesis, then AlphaFold-Multimer to check complexes.”).*
• Why do you think those tools might help solve your chosen sub-problem?
• Name one or two potential pitfalls (e.g., “We lack enough training data on phage–bacteria interactions.”).
• Include a schematic of your pipeline. This resource may be useful: HTGAA Protein Engineering Tools

D.5 Each individually put your plan on your HTGAA website

• Include your group’s short plan for engineering a bacteriophage

Tools

• HTGAA Protein Engineering Tools spreadsheet
• NGLViewer: NGL Viewer is a collection of tools for web-based molecular graphics. WebGL is employed to display molecules like proteins and DNA/RNA with a variety of representations.
  • Web application (really cool demos)
  • Jupyter Widget Tutorial
• PyMOL(https://pymol.org/edu/?q=educational): PyMOL is a user-sponsored molecular visualization system on an open-source foundation, maintained and distributed by Schrödinger.
  • Practical PyMOL for Beginners
  • Video Tutorials: Video 1 Video2 (and tons more… just search “PyMOL tutorial” in youtube).
  • Cheat Sheet
  • Advanced Cheat Sheet
• Chimera: A highly extensible program for interactive visualization and analysis of molecular structures and related data, including density maps, supramolecular assemblies, sequence alignments, docking results, trajectories, and conformational ensembles.
  • Chimera Tutorials
  • Video Tutorials: Video 1 Video 2 (and tons more… just search “Chimera tutorial” in youtube).
• VMD: A molecular visualization program for displaying, animating, and analyzing large biomolecular systems using 3-D graphics and built-in scripting
  • VMD Tutorials
  • Video Tutorials: Video 1 Video 2 (and tons more… you know the drill)
• https://search.foldseek.com/search

Phage Reading

• Identification MS2 lysis protein dependency on DnaJ
• Mutational analysis of the MS2 lysis protein L
• Characterization of the MS2 lysis protein properties
• Phage therapy: From biological mechanisms to future directions
• Phage Therapy: Past, Present and Future
• Generative design of novel bacteriophages with genome language models

References

• Laboratory of Molecular Architecture
• Shuguang Zhang Brief Bio-Laboratory of Molecular Architecture

Ref: https://www.youtube.com/watch?v=hL6ClTZDUNI#action=share https://www.youtube.com/watch?v=F7Cn52NR_TY

Week 5 HW: Protein Design II

Week 6 HW: Genetic Circuits Part I: Assembly Technologies

Week 7 HW: Genetic Circuits Part II: Neuromorphic Circuits

Week 8 No Homework: Spring Break

Week 9 HW: Cell-Free Systems

Week 10 HW: Advanced Imaging & Measurement Technology

Week 11 — Bioproduction & Cloud Labs

Week 12 HW: Building Genomes

Week 13 HW: Biodesign & Engineered Living Materials

Week 14 HW: Bio Design & Bio Fabrication

Week 1 Lab: Pipetting

Group Final Project

Individual Final Project

A. Proposal Ideas

Idea 1: Kinetic Bio-Interface

Sources of Inspiration: MPU-6050 IMU Sensor + Electrobacteria (Shewanella/Geobacter) + Pattern Recognition

Problem/Vision: It is easy to translate human movement into digital systems, but translating it into a “living/biological” system is very difficult. Can we design a device that bridges human kinetics and microbial metabolism?

Mechanism: A wearable MPU-6050 accelerometer sensor (IMU) reads the user’s hand or dance movements (gesture recognition). Machine learning algorithms analyze these movement patterns (pattern recognition) and convert them into small electrical signals. These signals are transmitted to electro-bacteria such as Shewanella oneidensis or Geobacter living on a microfluidic chip. The electro-bacteria take in this electron flow from the electrodes (microbial electrosynthesis), alter their metabolism, and produce a movement-specific response (e.g., a color change or fluorescent emission).

Automation (Opentrons): The robot is used to distribute different bacterial concentrations and conductive liquid media onto 96-well plates to test for the ideal “kinetic biosolution” where the electrical signal is transmitted most effectively.

Simulation

👉 Simulation

Strengths (+)

Interdisciplinary depth! Wearable sensor technology, machine learning, microfluidics, and synthetic biology all come together in a single project. Shewanella and Geobacter are organisms with well-characterized electron transfer mechanisms—in other words, we’re not inventing things from scratch; we’re building upon existing biology. Integrating with Opentrons also moves the project from a “conceptual demo” to “systematic optimization,” which is highly valuable.

Challenges / Risks (-)

The signal-bacteria interface is the most critical bottleneck. Electro-bacteria operate at microampere-level currents, and aligning signals derived from IMU data with this threshold requires significant calibration. Real-time response is also challenging: bacterial metabolism responds on the order of minutes, not seconds, so the “instant dance → luminescence” scenario will actually require a delay buffer. Microfluidic chip design and fabrication is a project in itself.

Decision on development feasibility: It can be developed, but with a narrowed scope. Perhaps simply getting the “single gesture → single bacterial response” loop to work for the final demo would be a very powerful demonstration. Attempting to run the full pipeline carries the risk of not being able to demonstrate any single layer in depth.

Idea 2: Paleo-Proteins (Neurological Protection Through Ice Age Resilience)

Waving to my Archeology background which I couldn’t complete 🫣 (Double Major)

Sources of Inspiration: Revival of plants from the Ice Age + Synthetic Antibody/Protein Design Using LLM + The Link Between Alzheimer’s and Dementia and Proteins

Problem/Vision: At the root of Alzheimer’s and dementia lies the accumulation of misfolded proteins in the brain. Ancient organisms that have remained dormant since the Ice Age and can revive possess remarkable chaperone (protective) protein mechanisms that prevent their proteins from freezing and breaking down.

Mechanism: Using Large Language Models (LLMs) and AI-based protein design tools (ProteinMPNN, ESMFold), we extract “language” from the protein structures of organisms that survived the Ice Age. With this AI model, we generate “Ancient Resilient Synthetic Chaperones” (Paleo-Proteins) that have the potential to prevent misfolding in Alzheimer’s plaques.

Automation (Opentrons): Dozens of different synthetic protein variants generated by AI are produced in cell-free systems. The Opentrons robot automatically screens these proteins by mixing them with target (amyloid/tau-like) proteins to determine which variant best prevents aggregation/folding (High-Throughput Screening).

Simulation

👉 Simulation

Strengths (+)

Narrative power! The statement “Ice Age organisms could treat Alzheimer’s” is both scientifically defensible and a compelling bio-narrative. My background in archaeology combined with biology perfectly aligns with the HTGAA spirit: I am the prime example of the archetype of someone who comes from a different discipline and views biotechnology differently.

It’s technically sound as well: chaperone proteins from permafrost organisms are well-characterized; ProteinMPNN and ESMFold are production-ready tools; cell-free protein expression with Opentrons is highly suitable for automation; and the ThT fluorescence assay is a standard method for measuring amyloid. Every layer is grounded in existing technologies.

Risks (-)

The biggest risk is the massive translation gap between synthetic chaperones preventing amyloid aggregation in vitro and neuroprotection in vivo; but this is a final project, not a clinical trial, so an in vitro demonstration is more than sufficient. Protein folding quality must also be controlled in cell-free expression; a misfolded chaperone would be ironic.

Paleo-Proteins has a much clearer “problem → solution” pathway compared to my other suggestions. The Kinetic Bio-Interface is creative, but the “what’s the point” question remains unclear; here, the target is Alzheimer’s, the mechanism of action is clear, and the measurement method is standard. (Every project should also consider the sponsor’s perspective, and this idea is in a position to attract more support.)

Idea 3: Bio-Swarm (Ant-Algorithm Micro-Surgical Droplets)

Sources of Inspiration: Surgeon ants + Microfluidics + Pattern Recognition

Problem/Vision: In nature, certain ant species recognize the infection scent/chemical (pattern) in the wounds of injured colony members and secrete specific antibiotics or perform bio-surgery on that area. Current drugs spread throughout the entire body. Can we build an ant-like system that delivers the drug only to the “problematic area”?

Mechanism: Using microfluidic technology, we produce liposomes (extracellular artificial cells) embedded with synthetic genetic circuits. These artificial droplets are designed to open only when they recognize a specific disease/infection chemical pattern (just like surgeon ants) and secrete their healing (surgical/antimicrobial) protein locally.

Automation (Opentrons): Opentrons drips target “infection chemicals” onto a petri dish or microfluidic chip in a maze-like pattern. It then sets up an automated experimental apparatus to visualize how accurately the synthetic surgical droplets detect and burst (respond to) these targets.

Simulation

👉 Simulation

Strengths (+)

Narratively, it’s very striking! The “surgeon ants” metaphor is memorable, and drawing inspiration from nature (bio-inspired design) is an approach HTGAA really loves. Liposome-based targeted drug delivery is already an active area of research, but combining it with synthetic genetic circuits offers a fresh perspective. The maze test on a microfluidic chip has the potential to be a visually striking demo compatible with Opentrons.

Risks (-)

This is the most technically challenging project idea. It requires integrating three separate systems, each complex in its own right: synthetic genetic circuit design (which is a project in itself), liposome fabrication, and microfluidic navigation behavior on top of these. The “ant algorithm” part is a nice metaphor, but in reality, liposomes performing active chemotaxis is a very different engineering challenge from passive diffusion — to achieve this, receptors must be placed on the liposome surface, which requires a deep understanding of protein engineering.

Additionally, the “trigger mechanism that bursts and releases the drug” (e.g., pH-sensitive or enzyme-sensitive liposomes) and “specific chemical pattern recognition” are both highly complex when considered together. Instead of demonstrating all of these in the final demo, it would be better to focus on just one aspect — such as the “opening of liposomes sensitive to infection markers” — and present that in depth.

Idea 4: Cryo-Lume

Spherified Biosensors for Aesthetic Contaminant Detection via Halochromic and Cryogenic Phase-Change

Background

1. Current environmental biosensors predominantly rely on simple colorimetric or fluorescent readouts to detect contaminants. Cryo-Lume proposes a paradigm shift in bio-art and bioremediation by introducing a dual-output biosensor that couples bioluminescence with a macroscopic physical phase change—specifically, localized ice nucleation in a supercooled fluid.

2. The biological chassis will be encapsulated within calcium alginate spheres. While sodium alginate is naturally highly hydrophilic, calcium alginate can be engineered to be hydrophobic for structural and bioremediation applications. The physical behavior of these hydrophobic spheres when introduced into an aquatic environment is critical; their specific water-entry dynamics and fluid displacement determine how effectively they interface with the sample fluid (Watson et al., 2025).

3. Upon entering the contaminated water, the engineered bacteria inside the hydrophobic spheres will detect specific pollutants (e.g., heavy metals/arsenic) and simultaneously activate two distinct pathways: the luxCDABE operon for autonomous bioluminescence, and the inaK or inaZ gene for ice nucleation. This causes the surrounding supercooled water to rapidly freeze around the sphere, forming a glowing, precipitating “snowflake” that visually and physically captures the contaminant.

Aim

1. Design and Characterize the Dual-Output Genetic Circuit. Construct an AND-gate logic circuit combining a contaminant-responsive promoter (e.g., ArsR) with the lux operon (for glowing) and the inaK protein (for ice nucleation).

2. Optimize the Hydrophobic Spherification Process. Produce calcium alginate capsules containing the engineered cells, modifying the polymer to achieve the specific hydrophobic water-entry dynamics and fluid interaction parameters outlined by Watson et al. (2025).

3. Automate Biosensor Screening and Calibration. Utilize the Opentrons liquid handling robot to automate reaction setups in 96-well plates. This high-throughput screening will allow us to test various alginate concentrations and characterize the signal-to-noise ratio, mapping exact contaminant levels to their corresponding cryogenic/luminescent response times.

Simulation

👉 Simulation

Strengths (+)

The dual-output design is genuinely novel. Existing biosensors produce a single readout (color, fluorescence, or electrochemical signal) that requires instrumentation to read. Cryo-Lume produces two simultaneous outputs: a molecular signal (bioluminescence, quantitative via luminometry) and a macroscopic physical phase change (ice nucleation, visible to the naked eye). This means the biosensor works both in a lab with a plate reader AND in a remote village with no equipment — you literally see it glow and feel the ice form. Both biological components — luxCDABE and inaK — are among the most well-characterized systems in synthetic biology. The lux operon has been used in biosensors since the 1990s with published dose-response curves. The inaK ice nucleation protein from Pseudomonas syringae has been studied for decades in atmospheric science and food technology. Neither requires speculative biology — this is engineering with proven parts. The real-world problem is enormous and urgent: arsenic contamination affects over 200 million people globally, predominantly in communities with the least access to analytical infrastructure. A low-cost, equipment-free biosensor directly addresses environmental justice.

Challenges / Risks (-)

Maintaining supercooled water at -3°C requires careful temperature control, though the ~8°C gap between inaK-induced and spontaneous nucleation provides a comfortable margin. The hydrophobic alginate coating could slow arsenic diffusion if too thick, but this is precisely what the Opentrons optimization matrix is designed to calibrate. Cell viability in spheres degrades over time — fine for a final demo with fresh preparations, but long-term deployment would need freeze-drying. All risks have clear mitigations and none are fundamental blockers.

B. FINAL PROPOSAL

Paleo-Proteins

Synthetic Cryoprotectants for Therapeutic Hypothermia and Tissue Preservation

During therapeutic hypothermia in surgeries and organ transplants, extreme cold damages human cells. Ancient plants that survived the Ice Age possess unique cryoprotective protein mechanisms.

This project uses AI-driven protein design to create synthetic cryoprotectants inspired by LEA proteins, dehydrins, and antifreeze proteins from Ice Age survivors; screening them via automated cold assays to identify candidates that protect human cells during medical cooling.

Aim 1: AI-Driven Protein Design

• Scaffold: Ancestral Sequence Reconstruction (ASR) utilizing genomic data from 30,000-year-old Siberian permafrost plants (Yashina et al., 2012) as the core methodology to generate scientifically grounded ‘paleo’ scaffolds (LEA/dehydrins), replacing the reliance on generic PDB structures.
• Design: RFdiffusion or ESM-IF for surface residue optimization; ESMFold (Lin et al., 2023) and IUPred3 for evolutionary-scale structural and flexibility validation.
• Filter: Tm prediction, hydrophilicity score, intrinsic disorder ratio + CamSol/Protein-Sol → stringent computational filtering to prioritize top 5-10 candidates for synthesis.

Aim 2: Cellular Expression & Validation

• Host: E. coli BL21(DE3) with T7 promoter for rapid first-pass expression of the top 5-10 paleo-candidates.
• Tag&Purify: N-terminal His₆-tag; Ni-NTA affinity purification
• Verify:SDS-PAGE + Western blot confirmation of soluble expression.
• Scale-up: If PTMs are needed, transition to Pichia pastoris or mammalian cells.

Aim.3: Automated High-Throughput Screening

• Platform: Opentrons OT-2 liquid handler; 96-well plate format using human cell lines (HEK293T or SH-SY5Y)
• Protocol: (a) Therapeutic hypothermia: 37°C → 33°C → 28°C. (b) Cryopreservation: 4°C / 0°C / −20°C for 4, 12, 24h incubation windows
• Readout: MTT viability assay; dose-response curves (0.1–100 μM) to calculate EC50; hit = ≥30% viability increase vs. untreated control
• Controls: (+) trehalose/glycerol, Type III AFP (RD3, PDB: 1HG7) as biological benchmark; (−) empty vector/GFP

We are not just looking at modern cold-hardy plants. The ‘Paleo-Proteins’ pipeline targets the genomic signatures of 30,000-year-old plant tissues buried in Siberian permafrost (Yashina et al., 2012). Although initially misidentified by morphology, recent molecular phylogenetic analyses have confirmed that these ancient survivors do indeed belong to the Silene linnaeana group (Kramina et al., 2021). This firm confidence in molecular truth rather than pure morphology is also the reason for using Ancestral Sequence Reconstruction (ASR) combined with evolutionary scale models such as ESMFold in Goal 1 (Lin et al., 2023).

Simulation

👉 Simulation

• According to simulation data, DHN-K2S (EC₅₀: 8.2 µM) is the most promising candidate—it passes all tests.

• In-silico phase will be completed in-course; the lab expression (Aim 2) and screening (Aim 3) phases are considered post-course due to gene synthesis costs and time constraints.

• Situated at the intersection of synthetic biology and quantum thermodynamics, Paleo-Proteins reimagines cellular cryopreservation, drawing inspiration from the physics of ultracold matter. Just as a single ‘impurity’ or ‘polaron’ can dynamically alter the energy, size, and motion of an ultracold Bose-Einstein condensate, AI-designed synthetic chaperones act as protective ‘impurities’ within the frozen cytoplasm, thermodynamically disrupt ice crystallization, and preserve cellular integrity (Simons, 2022). To validate these synthetic cryoprotectants, we will apply the meticulous calibration, control, and precise measurement practices inherent to quantum physics to Opentrons’ automated high-throughput screening, enabling the extraction of accurate signals from these noisy biological cell viability data. Ultimately, this project—which integrates AI-driven protein design, medical science, and the dynamics of ultra-cold fluids—aims to engineer unprecedented biological resilience during therapeutic hypothermia and push the boundaries of human lifespan through tissue preservation (Simons, 2025).

Organizations of Interest

Twist Biosciences: Whole plasmid synthesis for all Paleo-Protein expression constructs Ginkgo Bioworks: Primary lab automation (Echo525, PHERAstar FSX, Cytomat) and CFPS master mix Asimov Kernel: DNA construct design, circuit simulation, and construct registry Opentrons: OT-2 automated liquid handling for cell seeding and MTT assay workflow Thermo Fisher Scientific: Cell culture reagents, cDNA synthesis kit, BCA assay, labware Millipore Sigma: MTT reagent, IPTG, anti-His₆ antibody, trehalose positive control New England Biolabs: BL21(DE3) competent cells for recombinant protein expression SecureDNA: Biosecurity screening of all synthetic DNA orders

Future: DeepCure, Takeda Pharmaceuticals

References

Bikard, D., Euler, C. W., Jiang, W., Nussenzweig, P. M., Goldberg, G. W., Duportet, X., Fischetti, V. A. and Marraffini, L. A. (2014) ‘Exploiting CRISPR-Cas nucleases to produce sequence-specific antimicrobials’, Nature Biotechnology, 32, pp. 1146–1150.

Carbon Minds (n.d.) DNA as a Testament to Humanity: Transmuting social inequality indices and human rights principles into genetic code and storing them in Bacillus subtilis spores for millennia via cryptobiosis. Guillermo Romero Tecua.

Citorik, R. J., Mimee, M. and Lu, T. K. (2014) ‘Sequence-specific antimicrobials using efficiently delivered RNA-guided nucleases’, Nature Biotechnology, 32, pp. 1141–1145.

Dauparas, J. et al. (2022) ‘Robust deep learning–based protein sequence design using ProteinMPNN’, Science, 378(6615), pp. 49–56. DOI: 10.1126/science.add2187

Eriksson, S.K. et al. (2011). Phosphorylation of the dehydrin Lti29 in Arabidopsis thaliana is associated with cold acclimation. Plant, Cell & Environment, 34(7), 1071–1082. https://doi.org/10.1111/j.1365-3040.2011.02304.x

Frank, E. T., Kesner, L., Liberti, J. et al. (2023) ‘Targeted treatment of injured nestmates with antimicrobial compounds in an ant society’, Nature Communications, 14, 8446. Available at: https://doi.org/10.1038/s41467-023-43885-w

Jewett, M.C. and Swartz, J.R. (2004) ‘Mimicking the Escherichia coli cytoplasmic environment activates long‐lived and efficient cell‐free protein synthesis,’ Biotechnology and Bioengineering, 86(1), pp. 19–26. https://doi.org/10.1002/bit.20026.

Kramina, T.E. et al. (2021) ‘Molecular taxonomic identification of a Silene plant regenerated from Late Pleistocene fruit material,’ Wulfenia, (28), pp. 29–50. https://www.zobodat.at/pdf/Wulfenia_28_0029-0050.pdf

Lin, Z. et al. (2023) ‘Evolutionary-scale prediction of atomic-level protein structure with a language model,’ Science, 379(6637), pp. 1123–1130. https://doi.org/10.1126/science.ade2574.

Meighen, E.A. (1991) ‘Molecular biology of bacterial bioluminescence,’ Microbiological Reviews, 55(1), pp. 123–142. https://doi.org/10.1128/mr.55.1.123-142.1991.

Min, J., Kim, E. J., LaRossa, R. A. and Gu, M. B. (2000) ‘Detection of environmental effects using recombinant bioluminescent Escherichia coli strains’, Radiation and Environmental Biophysics, 39(1), pp. 41–45.

New understanding of why supercooled water droplets sometimes explode when they freeze’ (2023) Chemistry World. Available at: https://www.chemistryworld.com/news/new-understanding-of-why-supercooled-water-droplets-sometimes-explode-when-they-freeze/4017904.article (Accessed: 3 April 2026).

Oxford University Cryogenic Fluid Dynamics Lab (n.d.) Research on multiphase cryogenic processes, liquid hydrogen, and cryogenic carbon capture. Available at: http://eng.ox.ac.uk/cryogenic-fluid-dynamics-lab

Sarkar, K., Bonnerjee, D. and Bagh, S. (2021) ‘Distributed computing with engineered bacteria and its application in solving chemically generated 2×2 maze problems’, ACS Synthetic Biology, 10(10), pp. 2456–2464. Available at: https://pubs.acs.org/doi/10.1021/acssynbio.1c00279

Schaefer, V. J. (1946) ‘The production of ice crystals in a cloud of supercooled water droplets’, Science, 104(2707), pp. 457–459. Available at: https://doi.org/10.1126/science.104.2707.457

‘Scientists Discover Water That’s Frozen and Liquid at Once’ (2025) ScienceBlog.com. Available at: https://scienceblog.com/scientists-discover-water-thats-frozen-and-liquid-at-once/ (Accessed: 3 April 2026).

Shi, L. et al. (2016) ‘Extracellular electron transfer mechanisms between microorganisms and minerals,’ Nature Reviews Microbiology, 14(10), pp. 651–662. https://doi.org/10.1038/nrmicro.2016.93.

Simons, L. (2025). Bose polarons in superfluids and supersolids [Doctoral dissertation, University of Antwerp] https://repository.uantwerpen.be/docman/irua/d8fd0bmotoM35

Simons, L. (2021). Analog physics with excitations in Bose-Einstein Condensates [Master’s thesis, Universiteit Antwerpen] https://medialibrary.uantwerpen.be/files/7606/704c6cf3-5f9c-4a15-989f-98fed6ffd3b2.pdf

Soprunyuk, V. and Schranz, W. (2023) ‘Glass and freezing transition of supercooled water confined in mesoporous materials and biological systems,’ International Online Medical Council [Preprint]. https://doi.org/10.35248/2322-3308-12.4.001.

Tunnacliffe, A. and Wise, M.J. (2007) The continuing conundrum of the LEA proteins, Naturwissenschaften. journal-article, pp. 791–812. https://doi.org/10.1007/s00114-007-0254-y.

Watson, D.A. et al. (2025) ‘Water entry dynamics of hydrophobic spheres bounded by semi-infinite cylindrical pipes,’ Physics of Fluids, 37(11). https://doi.org/10.1063/5.0300541.

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Wogan, T. (2024) ‘Experimental proof of liquid–liquid transition in supercooled water’, Chemistry World. Available at: https://www.chemistryworld.com/news/experimental-proof-of-liquid-liquid-transition-in-supercooled-water/4012820.article

Wu, J. and Rosen, B.P. (1993) ‘Metalloregulated expression of the ars operon.,’ Journal of Biological Chemistry, 268(1), pp. 52–58. https://doi.org/10.1016/s0021-9258(18)54113-2.

Yashina, S., Gubin, S., Maksimovich, S., Yashina, A., Gakhova, E. and Gilichinsky, D. (2012) ‘Regeneration of whole fertile plants from 30,000-y-old fruit tissue buried in Siberian permafrost’, Proceedings of the National Academy of Sciences, 109(10), pp. 4008–4013. Available at: https://doi.org/10.1073/pnas.1118386109

• I used Claude for all technical infrastructure and simulation testing and generated the visuals using NanoBanana 2.

• I would like to thank our Node Chair Prof. Han, for his valuable contributions and feedback which helped me refine the final project idea.

C. HTGAA 2026: Individual Final Project Documentation

SECTION 1: ABSTRACT

1. Abstract

Therapeutic hypothermia and cryopreservation are critical procedures in medical surgeries, organ transplants, and cellular therapies. However, extreme cold temperatures inherently cause cellular damage, crystallization, and tissue degradation. At the root of this challenge is the need for effective cryoprotectants that can thermodynamically disrupt ice crystallization and preserve cellular integrity. To address this, the “Paleo-Proteins” project draws inspiration from evolutionary biology—specifically, ancient plants that survived the Ice Age, such as the Silene linnaeana group found in 30,000-year-old Siberian permafrost. These ancient organisms possess unique, highly resilient chaperone protein mechanisms.

The broad objective of this project is to harness AI-driven protein design to develop synthetic cryoprotectants inspired by Late Pleistocene Late Embryogenesis Abundant (LEA) proteins and dehydrins. We hypothesize that computationally generated “paleo” scaffolds, optimized via modern AI models, will act as protective ‘impurities’ within the frozen cytoplasm and exhibit superior cryoprotective properties compared to generic proteins. The specific aims involve utilizing Ancestral Sequence Reconstruction (ASR) to generate scaffolds, optimizing them with RFdiffusion and ESM-IF, and filtering candidates using ESMFold and IUPred3.

Ultimately, this project aims to express these candidates and validate their efficacy via automated high-throughput cold assays, establishing a novel pipeline for engineering unprecedented biological resilience during therapeutic cooling.

SECTION 2: PROJECT AIMS

AIM.1: Experimental Aim The first aim of my final project is to computationally design a library of ‘Ancient Resilient Synthetic Chaperones’ (Paleo-Proteins) by utilizing Ancestral Sequence Reconstruction (ASR) and AI-based protein design tools. Relevant methods and resources:

• Ancestral Sequence Reconstruction (ASR): Mining and reconstructing genomic data from 30,000-year-old Ice Age permafrost survivors (Silene linnaeana).

• AI Protein Design Tools: Utilizing RFdiffusion or ESM-IF for de novo surface residue optimization, alongside ESMFold and IUPred3 for atomic-level 3D structural prediction and flexibility validation.

• Computational Filtering: Applying computational models to screen candidates based on melting temperature (Tm) predictions, hydrophilicity scores, and intrinsic disorder ratios (CamSol/Protein-Sol) to prioritize the top 5-10 candidates.

AIM.2: Experimental Aim

• The next step following a successful in-silico design (Aim 1) would be to transition into physical synthesis and automated in-vitro testing. This involves synthesizing the DNA sequences of the top AI-generated protein candidates via Twist Bioscience and expressing them in model cell lines (e.g., E. coli BL21).

• To address the technical challenge of high-throughput testing, an Opentrons OT-2 liquid-handling robot will be deployed to automate the screening workflow. The robot will format 96-well plates, subject the engineered cells to a gradient of sub-body/freezing temperatures (simulating therapeutic hypothermia), and automate MTT cell viability assays to pinpoint which specific variants successfully confer cold resistance.

AIM.3: Visionary Aim

• The long-term vision for this project is to translate these synthetic cryoprotectants into clinical therapeutics, directly addressing a major barrier in the medical field: the inherent cellular damage caused by extreme cold during therapeutic hypothermia.

• If fully realized, these “Paleo-Proteins” would challenge the existing paradigm of tissue preservation by acting as protective ‘impurities’ (conceptually akin to polarons stabilizing ultracold quantum matter) that thermodynamically disrupt ice crystallization inside human cells.

• This would revolutionize clinical practices during complex surgeries, organ transplants, and brain trauma management, safely extending the viability window for human organs and ultimately pushing the boundaries of human longevity.

SECTION 3: BACKGROUND

3a. Background and Literature Context

Therapeutic hypothermia is a critical clinical practice used to preserve organ function and minimize brain trauma during complex surgeries, yet the inherent cellular damage caused by extreme cold remains a major barrier.

Current cryopreservation methods lack targeted, highly efficient biological protectants that operate effectively within human tissue at sub-body temperatures without causing toxicity. To address this gap, this project draws core biological inspiration from Yashina et al. (2012), who successfully regenerated whole fertile plants from 30,000-year-old fruit tissue buried in Siberian permafrost, demonstrating the remarkable, long-term viability of ancient cryoprotective mechanisms.

Complementarily, the physical principles of this project are inspired by Simons (2025), whose research on Bose polarons in ultracold matter illustrates how single “impurities” can dynamically alter the energy, size, and motion of supercooled environments. By integrating these biological and physical insights, we aim to design synthetic chaperones that act as protective ‘impurities’ within freezing cytoplasm to prevent crystallization.

3b. How the project is novel or innovative

Unlike traditional drug discovery that relies on existing modern protein structures, this project utilizes Ancestral Sequence Reconstruction (ASR) combined with evolutionary-scale AI models (like ESMFold) to mine and revive ancient genomic data from Ice Age survivors.

Furthermore, it reconceptualizes biological freezing through the lens of quantum thermodynamics, treating the engineered synthetic chaperones as functional ‘impurities’ (akin to polarons) that thermodynamically disrupt ice crystallization inside the cell. This highly interdisciplinary approach expands the boundaries of synthetic biology by merging evolutionary archaeology, artificial intelligence, and ultracold fluid dynamics to challenge the current biological limits of cellular cold resistance.

3c. Why the project matters and what impact it could have

During complex medical procedures such as organ transplants, major cardiovascular surgeries, or brain trauma management, inducing sub-body temperatures (therapeutic hypothermia) is crucial to temporarily halt cellular decay and increase patient survival . However, a pressing real-world problem is that extreme cold inherently damages human cells, representing a critical barrier to progress in these life-saving surgical practices . This project attempts to solve this paradox by developing therapeutic “Paleo-Proteins” that act as synthetic cryoprotectants to safely prevent cold-induced cellular damage.

If successful, the broader societal contribution would be immense; it could significantly extend the viability window for donor organs—easing the global organ shortage crisis—and reduce irreversible brain damage in trauma patients . Ultimately, this advancement would drastically alter clinical practices in hypothermic medicine, improve the safety of complex surgeries, and push the boundaries of human longevity by enabling an entirely new biological capability: engineered resilience against extreme cold.

3d. Ethical implications associated with project and relevant ethical principles The development of “Paleo-Proteins” as synthetic cryoprotectants for therapeutic hypothermia holds profound implications for public health, directly aligning with the ethical principle of beneficence. By extending the viability window for donor organs and minimizing cellular damage during complex brain or cardiovascular surgeries, this research addresses a critical medical need and aims to save lives. However, because this technology fundamentally pushes the boundaries of tissue preservation and human longevity, it introduces severe implications regarding justice. As seen in emerging longevity research, such advancements raise crucial questions about equity, consent, and accessibility. If these enhanced cryoprotectants are successfully developed, there is a risk they could be restricted exclusively to high-resource medical facilities or wealthy individuals, thereby widening the global healthcare inequality gap. Furthermore, the principle of non-maleficence must be prioritized; engineering extreme cold resistance into biological expression systems (such as the E. coli used for production) presents a potential biosafety hazard if these resilient traits were to escape into the environment and disrupt local ecosystems.

To ensure the ethical execution of this project, I propose implementing strict biocontainment actions—such as engineered genetic kill-switches or synthetic amino acid auxotrophy—in our microbial chassis, alongside transparent, equitable licensing models to guarantee broad public health access to the final therapeutic. A potential unintended consequence of successfully creating highly resilient “super-cryoprotectants” is that they might inadvertently encourage unsafe, speculative procedures (such as unregulated extreme body cooling or commercial longevity biohacking) outside of approved clinical settings. Furthermore, we must acknowledge crucial scientific uncertainties: we could be wrong in our assumption that AI-designed ancient protein analogs will be safe and non-immunogenic in vivo. Medical AI models can overfit, and introducing foreign protein structures into the human bloodstream might trigger severe, unforeseen immune responses. Given these risks, a viable alternative to our proposed action of administering synthetic proteins directly to patients would be to use our in vitro automated screening platform solely to discover non-biological, small-molecule drugs that mimic the thermodynamic properties of Paleo-Proteins. This alternative would offer a cheaper, less immunogenic, and more universally accessible solution for global public health.

SECTION 4: EXPERIMENTAL DESIGN, TECHNIQUES, TOOLS, AND TECHNOLOGY

• Claude | Final Project Experimental Design

Please use this directing link to see my final project proposal which is created via Claude Code based on a skill developed by Ronan Donovan.

1. Detailed Experimental Plan & Timeline

Sub-aim 1: In-Silico AI Protein Design and DNA Assembly (Weeks 1-3)

┌─────────────────────────────────────────────────────────────────────────┐
│                    SUB-AIM 1: IN-SILICO DESIGN PIPELINE                 │
└─────────────────────────────────────────────────────────────────────────┘

  [1] ANCESTRAL DATA              [2] AI BACKBONE DESIGN
  ─────────────────               ──────────────────────
  Silene spp. permafrost    ──►   RFdiffusion
  genomic data                    de novo backbone
  (Yashina et al. 2012)           generation
  Basecamp Research DB            (~500 candidate
  LEA / dehydrin sequences        backbones)
         │                               │
         ▼                               ▼
  [3] SEQUENCE DESIGN             [4] STRUCTURAL VALIDATION
  ───────────────────             ─────────────────────────
  ESM-IF inverse folding   ──►   ESMFold structure
  ESM-2 embeddings                prediction
  PepMLM scaffold seeding         → pLDDT score ≥ 70
  (~200 sequences)                → RMSD vs. known
         │                          dehydrins < 3.5 Å
         ▼                               │
  [5] DISORDER & PROPERTY               ▼
      FILTERING                  [6] CANDIDATE SCORING
  ────────────────────           ──────────────────────
  IUPred3: disorder ratio        Composite score:
  ≥ 0.6 (intrinsically           • Tm prediction
  disordered required)           • Grand avg hydro-
  Anchor2: binding                 philicity (GRAVY)
  site prediction                • Disorder ratio
  GRAVY ≤ −0.5                  • pLDDT
         │                       • K-segment count
         └──────────┬────────────┘
                    ▼
           [7] TOP 5–10 CANDIDATES
           ─────────────────────────
           DHN-K2S (primary)
           DHN-K1, DHN-K2S-ΔS
           + 2–7 additional variants
                    │
                    ▼
           [8] DNA CONSTRUCT DESIGN
           ─────────────────────────
           Benchling (sequence design)
           Asimov Kernel (circuit
           verification & registry)
           pET-28a + His₆-tag + T7
                    │
                    ▼
           [9] TWIST ORDER + DELIVERY
           ─────────────────────────
           Whole Plasmid Synthesis
           SecureDNA screening
           → sequence-verified plasmids
             delivered in 7–10 days

2. Reminder: All HTGAA projects must include some DNA design! Make sure this form is submitted.

• Please use this directing link to see my submitted form.

3. Techniques relevant to the project

• Foundational Lab Practices
  • Pipetting
  • Lab Safety
  • Bioethical Considerations (must check this box)
• DNA Skills & Analysis
  • DNA Gel Art
  • DNA Sequencing
  • DNA Editing (e.g., CRISPR)
  • DNA Construct Design
  • Restriction Enzyme Digestion
  • Gel Electrophoresis
  • DNA Purification from Gel
  • Databases (e.g., GenBank, NCBI, Ensembl, UCSC Genome Browser)
• Laboratory Automation
  • Opentrons
    • Creating Code for Laboratory Automation
    • Using Liquid Handling Robots (e.g., Opentrons)
• Protein Design
  • Protein Design
    1. Models and Notebooks
    2. Databases
    3. Tools
• BioProduction - [x] Chassis Selection (e.g., DH5α, Bl21-DE3 for expression)
  • Registry of Standard Biological Parts
  • FreeGenes
  • Plasmid Preparation
  • Bacterial Culturing
  • Quality Control / Analysis
  • Bacterial Processing (e.g., Centrifugation, Lysis, DNA Purification)
• Cell-Free System
  • Cell-Free Reactions
  • Freeze-Dried Cell-Free Systems
  • NEB Express Kits
  • miniPCR Tools
• Gibson Assembly
  • Primer Design or Selection
  • PCR Reactions
  • Gibson Assembly
  • Other Cloning Methods (e.g., Restriction Enzyme Digestion or Gateway Cloning)
  • Creating Twist Order

4. Two expanded techniques by describing how I would utilize those techniques in final project.

Technique 1 - AI Protein Design: AI-driven protein design is the foundational technology of this project — without it, generating a library of cryoprotective candidates from ancient genomic data would be impossible within any realistic experimental timeline. In this project, I use a three-stage computational pipeline: RFdiffusion generates diverse protein backbone geometries conditioned on the canonical dehydrin K-segment amphipathic helix motif (EKKGIMDKIKEKLPG), exploring structural space that no natural evolutionary trajectory has visited; ESM-IF1 then performs inverse folding on each backbone, outputting amino acid sequences predicted to fold into those geometries while simultaneously satisfying evolutionary plausibility constraints learned from 250 million natural protein sequences; and ESMFold validates each candidate by predicting its full 3D structure from sequence alone, with per-residue pLDDT confidence scores used to confirm that K-segment regions adopt the expected α-helical geometry while spacer regions remain genuinely disordered — a property that is not a flaw but a functional requirement for intrinsically disordered cryoprotectants. The key biological insight driving this design strategy is that dehydrins protect membranes during freezing through an entropic chain mechanism: their disordered regions form a hydration shell that slows ice nucleation and maintains membrane fluidity at sub-zero temperatures, while their K-segment helices anchor to lipid bilayers and prevent phase separation — properties I am computationally amplifying by selecting for high disorder ratio (IUPred3 ≥ 0.60 in spacers), strong amphipathic K-segment helicity, and negative GRAVY scores (≤ −0.5) that ensure sufficient hydrophilicity for intracellular water interaction. This AI-first approach compresses what would traditionally be a years-long directed evolution campaign into a weeks-long computational screen, with the top 5 candidates emerging from a funnel of ~500 RFdiffusion backbones → ~200 ESM-IF sequences → ~40 IUPred3/ESMFold-validated candidates → 5 synthesis-ready constructs ordered as whole plasmids from Twist Bioscience.

Technique 2 - CFPS: Cell-free protein synthesis (CFPS) is a transformative technique that liberates protein expression from the constraints of living cells — instead of growing bacteria overnight, transforming them, inducing expression, and waiting days for results, CFPS allows any sequence-verified DNA to be transcribed and translated directly in a cell lysate within 4 hours, making it ideal for rapid prototyping of novel protein designs. In this project, I use E. coli BL21(DE3) lysate combined with the Ginkgo Bioworks CFPS master mix to perform a same-day proof-of-concept expression test for each Paleo-Protein candidate immediately upon receipt of Twist-synthesized plasmids — this means I can confirm that my AI-designed sequences are actually expressible before committing to the 3-week whole-cell expression and Ni-NTA purification campaign, dramatically de-risking the experimental timeline. The open-reaction format of CFPS is particularly valuable for intrinsically disordered proteins like my DHN-K2S candidates: because the reaction lacks cellular compartmentalization, the expressed protein immediately enters a buffered aqueous environment where it can be sampled, quantified by BCA assay, run on SDS-PAGE for size confirmation, and — critically — applied directly to hypothermia-stressed HEK293T cells for a functional MTT viability readout without any intermediate purification step. This crude CFPS-to-cell screening approach, with a go/no-go threshold of ≥15% viability improvement over the untreated hypothermic control, provides actionable functional data within a single lab session and establishes a direct mechanistic link between AI-designed sequence → expressed protein → measurable cryoprotection, validating the core hypothesis of the project before any large-scale synthesis investment is made.

5.Associated Industry Council companies

Twist Biosciences: Whole plasmid synthesis for all Paleo-Protein expression constructs Ginkgo Bioworks: Primary lab automation (Echo525, PHERAstar FSX, Cytomat) and CFPS master mix Asimov Kernel: DNA construct design, circuit simulation, and construct registry Opentrons: OT-2 automated liquid handling for cell seeding and MTT assay workflow Thermo Fisher Scientific: Cell culture reagents, cDNA synthesis kit, BCA assay, labware Millipore Sigma: MTT reagent, IPTG, anti-His₆ antibody, trehalose positive control New England Biolabs: BL21(DE3) competent cells for recombinant protein expression SecureDNA: Biosecurity screening of all synthetic DNA orders

Future: DeepCure, Takeda Pharmaceuticals

SECTION 5: PROJECT VALIDATION

1.Validation Choice

The primary validation experiment is cell-free protein synthesis (CFPS) followed by direct MTT functional screening, serving as a rapid proof-of-concept for protein activity before committing to the full multi-week E. coli expression and purification campaign. CFPS using BL21(DE3) lysate with Ginkgo Bioworks master mix enables expression of His₆-DHN-K2S directly from circular plasmid DNA within 4 hours, producing a partially purified crude protein fraction that can be applied directly to hypothermia-stressed HEK293T cells for a preliminary functional readout within a single lab session after DNA receipt.

2.Validation Protocol

1. Resuspend Twist-delivered pET-28a-His₆-DHN-K2S plasmid at 50 ng/μL in nuclease-free water.
2. Assemble CFPS reaction on ice: 33 μL BL21(DE3) cell-free lysate + 12 μL Ginkgo Bioworks CFPS master mix + 1 μg plasmid DNA + nuclease-free water to 50 μL total.
3. Transfer reaction to a 1.5 mL microcentrifuge tube. Incubate at 30°C for 4 hours in Inheco Plate Incubator.
4. Centrifuge at 12,000 × g for 5 min (HiG Centrifuge) to pellet aggregates; retain supernatant.
5. Run 2 μL supernatant on 12% SDS-PAGE alongside a His₆ protein ladder. Stain with Coomassie Blue. Confirm band at ~11.2 kDa.
6. Western blot: transfer to PVDF membrane, probe with anti-His₆-HRP antibody, develop with ECL. Confirm identity of band.
7. Quantify protein concentration in CFPS supernatant by BCA assay (Thermo Fisher Pierce BCA Kit).
8. Dilute CFPS supernatant into HEK293T cell culture medium to achieve estimated 1, 10, and 100 μg/mL concentrations (crude, not purified). Include a matched volume of empty-vector CFPS supernatant as vehicle control.
9. Treat pre-seeded 96-well plates of HEK293T cells (seeded by Opentrons OT-2, overnight, 5×10⁴ cells/well) with prepared dilutions.
10. Seal plates with A4s breathable seal (Plateloc). Transfer to 28°C hypothermic condition for 12 hours.
11. Bring plates to room temperature (15 min). Add MTT reagent (Opentrons OT-2). Incubate at 37°C for 4 hours (Inheco).
12. Add DMSO, shake 5 min (BioshakeD3000). Read at 570/670 nm (PHERAstar FSX).
13. Calculate % viability normalized to within-plate 37°C negative control. Go/no-go threshold: ≥15% viability improvement over untreated 28°C control in crude CFPS product. If met → proceed to preparative purification (Steps 6–8 of full protocol).

3.Techniques Used The CFPS validation protocol integrates cell-free protein synthesis as a rapid prototyping technology that decouples gene expression from bacterial cell growth and viability constraints, enabling expression of any sequence-verified plasmid directly in an open-reaction format within hours of DNA receipt. SDS-PAGE provides gel-based confirmation of protein production and approximate molecular weight in less than 2 hours, serving as a low-cost, high-confidence first-pass quality check before committing to any downstream purification or cellular assays. Western blotting with an anti-His₆ HRP antibody provides orthogonal immunological identity confirmation, distinguishing the specific target protein from background CFPS components based on epitope recognition rather than size alone — critical for disordered proteins like dehydrins that may comigrate with CFPS background bands. The MTT cell viability assay, applied directly to crude CFPS-derived protein without full purification, provides functional activity data within the same week as Twist DNA delivery, dramatically compressing the design-build-test-learn cycle and generating actionable go/no-go data before investing in 3-week preparative expression campaigns.

4.Hypothetical Data

Simulated dose-response data — DHN-K2S MTT viability assay at 28°C, 12-hour hypothermia (HEK293T cells):

Cell Viability (% of 37°C untreated control)

100 |                                      ●  ●
 95 |                               ●
 88 |                        ●
 80 |
 75 |                  ●                        ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆
 67 |  ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■
 65 |                  ●
 55 |  ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲
      0.1   0.3    1     3    10    30    50   100  (μM)

● DHN-K2S (predicted EC₅₀ = 8.2 μM; max viability = 98% at 100 μM)
◆ Trehalose 100 mM (fixed concentration, viability = 72%)
■ Untreated cells at 28°C (negative control, viability = 65%)
▲ GFP / empty vector at 28°C (vehicle control, viability = 55%)

Interpretation: DHN-K2S achieves the ≥30% viability improvement threshold at 50 μM, with the dose-response curve consistent with a predicted EC₅₀ of ~8.2 μM (as projected by in silico modeling). At 10 μM (approximately EC₅₀), a 23% improvement is already observed — substantially exceeding both chemical (trehalose: +6.8%) and biological (AFP-RD3: +4.1%) positive controls. These simulated values establish the quantitative benchmarks for experimental validation.

5.Troubleshooting

The primary anticipated challenge is low soluble expression of intrinsically disordered Paleo-Proteins in E. coli, as disordered proteins are prone to partitioning into inclusion bodies; this will be addressed by inducing at reduced temperature (18°C overnight), titrating IPTG concentration down to 0.1 mM, and switching to solubility-enhancing N-terminal fusion tags (SUMO, MBP) if needed — with SUMO cleavage by Ulp1 protease restoring the native N-terminus post-purification. A second concern is non-specific cytotoxicity at high protein concentrations (>50 μM), which could confound viability data and generate false-negative dose-response curves; this will be controlled by running matched-concentration vehicle-only wells (purification buffer diluted equivalently into cell medium) and monitoring cell morphology by brightfield microscopy at each timepoint alongside MTT readings. Inter-plate variability across the hypothermia timecourse is mitigated by including a within-plate 37°C normothermic control column on every assay plate for independent normalization, and by calibrating Opentrons OT-2 pipette tips before each run to maintain dispensing accuracy within ±2%. If formazan signal is confounded by protein pigmentation or aggregation at high concentrations, an alternative resazurin-based metabolic viability assay (CellTiter-Blue, Promega) will be substituted as an orthogonal readout, which is also compatible with the PHERAstar FSX fluorescence detection module.

SECTION 6: ADDITIONAL INFORMATION

1. References

• Yashina, S., Gubin, S., Maksimovich, S., et al. (2012). Regeneration of whole fertile plants from 30,000-y-old fruit tissue buried in Siberian permafrost. Proceedings of the National Academy of Sciences, 109(10), 4008–4013. https://doi.org/10.1073/pnas.1118386109
• Lin, Z., Akin, H., Rao, R., et al. (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, 379(6637), 1123–1130. https://doi.org/10.1126/science.ade2574
• Watson, J.L., Juergens, D., Bennett, N.R., et al. (2023). De novo design of protein structure and function with RFdiffusion. Nature, 620, 1089–1100. https://doi.org/10.1038/s41586-023-06415-8
• Hsu, C., Verkuil, R., Liu, J., et al. (2022). Learning inverse folding from millions of predicted structures. ICML 2022. https://doi.org/10.1101/2022.04.10.487779
• Kramina, T.E., Kochkin, I.T., Tatanov, I.V., & Samigullin, T.H. (2021). Towards molecular identification and phylogenetic placement of Silene (Caryophyllaceae). PhytoKeys, 173, 1–26. https://doi.org/10.3897/phytokeys.173.57402
• Jewett, M.C., & Swartz, J.R. (2004). Mimicking the Escherichia coli cytoplasmic environment activates long-lived and efficient cell-free protein synthesis. Biotechnology and Bioengineering, 86(1), 19–26. https://doi.org/10.1002/bit.20026
• Tunnacliffe, A., & Wise, M.J. (2007). The continuing conundrum of the LEA proteins. Naturwissenschaften, 94(10), 791–812. https://doi.org/10.1007/s00114-007-0254-y
• Dure, L., Crouch, M., Harada, J., et al. (1989). Common amino acid sequence domains among the LEA proteins of higher plants. Plant Molecular Biology, 12(5), 475–486. https://doi.org/10.1007/BF00036962
• Souza Filho, P.J.A., et al. (2016). Dehydrins: structure and functional role in plant stress tolerance. Plant Cell & Environment, 39(9), 1943–1953. https://doi.org/10.1111/pce.12740
• DeVries, A.L. (1971). Glycoproteins as biological antifreeze agents in Antarctic fishes. Science, 172(3988), 1152–1155. https://doi.org/10.1126/science.172.3988.1152
• Koag, M.C., & Lee, S. (2003). The binding of maize DHN1 to lipid vesicles: gain of structure and lipid specificity. Plant Cell, 15(5), 1061–1073. https://doi.org/10.1105/tpc.010793
• Doyle, S.M., & Wickner, S. (2009). Hsp104 and ClpB: protein disaggregating machines. Trends in Biochemical Sciences, 34(1), 40–48. https://doi.org/10.1016/j.tibs.2008.09.010

2. Supply list and budget

DNA Synthesis & Cloning

• Twist Bioscience Whole Plasmid Synthesis × 8 constructs (pET-28a-His₆ backbone, 5 candidates + 3 controls) — Twist Bioscience
• E. coli BL21(DE3) High-Efficiency Competent Cells (NEB C2527H) — New England Biolabs
• LB broth powder and LB agar — Millipore Sigma
• Kanamycin sulfate (50 mg/mL stock) — Millipore Sigma
• SecureDNA sequence screening (×8 constructs) — SecureDNA (free academic access)

Protein Expression & Purification

• IPTG (isopropyl β-D-1-thiogalactopyranoside, 1 g) — Millipore Sigma
• Ni-NTA Agarose resin (5 mL packed column) — Qiagen
• Protease inhibitor cocktail tablets (cOmplete, EDTA-free) — Millipore Sigma
• PD-10 desalting columns (×10) — Cytiva / Millipore Sigma
• Pierce BCA Protein Assay Kit — Thermo Fisher Scientific

Protein Validation (SDS-PAGE + Western Blot)

• Mini-PROTEAN TGX 12% precast gels (10-pack) — Bio-Rad
• Coomassie Brilliant Blue R-250 staining solution — Bio-Rad
• PVDF transfer membranes — Bio-Rad
• Anti-His₆-HRP antibody (200 μL) — Millipore Sigma
• ECL Western blot detection reagent — Thermo Fisher Scientific
• Precision Plus Protein Dual Color Standards (ladder) — Bio-Rad

Cell Culture

• HEK293T cells (ATCC CRL-3216) — ATCC
• DMEM + GlutaMAX media (500 mL × 2) — Thermo Fisher Scientific
• Fetal Bovine Serum, heat-inactivated (500 mL) — Thermo Fisher Scientific
• Penicillin-Streptomycin solution (100×) — Thermo Fisher Scientific
• MycoAlert Mycoplasma Detection Kit — Lonza
• T-75 cell culture flasks (×10) — Thermo Fisher Scientific

High-Throughput Screening (MTT Assay)

• MTT reagent (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, 1 g) — Millipore Sigma
• 96-well flat-bottom cell culture plates (×20) — Thermo Fisher Scientific
• DMSO (cell culture grade, 100 mL) — Millipore Sigma
• Trehalose dihydrate (positive control cryoprotectant) — Millipore Sigma
• Opentrons OT-2 filtered tips, 96-well racks (×10) — Opentrons

Cell-Free Protein Synthesis (Validation)

• BL21(DE3) cell-free lysate + CFPS master mix — Ginkgo Bioworks (course infrastructure)
• Nuclease-free water (500 mL) — Thermo Fisher Scientific

Gene Expression Profiling (qPCR)

• RNeasy Mini Kit (50 rxns) — Qiagen
• SuperScript IV First-Strand Synthesis Kit — Thermo Fisher Scientific
• PowerUp SYBR Green Master Mix (500 rxns) — Thermo Fisher Scientific
• qPCR primers for CIRBP, RBM3, CASP3, BCL2, GAPDH (custom synthesis) — Millipore Sigma
• 384-well PCR plates (Eppendorf, ×5) — Thermo Fisher Scientific

Equipment (Course Infrastructure / Ginkgo Bioworks)

• Opentrons OT-2 liquid handler — Opentrons (course access)
• Echo525 acoustic liquid handler — Ginkgo Bioworks
• PHERAstar FSX plate reader (absorbance 570/670 nm) — Ginkgo Bioworks
• Spark Plate Reader — Ginkgo Bioworks
• CFX Opus qPCR system — Ginkgo Bioworks
• HiG Centrifuge — Ginkgo Bioworks
• Inheco Plate Incubator — Ginkgo Bioworks
• Cytomat shaking incubator — Ginkgo Bioworks
• BioshakeD3000 plate shaker — Ginkgo Bioworks
• Plateloc plate sealer + A4s breathable seals — Ginkgo Bioworks

Itemized Budget

Ginkgo Bioworks automation access (Echo525, PHERAstar FSX, Multiflo, Cytomat) provided through course infrastructure.

DNA Construct — GenBank Format

Primary Construct: pET-28a-His₆-DHN-K2S

This construct encodes a synthetic K2S-type dehydrin (2 K-segments + 1 S-segment) inspired by ancestral Silene LEA protein sequences, designed by RFdiffusion/ESM-IF and codon-optimized for E. coli BL21(DE3). The full plasmid (insert + pET-28a backbone) is ordered as Twist Bioscience Whole Plasmid Synthesis.

LOCUS       pET28a_His6_DHN_K2S      315 bp    DNA     linear   SYN 07-APR-2026
DEFINITION  Synthetic expression insert: N-terminal His6-tagged K2S-type
            dehydrin paleo-protein (DHN-K2S); designed by RFdiffusion and
            ESM-IF from ancestral Silene LEA sequences; codon-optimized for
            E. coli BL21(DE3); cloned into pET-28a between NdeI and XhoI sites;
            ordered as whole-plasmid synthesis from Twist Bioscience.
ACCESSION   .
VERSION     .
KEYWORDS    LEA protein; dehydrin; K2S; cryoprotectant; synthetic biology;
            ancestral sequence reconstruction; paleo-protein.
SOURCE      Synthetic construct
  ORGANISM  Synthetic construct
            other sequences; artificial sequences.
FEATURES             Location/Qualifiers
     CDS             1..315
                     /label="His6-DHN-K2S"
                     /codon_start=1
                     /transl_table=11
                     /product="His6-tagged K2S-type dehydrin paleo-protein"
                     /note="Codon-optimized for E. coli expression (CAI > 0.85);
                      AI-designed scaffold; ancestral Silene LEA inspiration"
                     /translation="MHHHHHHGSDEYGMPAQAAQTGKSSEKKGIMDKIKEKLPG
                                   DKTPEQMAQLKKELPEGSSSSSSSSAEQTGGQQEKKGIMDK
                                   IKEKLPGAQAAQTGKSS"
     misc_feature    1..21
                     /label="His6-tag"
                     /note="6x histidine purification tag; Ni-NTA affinity"
     misc_feature    22..39
                     /label="GS linker + Y-segment"
                     /note="Gly-Ser flexible linker; DEYGMP Y-segment motif"
     misc_feature    40..84
                     /label="K-segment 1"
                     /note="EKKGIMDKIKEKLPG - canonical dehydrin K-segment;
                      amphipathic helix in dehydrated state"
     misc_feature    85..135
                     /label="spacer region"
                     /note="DKTPEQMAQLKKELPEGG - connecting spacer"
     misc_feature    136..159
                     /label="S-segment"
                     /note="SSSSSSSS - phosphorylatable serine cluster;
                      binds Ca2+ and mediates nuclear targeting"
     misc_feature    160..183
                     /label="phi-segment"
                     /note="AEQTGGQQ - phi-segment conserved in K2S dehydrins"
     misc_feature    184..228
                     /label="K-segment 2"
                     /note="EKKGIMDKIKEKLPG - second canonical K-segment"
     misc_feature    229..315
                     /label="C-terminal region + stop"
ORIGIN
        1 atgcaccacc accaccacca cggcagcgat gaatatggca tgccggcgca ggcggcgcag
       61 accggcaaaa gcagcgaaaa aaaaggcatc atggataaaa tcaaagaaaa actgccgggc
      121 gataaaaccc cggaacagat ggcgcagctg aaaaaagaac tgccggaagg cagcagcagc
      181 agcagcagca gcagcgcgga acagaccggc ggccagcagg aaaaaaaagg catcatggat
      241 aaaatcaaag aaaaactgcc gggcgcgcag gcggcgcaga ccggcaaaag cagctaa
//

Twist Bioscience Insert Sequences

Submit the sequences below to Twist Bioscience using the Whole Plasmid Synthesis product. Select pET-28a as backbone. Specify NdeI / XhoI cloning sites. Choose kanamycin resistance.

Construct 1 — His₆-DHN-K2S (Primary Candidate, K2S-type dehydrin)

ATGCACCACCACCACCACCACGGCAGCGATGAATATGGCATGCCGGCGCAGGCGGCGCAG
ACCGGCAAAAGCAGCGAAAAAAAAGGCATCATGGATAAAATCAAAGAAAAACTGCCGGGC
GATAAAACCCCGGAACAGATGGCGCAGCTGAAAAAAGAACTGCCGGAAGGCAGCAGCAGC
AGCAGCAGCAGCAGCGCGGAACAGACCGGCGGCCAGCAGGAAAAAAAAGGCATCATGGAT
AAAATCAAAGAAAAACTGCCGGGCGCGCAGGCGGCGCAGACCGGCAAAAGCAGCTAA

Insert length: 315 bp | Protein MW: ~11.4 kDa | pI: 4.9 | Host: E. coli BL21(DE3)

Construct 2 — His₆-DHN-K1 (Minimal Single K-Segment Control)

ATGCACCACCACCACCACCACGGCAGCGATGAATATGGCATGCCGGCGCAGGCGGCGCAG
ACCGGCAAAAGCAGCGAAAAAAAAGGCATCATGGATAAAATCAAAGAAAAACTGCCGGGC
GCGCAGGCGGCGCAGACCGGCAAAAGCAGCTAA

Insert length: 165 bp | Protein MW: ~6.1 kDa | Used as minimal K-segment structural control

Construct 3 — His₆-DHN-K2S-ΔS (S-Segment Deletion Mutant, Mechanistic Control)

ATGCACCACCACCACCACCACGGCAGCGATGAATATGGCATGCCGGCGCAGGCGGCGCAG
ACCGGCAAAAGCAGCGAAAAAAAAGGCATCATGGATAAAATCAAAGAAAAACTGCCGGGC
GATAAAACCCCGGAACAGATGGCGCAGCTGAAAAAAGAACTGCCGGAAGCGGAACAGACC
GGCGGCCAGCAGGAAAAAAAAGGCATCATGGATAAAATCAAAGAAAAACTGCCGGGCGCG
CAGGCGGCGCAGACCGGCAAAAGCAGCTAA

Insert length: 285 bp | S-segment (SSSSSSSS) replaced by Ala-Gly linker | Used to assess S-segment contribution to cryoprotection

Work in Progress

The current document will be updated via this link; I will continue to edit the document here until the deadline:

(View Full Screen)

TWIST ORDER (FINAL)

(Benchling (from clonal to whole plasmid again and again)))

After long journey from clonal gene to whole plasmid synthesis discussions; finally I decided to order 3 whole plasmid synthesis. Since it is way more expensive to order than clonal gene; I have a plan with three options:**

The Drive folder also contains Benchling exports for both options:

Plan A (preferred): Order all three constructs as Whole Plasmid Synthesis from Twist Bioscience on the pET-28a(+) backbone (NdeI/XhoI cloning sites) — pET28a-His6-DHN-K2S, pET28a-His6-DHN-K1, and pET28a-His6-DHN-K2S-ΔS. This is the fastest and most reliable route since the constructs arrive at Gingko ready-to-transform.

Plan B (if Plan A is not feasible due to cost or timeline): Order only the three inserts (DHN-K2S, DHN-K1, DHN-K2S-ΔS) as Clonal Genes / Gene Fragments with NdeI and XhoI flanking sites (also in the Drive folder). In this case, NdeI/XhoI restriction digestion and ligation into pET-28a(+) would be performed at Ginkgo as an additional cloning step before expression.

Plan C (minimum-viable option): Order only the primary construct (pET28a-His6-DHN-K2S) as Whole Plasmid Synthesis to first validate whether the lead candidate shows the expected cryoprotection activity. If K2S performs well, we proceed with K1 and K2S-ΔS controls in a second round.

Note: Plan B saves on synthesis cost but adds ~1–2 weeks at Ginkgo for cloning, screening, and sequence verification. Whole Plasmid Synthesis is often more cost-effective when accounting for hands-on time.

(ORDER 1 — pET28a-His6-DHN-K2S (Primary Construct))

(ORDER 2 — pET28a-His6-DHN-K1 (Minimal Single K-Segment Control))

(ORDER 3 — pET28a-His6-DHN-K2S-ΔS (S-Segment Deletion Mutant))

A note on my experience: This is my first time doing molecular cloning, and I found the in silico design in Benchling challenging (digestion, sticky-end orientation, frame verification). I completed all three constructs, but I’d strongly prefer Plan A (Whole Plasmid Synthesis) to avoid wet-lab cloning steps I don’t yet have experience with. If cost is a concern, Plan C (only K2S first) is also reasonable.

GINKGO BIOWORKS (NEXT STEP)

🧪 What will Ginkgo do once they receive my plasmids? Once my 3 plasmids from Twist arrive at Ginkgo, I have an automated workflow planned that will run within 3 days:

Day 1 — CFPS reaction: Ginkgo will dilute my plasmids to 50 ng/µL and dispense them into a 96-well plate with nanoliter precision using the Echo525 (acoustic liquid handler). Each well will contain a mixture of BL21(DE3) lysate + Ginkgo’s CFPS master mix + 1 µg of my plasmid. This mixture will produce the protein I designed in just 4 hours at 30 °C in an Inheco incubator — using only bacterial extract, without any live bacteria. At the end of the day, aggregates will be separated using a HiG Centrifuge, and I’ll keep the supernatant (containing my crude protein) for the next stage. On the same day, I’ll verify protein production via SDS-PAGE and anti-His6 Western blot, and measure its quantity using a BCA assay.

Day 2 — Cell testing begins: The night before, an Opentrons OT-2 robot will have seeded HEK293T cells into a 96-well plate at a density of 5×10⁴ cells per well. Ginkgo’s robots will then dilute my crude CFPS protein to concentrations of 1, 10, and 100 µg/mL in cell culture medium and add it to the cells. The plate will be sealed with a breathable film using Plateloc and left in the Cytomat at 28 °C for 12 hours under hypothermic stress — essentially telling the cells, “you’re freezing.”

Day 3 — MTT assay and decision: After 12 hours of cold stress, MTT reagent will be added to the plates (live cells convert it to purple formazan, while dead cells do not), followed by a 4-hour incubation at 37 °C, dissolution with DMSO, 5-minute shaking in a BioshakeD3000, and absorbance reading at 570/670 nm on a PHERAstar FSX plate reader. The result will tell me whether my DHN-K2S protein was successfully produced and whether it kept the cells alive under cold stress. If cell viability is ≥15% higher than the control, my design works → I’ll proceed to large-scale production. Otherwise, I’ll revise the design and iterate.

Interactive project mentor by Derek

I also had chance to try Derek’s Interactive project mentor which is developedwith Sonnet (Claude). You can see proposal file developed by discussion we had with Final Project Interview with AI mentor: Proposal Export

If you have any questions please contact me via beyzabatir@gmail.com.