Analog photography has been experiencing a growing revival and with it a growing ecological concern, specially regarding the impacts of its “magical” component — silver halides. Much of the movement of trying to address the environmental impact of analogue film has fallen on individual artists and researchers, by trying to mitigate the consequences of silver. However, despite the efforts of exploring plant-based developers, and darkroom procedures to prevent damaging disposal of silver contaminated solutions, (extremely toxic for the environment affecting primarily microbial life) we are still left with the need to use this toxic metal in lack of any other option for analog camera photography.

Based on chlorophyll’s photosensitive effectiveness, my research is focused on exploring this molecule as an alternative substance to silver. As of now, I have reached the conclusion that there is a potential in this molecule due to the process of degradation that occurs when chlorophyll is exposed to light outside a living cell — it can demetallate into a porphyrin-type structure that is able to chelate iron, therefore, creating a negative image formed by iron complexes. This hypothesis uses chlorophyll as the photosensitizer and iron as the density builder in order to obtain an image. By developing the image with iron and creating porphyrin-iron type complexes it’s possible to confer a permanent image formation — archival quality to be tested.

For this effect I would like to explore the possibility of engenineering bacteria that could produce a modified version of chlorophyll (that could be organized into supramolecular structures) for optimal photographic application, or an adapted version light-harvesting chlorophyll proteins (LHCPs). The use of bacteria for this effect would ensure a renewable efficient way of producing a photographic emulsion at industrial level.

In a more speculative note, there has also been a growing interest in the experimental photography community to use SCOBY membranes as photographic printing support. It would be interesting if the bacteria could be designed to form a chlorophyll layer at the surface of the cellulose membrane in order to grow photographic “paper”.

Observed: Chlorophyll photodegradation, porphyrin demetallation, iron chelation

Speculative: supramolecular organization of chlorophyll, engineered LHCPs for photographic purposes, SCOBY-grown photographic paper

This intersection between biotechnology and the foundation of an artistic medium can incentivize the much-needed discussion around the role of art when confronted with technological advances and the revision of artistic practices. Specially in the context of ecological artistic practices, there is an interesting space to explore the limits of what is considered ethical in order to make the most out of other-than-human interactions and the creation of symbiotic links through biotechnology.

Although this project is primarily focused on a material design/engineering point of view and the possible development of a new photographic process it does pose ethical questions both at practical and conceptual levels. Specially if entertaining the idea of a photographic SCOBY, that could be passed from one enthusiast to another like it happens today with kombucha cultures. Still, even if we just contemplate the possibility of a genetic modification derived chlorophyll film, that would no longer contain living cells, there could be some implications at conceptual levels regarding people that are developing ecological practices. Taking this into account, some governance/policy goals that could make this project come to life in a safe and ethical manner are:

Transparency regarding the modifications

Have open documentation of genetic modifications in the cells used and processes of production of the film, to allow for an informed ethical evaluation by the users.

The same would apply to a SCOBY plus clearly stating what living cells would that culture contain.

Esurance of biosafety

Utilization of bacteria that present low biosafety hazard risk, both for human handling and eventual environmental release.

Create clear protocols of disposal and deactivation of the cultures.

Understand the impact of a modified culture that could be grown and passed from one person to another in an amateur context

Use of a “kill switch” – nutrient without which the SCOBY culture couldn’t survive

Environmental sustainability

Understanding the life cycle of the engineered material and create clear protocols for sustainable use

Design the materials that compose the film to ensure biodegradability, like substituting the gelatine used in current films for algae derived gels and using bioplastic as film base.

Avoid “greenwashing” through a narrative of sustainability without being sure of the extent of the possible impacts

Pedagogy and discussion

Generate open discussions about ethical use of synthetic biology and offer workshops on the use of this technology

Use as teaching tool to contribute to a more distributed knowledge about biotechnology and how it can be used for creation and evolution. The SCOBY could be a great opportunity to demystify synthetic biology.

Preventing misuse or misinterpretation

While trying to democratize the knowledge about synthetic biology the take measures to prevent the public notion of biological = harmless

Preventing unregulated bio-modification

Thinking about these subjects made me understand the difficult role of defining where should be the limit in making synthetic biology more accessible to the public and made me eager to start dialogues around the role of the arts in play in this. I still think it is important to show how humans can relate with other species at different levels and through symbiosis (whatever form it takes) evolve

Action 1(most plausible): Disclosure standard for bio-engineered materials

Actors: Academic and Scientific researchers, Art institutions, Funding institutions

Purpose: Regarding the case of the chlorophyll film which could be produced through GM bacteria, but the final product wouldn’t contain any viable » cells. This, by Portuguese and EU law wouldn’t have the need of any labeling regarding its origin of production. As a result, users and audiences are typically unaware of the biological engineering involved in the production process. Proposal: Introduce a voluntary disclosure standard for this type of product outside the food and feed context that are derived from GMO production processes but are themselves non-living, clarifying their origin.

Design:

• Develop a standardized disclosure label or documentation stating: That the material was produced using genetically modified microorganisms under contained conditions The procedure of production: which type of organism was used and in which material it contributed to That the final product contains no living organisms or viable genetic material
• Adoption driven by: Research institutions Art schools, Museums and Galleries Funding agencies — transparency statements

Assumptions:

• It’s expected that transparency of production methods increases trust rather than fear
• Artists, researchers and institutions are willing to adopt ethical commitments
• The public will be receptive to the difference between process-based and product-based genetic modification when explained clearly

Risks of Failure & Sucess: Failure:

• Low adoption due to lack of incentives
• Misinterpretation of disclosure as associated risk Success:
• Voluntary disclosure could become an informal requirement
• May unintentionally reinforce the idea that GMO-derived products are Inherently suspect

Action 2 (still plausible): Offering demonstrations in contained conditions

Actors: Researchers; Selected Laboratories (could be biolabs); Experimental Photography Organizations; Public — artists, enthusiasts

Purpose: Regarding both the chlorophyll film and the SCOBY.

Proposal: Through the reach of international photography organizations arrange in collaboration with biolabs demonstrations of the production of both the film and the live photographic SCOBY in contained conditions.

Design:

• According to biosafety levels of both the needed GMOs and the laboratories it should be possible to realize demonstrations of the chlorophyll film production using modified bacteria and the growing of photographic SCOBY membranes since these wouldn’t leave biosafety areas.
• These demonstrations could include the following: The protocols for extracting the modified chlorophyll from the bacteria and turning it into photographic emulsion Developing and processing chlorophyll film Overview the safe and sustainable disposal of the film and chemicals used The protocols used to grow the photographic SCOBY membrane such as feeding, processing the grown membrane, print an image on it and develop it.
• This would be a great opportunity to be able to understand the opinion of the artistic community regarding the use of synthetic biology. >And if it seems justifiable for this end.

Assumptions:

• By sharing the production protocols of a new analog photography technology artists might be more interested to build upon it and feel more confident about biotechnology
• The interaction with a living GM SCOBY would largely contribute for the demystification of synthetic biology
• The public interested in both traditional analog photography and experimental photography would be available to understand more about a new and ecological way of using film

Risks of Failure & “Success” Failure:

• Lack of adherence due to preconceived ideas about GMOs and ethical collision against ecological practices Success:
• Increase of concerns about GMOs due to the demonstration being restricted to biosafe infrastructures

Action 3 (least plausible): Framework for release and sharing of GM SCOBY

Actors: Portuguese and EU regulatory bodies; Research centers; Community Labs

Purpose: Under Portuguese and EU regulation, the deliberate release of genetically modified organisms into the environment — including sharing living cultures outside contained laboratory conditions — is heavily restricted and prohibited without formal authorization. Informal circulation of living GMOs through artistic or DIY communities is not legally accommodated. Proposal: Establish a formal regulatory framework that would allow, under strict conditions, the deliberate release and downstream sharing of that genetically modified SCOBY, used for artistic or photographic purposes.

Design:

• Develop a dedicated authorization pathway under existing GMO delierate release legislations adapted for non-agricultural, non-food, artistic uses
• Requirements would include: Environmental risk acessment Proof of containent or ecological self-limitation Monitoring and reporting obligations Clear disposal protocols
• Oversighted by the Portuguese regulator (APA), possibly coordinated at the EU level
• Participation could imply institutional support from an university/research center, community biolabs for approval prior to sharing or release

Assumptions:

• The environmental risks of a modified SCOBY can be sufficiently predicted and controlled
• Regulators would be willing to differenciate cultural/artistic uses from agricultural and and commercial uses
• That downstream user would comply with handling, propagation and disposal protocols
• That a legal framework would reduce informal or illegal dissemination

Risks of Failure & “Success” Failure:

• High admnistrative and financial burden could make the framework unusable
• Difficulty in ensuring compliance once organisms start circulating
• Public opposition to deliberate release of GMO undermines feasibility Success:
• Normalising the release of GMO for artistic porpuses could endanger biosafety norms
• Authorized release could be interpreted as being biologically harmless and contribute to reckless use
• Aproval could legitimize risky practices under the context of art

Disclosure standard for bio-engineered materials

This action would receive the most priority since having a disclosure that allows the consumer to make an informed ethical decision about the technology offered is essential and creates an opportunity to broaden the perception of the range of synthetic biology use.

Offering demonstrations in contained conditions

I consider this action the most interesting to accomplish the main two objectives of increasing proximity and dissemination of biotechnology in the arts and incentivize research on the topic of this project. However, it would imply more difficulties due to the need for living GM cultures, that not being authorized to leave biosafe areas, would need to be reproduced in every lab the demonstrations took place.

First of all I started by making a digest with a single enzyme at a time.

Then tried to color code the result of every enzyme and superimpose them on top of each other so to create a “grid” were I would make my design. I soon understood it would be way too confusing, plus, that the result of using a combination of enzymes doesn’t necessarily correspond to the superimposition of the lines created by each enzyme separately.

Then I started using Ronan’s website to iterate on combinations of enzymes + using some unconventional techniques.

The protein I’d like to work with is the prochlorophyte chlorophyll-binding (Pcb) protein which is the light-harvesting protein (LHP) in prokaryotes that uses only chlorophyll as their photosensitive pigment. A modified version of this protein could be used to efficiently absorb light causing degradation of chlorophyll a, b and d molecules into porphyrin-type derivatives — that can be used to bind iron and create photographic images in a cell-free system.

MGMQTYGNPDVEYGWWAGNSRLAGFSGKWLAAHVAQAALIVFWAGAICLFEVARYTADVPLGEQNLILIPHMASLGLGIGEGGQIVDTFPYFAVGVVHLVSSAVIGAGGLYHSLRGPAILKEGPARAPKFDFDWGDGKRLGFILGHHLILLGLGALFLVLWAVFFGIYDPVIGEVRTVTSPTLNPFTIFGYQTHFVETNTLEDLIGGHVYVAIIEISGGLWHIFCPPFKWAQRLIIYSGEGLLAYALGGLAIMGFTAAVYCAFNTLAYPVEFYGPPLDFRFSFAPYFIDTADLPSGQYTARAWLCNVHFFLAFFVLQGHLWHALRTLGFDFKRIPAALGSLSEDVVDAKA

(from NCBI chlorophyll a/b binding protein [Prochloron didemni])

ATGGGCATGCAGACCTATGGCAACCCGGATGTGGAATATGGCTGGTGGGCGGGCAACAGCCGCCTGGCGGGCTTTAGCGGCAAATGGCTGGCGGCGCATGTGGCGCAGGCGGCGCTGATTGTGTTTTGGGCGGGCGCGATTTGCCTGTTTGAAGTGGCGCGCTATACCGCGGATGTGCCGCTGGGCGAACAGAACCTGATTCTGATTCCGCATATGGCGAGCCTGGGCCTGGGCATTGGCGAAGGCGGCCAGATTGTGGATACCTTTCCGTATTTTGCGGTGGGCGTGGTGCATCTGGTGAGCAGCGCGGTGATTGGCGCGGGCGGCCTGTATCATAGCCTGCGCGGCCCGGCGATTCTGAAAGAAGGCCCGGCGCGCGCGCCGAAATTTGATTTTGATTGGGGCGATGGCAAACGCCTGGGCTTTATTCTGGGCCATCATCTGATTCTGCTGGGCCTGGGCGCGCTGTTTCTGGTGCTGTGGGCGGTGTTTTTTGGCATTTATGATCCGGTGATTGGCGAAGTGCGCACCGTGACCAGCCCGACCCTGAACCCGTTTACCATTTTTGGCTATCAGACCCATTTTGTGGAAACCAACACCCTGGAAGATCTGATTGGCGGCCATGTGTATGTGGCGATTATTGAAATTAGCGGCGGCCTGTGGCATATTTTTTGCCCGCCGTTTAAATGGGCGCAGCGCCTGATTATTTATAGCGGCGAAGGCCTGCTGGCGTATGCGCTGGGCGGCCTGGCGATTATGGGCTTTACCGCGGCGGTGTATTGCGCGTTTAACACCCTGGCGTATCCGGTGGAATTTTATGGCCCGCCGCTGGATTTTCGCTTTAGCTTTGCGCCGTATTTTATTGATACCGCGGATCTGCCGAGCGGCCAGTATACCGCGCGCGCGTGGCTGTGCAACGTGCATTTTTTTCTGGCGTTTTTTGTGCTGCAGGGCCATCTGTGGCATGCGCTGCGCACCCTGGGCTTTGATTTTAAACGCATTCCGGCGGCGCTGGGCAGCCTGAGCGAAGATGTGGTGGATGCGAAAGCGTAA

(converted using bioinformatics.org)

Codon optimization is an important process due to different organisms having and producing different amino acids in different proportions. So, if a gene codes for a rare amino acid, it might slow the translation process and therefore the folding of the protein and might even render the protein non-functional. In this case, the gene should be optimized for e. coli which is probably the best choice since the primary objective is to express a protein that is going to be used in a cell-free system and it is the simplest organism to work with. For this codon optimization I avoided Type IIS enzyme recognition sites for BsaI, BsmBI, and BbsI — these are some enzymes that are useful for ligation with plasmid backone.

ATGGGGATGCAAACGTACGGAAATCCTGACGTAGAGTACGGTTGGTGGGCTGGAAATTCAAGATTAGCTGGATTCTCTGGTAAGTGGCTTGCAGCTCACGTAGCACAAGCCGCACTTATAGTTTTCTGGGCAGGTGCAATATGTTTATTCGAGGTCGCCCGTTACACAGCTGACGTCCCTTTAGGTGAGCAAAATCTTATCTTGATCCCACACATGGCTTCCTTAGGTCTTGGTATAGGAGAGGGTGGTCAAATCGTTGACACATTCCCATACTTCGCTGTTGGTGTCGTACACCTTGTTTCCTCGGCCGTCATCGGGGCAGGTGGTTTGTACCACTCTTTACGAGGTCCCGCCATATTAAAGGAAGGACCCGCACGTGCTCCAAAGTTCGACTTCGACTGGGGCGACGGTAAGCGGTTAGGATTCATCTTAGGTCACCACTTGATACTCTTAGGGTTAGGGGCCCTTTTCCTTGTACTTTGGGCAGTCTTCTTCGGTATATACGACCCTGTTATAGGGGAAGTAAGAACGGTTACATCCCCTACATTGAATCCATTCACAATATTCGGTTACCAAACTCACTTCGTAGAGACTAATACGCTTGAGGACTTAATCGGTGGTCACGTTTACGTCGCCATCATCGAGATCTCCGGCGGGTTGTGGCACATCTTCTGTCCCCCATTCAAGTGGGCACAACGATTGATCATATACTCAGGTGAGGGGTTGCTTGCCTACGCATTGGGTGGTCTCGCTATAATGGGTTTCACTGCCGCAGTCTACTGTGCCTTCAATACGCTTGCCTACCCTGTAGAGTTCTACGGTCCACCTTTAGACTTCCGTTTCTCATTCGCACCATACTTCATCGACACAGCCGACTTGCCGTCCGGGCAATACACAGCCCGAGCCTGGTTGTGTAATGTTCACTTCTTCTTAGCTTTCTTCGTATTGCAAGGGCACCTTTGGCACGCATTACGTACGCTTGGTTTCGACTTCAAGCGTATCCCCGCAGCATTAGGTTCCCTCTCTGAGGACGTTGTTGACGCCAAAGCGTAA

(made with Codon Optimization Tool | Twist Bioscience)

This gene sequence could be synthesized through chemical synthesis on silicon chips and assembled into a vector backbone— bacterial plasmid— then, put into e. coli. This allows for the use of the bacteria’s own cellular machinery to first transcribe this DNA sequence into mRNA (which would be identical to this coding DNA strand, except for “U”), and finally the bacteria’s ribosomes would translate the resulting mRNA into the final amino acid sequence. This AA sequence would be the pcbA protein in its apoprotein form— since e. coli lacks the machinery to produce chlorophyll molecules that play as a co-factor in the folding of this protein— which would be later combined with chlorophyll extract to render it functional.

1. Describe how a single gene codes for multiple proteins at the transcriptional level.

In biological systems, a single gene can code for multiple proteins at the transcriptional level through the process of alternative splicing (in eukaryotes)— a process where different combinations of exons from the same pre-mRNA molecule are joined together. This process happens inside the nucleus during the processing of pre-mRNA, leading to the synthesis of multiple protein isoforms, which are related forms of the same protein, but with different structural or functional properties. Another process that allows for a single gene to code for multiple proteins (both in eukaryotes and prokaryotes) is the action of alternative promoter genes that create different initiation sites, affecting which exons are included in transcription.

2. Try aligning the DNA sequence, the transcribed RNA, and also the resulting translated Protein!!!

The DNA I’m interested in sequencing and further understanding is cyanobacteria’s genes for the production of Chlorophyll LHP (Light-Harvesting proteins) which is the nature’s way of organizing chlorophyll molecules in order to get the most light absorption out of them. This biological way of organizing light sensitive pigments could be the answer to a new generation of analog photography media and can also be used to engineer more efficient solar cells to produce energy.

For this type of application, the most adequate sequencing technology would probably be Sanger sequencing using a device like Sanger-ABI, a 1st generation technology that has been around since 1977 but would be more than enough for things like reading single protein coding sequences. This would be a small-scale project needing only to analyze relatively small nucleotide sequences, it wouldn’t demand the comparison of more complex genes like comparing/analyzing whole genomes. For this method I would make a DNA extraction from cyanobacteria cells and purify it, followed by designing primers specific for the sequence I want to analyze and amplify it through PCR, then remove excess primers and dNTPs. Next step would be to perform a cycle sequencing reaction “chain terminator PCR” using single primers, DNA polymerases, dNTPs and fluorescently labeled ddNTPs— these fluorescent ddNTPs act as chain terminators, stopping synthesis randomly at every possible length to create labeled fragments. Clean up residual dye labeled ddNTPs to prevent noise during read and submit these tagged fragments to Sanger-ABI capillary electrophoresis, which separates the fragments by length and then makes the read by exciting the label of each fragment and detecting the color emitted— then using software to translate the fluorescence signals into a chromatogram, revealing the sequence of the DNA sample.

I would need to synthesize a bacterial plasmid (for e. coli) with the insert for the Chlorophyll LHP gene, in this case the PcbA protein. The application for this would be to develop a way to keep these proteins functioning in a cell free system in order to create a novel biomaterial that would serve as photographic emulsion for analog film.

Probably the most affordable and effective way to synthesize this kind of DNA would be the through clonal gene chip-based chemical synthesis and assembly of the DNA sequence into a plasmid vector through Golden Gate Assembly using type IIS restriction enzymes and T4 DNA ligase. Due to the small-scale nature of this project and standard difficulty of synthesizing this kind of DNA, I don’t think there would be significant limitations speed, accuracy and scalability wise.

For the objective of this project the DNA that would be interesting to edit would range from the genes coding for the chlorophyll synthesis pathway— in order to develop a modified version of chlorophyl that would be more optimized for photographic purposes— and the sequences coding for the LHP if there is the need to modify the natural occurring proteins, either by decreasing the protection these proteins confer against chlorophyll degradation or by potentially improving on their ability to maximize chlorophyll’s light absorption qualities.

CRISPR-based genome editing would probably be the best choice for this purpose since Chlorophyll biosynthesis involves multiple genes, often with regulatory fine-tuning rather than simple on/off behavior. CRISPR systems allow gene-specific, locus-specific edits, making them well suited for altering enzyme functionality in the chlorophyll synthesis pathway; modifying regulatory regions that affect pigment ratios (e.g. chlorophyll a, b or d) and engineering specific amino-acid changes in LHPs. CRISPR edits DNA by using an RNA guide to bring a DNA-cutting enzyme (nuclease) to a specific genomic site which is cut and where the cell’s own DNA-repair machinery makes the final change utilizing a DNA template (single stranded or double stranded for larger edits) which is delivered together with the Cas9 enzyme. For this end, the first steps would be to define an objective precisely like make chlorophyll more sensitive to light or more prone to degradation under certain conditions, or reduce photoprotective quenching of the LHP and identify genes, regulatory regions or domains that are relevant for those functions. After that, decide what type of edit strategy is needed for a specific site (gene knockout, single or few nucleotide changes or sequence replacement) and design a guide RNA to bind to the target DNA of that specific site, and if a sequence rewrite is needed, design the DNA template for that repair which must have homologous endings that match those surrounding the cut site. To perform the actual edit the gRNA, Cas9 nuclease and template DNA are combined into a plasmid vector or ribonucleoprotein and introduced into the cells via heat shock, electroporation or lipofection. The limitations of this method might include off-target effects by binding and editing similar but not the exact sites; in a cell culture it might happen that not all cells be edited resulting in a mixture of edited and unedited cells, which can make it difficult to achieve a uniform result, and the HDR might have a low efficiency compared to the non-homologous end joining (NHEJ) pathway, not resulting in the intended edit.

First of all I used opentrons-art.rcdonovan.com to generate a base design with an image from “vecteezy.com” and then modified it manually to reach the final design

Then created the zones in red with the following logic (adapting from Example 5 and 7):

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for i in red:
    if i % 40 == 0:  ## Every 40 drops (not 20 because .5 drops), including at i == 0 for the start
     ## Aspirate the smaller value between pipette_20ul max_volume and how much volume is still needed given that each drop is a .5 drop
        pipette_20ul.aspirate(min(pipette_20ul.max_volume,(red.stop - i) * 0.5),
            location_of_color('Red'))
    dispense_and_detach(pipette_20ul, .5, cursor)
    cursor = cursor.move(types.Point(y=0, x=2.2))
    ## Here I start printing the red part of the cap in a side to side printer style movement — in an attempt to not have to enter every single coordinate and jumping the areas for other colors 
    if i == 13:
      cursor = cursor.move(types.Point(y=-2.2, x=2.2))
    if i > 13:
      cursor = cursor.move(types.Point(y=0, x=-4.4))
    if i  == 25:
      cursor = cursor.move(types.Point(y=0, x=-2.2*3))

Used Chat GPT to write all the coordinates using a “template dictionary” I wrote, and then adapted the logic I used before to work with the dictionary instead of a range:

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dots = list(yellow_stem.values())

  for i in range(len(dots)):
    if i % 40 == 0:
      ## same logic of refill but adapted to the yellow_stem dictionary
        pipette_20ul.aspirate(min(pipette_20ul.max_volume, (len(dots) - i) * 0.5),
            location_of_color('Orange'))
    cursor = center_location.move(dots[i])   
    dispense_and_detach(pipette_20ul, 0.5, cursor)

  pipette_20ul.drop_tip()

Repeated the same process for the green spots, but because I made some mistake in the red dots the coordinates from the green weren’t matching as they should, so I solved that by manually adjusting the red dots to the placement of the green spots

If we assume that 100g of meat has on average ≈ 26g of protein then 500g would have 130g. 100 Daltons per amino acid is ≈ to 100g|mol of amino acids. So if we get how many moles are present in 130g of protein and multiply that by the Avogadro’s number we get the number of molecules of amino acids present.

N = number of molecules

• N = 130g/100g|mol x (6.022x10^23molecules|mol)
• N ≈ 7.83 x 10^23 molecules

When an animal eats any other being the digestion system breaks down and processes the food into particles our cells can use to function. In the case of proteins, when we consume any other being we are repurposing the amino acids that being contains and turning them into the combinations we need to make our own proteins. We don’t start using the same proteins other beings use. Our DNA contains the code to build human proteins that repurpose and rearrange the building blocks that compose what we eat.

I assume the reason there are only 20 natural amino acids is that those molecules were the ones that ended up being easier to produce through metabolic pathways, were the most stable and had enough differences between them to perform all the needed interactions. Other iterations of amino acids that appeared throughout evolution must have stopped been produced because they didn’t fit these parameters one way or another.

Yes, we can synthesize new non-natural amino acids through chemical synthesis or enzyme-based reactions

Given that amino acids are just molecules— combinations of atoms, they can organize spontaneously through chemical reactions under the right conditions, without any type of biological intervention.

A left handedness would be expected, since what can be observed from natural occurring amino acids that build proteins, which are left-handed, is the formation of right-handed alpha helices because this clockwise twisting promotes less clashes between the side chains and the peptide backbone. So if the amino acids were right-handed the opposite would probably happen.

Yes, there are some other types of protein helices, though they are less common than alpha.

There are 3-10 helices, often found at the ends of helices. They are tighter, with 3 residues per turn and (instead of the 3.6 found in alpha), and often observed in membrane proteins acting as an intermediate conformation in the unfolding/folding of alpha helices.

Also, Pi-helices, which are wider with aprox. 4.4 residues per turn, inserted inside alpha-helices, appearing as a bulge. Often found in functionally important regions of proteins, such as active sites or ion-binding sites, providing increased conformational flexibility.

If we assume the context of biology then most helices are either DNA/RNA or proteins, hand both the building blocks of these types of substances confer a final right-handed helical formation. DNA/RNA— the building blocks of these structures have a certain bias, specially the sugars D-deoxyribose/D-ribose which force the backbone to twist to the right in order to minimize strain. And proteins— which are made up of L-amino acids which also have a preferable twisting bias to the right.

Beta-sheets tend to aggregate because their structures leave hydrogen-bonding edges exposed. While helices stabilize themselves by having hydrogen bonds stabilizing the coil from the inside, beta sheets leave C=O from the backbone and N=H groups free, and so they tend to aggregate with other nearby sheets or fold into themselves in a structure known as steric zipper.

The beta-sheet formation is the more energetically favorable conformation for satisfying the backbone hydrogen bonds, so whenever a protein misfolds the probability of it’s backbone folding into a default of beta-sheet formation is high. The problem of beta-sheets is that they self-aggregate and so they can form a cascading effect of by attracting other proteins backbone with the exposed hydrogen bonds, which can stack into amyloid fibrils and can accumulate and disrupt cell and organ function. Beta-sheets form highly ordered and stable molecular structures providing rigidity while allowing for flexibility, resulting in high tensile strength, as observed in natural fibers like silk or collagen. So, amyloid-like fibrils which follow a cross-beta motif generally unbranched and form chains into a continuous, repeating, ribbon-like core are perfect for a strong fiber-like material.

The protein I selected is CP43 (encoded by the psbC gene) which is an essential chlorophyll-binding core antenna protein in Photosystem II (PSII). I chose this protein since I’m doing research into light harvesting proteins in cyanobacteria. I had already done some research into Pcb proteins (chlorophyll binding proteins specific to Prochlorococcus and Prochloron), so now I thought it would be interesting to do some research into a more widely used protein.

MVTLSNTSMVGGRDLPSTGFAWWSGNARLINLSGKLLGAHVAHAGLIVFWAGAMTLFEVAHFIPEKPMYEQGLILLPHIATLGWGVGPAGEVTDIFPFFVVGVLHLISSAVLGLGGIYHALRGPEVLEEYSSFFGYDWKDKNQMTNIIGYHLILLGCGALLLVFKAMFFGGVYDTWAPGGGDVRVITNPTLNPAIIFGYLLKAPFGGEGWIISVNNMEDIIGGHIWIGLICISGGIWHILTKPFGWARRALIWSGEAYLSYSLGALSLMGFIASVFVWFNNTAYPSEFYGPTGMEASQSQAFTFLVRDQRLGANIASAQGPTGLGKYLMRSPSGEIIFGGETMRFWDFRGPWLEPLRGPNGLDLDKLRNDIQPWQVRRAAEYMTHAPLGSLNSVGGVITDVNSFNYVSPRAWLATSHFVLGFFFLVGHLWHAGRARAAAAGFEKGIDRETEPTLFMPDLD

• 460 AA long — The most common AA is G (Glycine)
• Uniprot BLAST came back with 250 results for homologs for this CP43 protein sequence
• CP43 is the canonical membre of CP43-like class of light-harvesting proteins, which is one of the classes in the photosynthetic antenna superfamily

RCSB CP43 page

• First solved in 2020 with 2.58 resolution, version of 2021 is the one with best resolution 1.93
• Yes, there are present the other proteins(1.) that constitute Photosystem II as well as ligands(2.), sugars(3.), ions and water molecules(4.)

• 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?

More helices, Hydrophobic

• Visualize the surface of the protein. Does it have any “holes” (aka binding pockets)?

Yes it has several binding pockets. I turned on the ligands and sugars visualization to try to understand the binding pockets better

When I first ran the code there were way too many proteins in the visualization, then I understood that the fasta file being loaded had around 15 thousand sequences which were being rendered. So I asked Chat GPT (in order to not break the whole code by experimenting) where I could limit the amout of pronteins that were being embeded to a more manageable array.

Then I asked chat gpt how I could insert my protein and it first tried to code it into a token. But it seemed easier and less risky If I could append my sequence to the array of sequences imported so I asked it to do that

So it would be easier to find my protein I asked chat GPT to label it with the name ““My protein”” and color it bright green

I set it to 800 which seemed to have a good amount of variation for me to understand what was happening. After that, I increased the array to 8000 because I didn’t have too many direct neighbours to my protein

There are two primarily noticable patterns. The first one is the Cystine residue which mostly seems like a bad fit throughout the whole sequence, persumably because the chemistry of this amino acid would disrupt the function of this protein in most places— the actual protein sequence only contains 2 Cystines. The second pattern is the vertical blue columns throughout the sequence that indicate parts of the protein sequence which are propably very functionaly critic zones, thus toleratig very little exchange of residues. In these columns there are some interuptions for certain amino acids which seem to be fairly well accepted along the whole sequence which include: G (the most comon AA), and L, F, V and I which are all hidrophobic, since the protein as a whole is mainly hidrophobic this makes sence.

I analysed 2 well identifiable distinct neighborhoods. The one selected in green has several proteins related to neural function, while the one in pink has several calcium-binding proteins that serve different functions.

The closest and most interesting neighbour that I could identify as aproximate in function was a Cryptochrome C-terminal domain which is a flavoprotein blue light-sensing photoreceptors found in plants, animals, and microorganisms that regulate circadian rhythms and developmental processes Most other neighbours seemed to be related by being membrane-related proteins or also being rich in helices like Disulfide bond formation protein, E. coli (membrane protein with transmembrane helices), Cytochrome c peroxidase (predominantly alpha-helical) and Viral nucleoproteins (helix-rich folds).

The predicted structure seems slightly different regarding the helices on the bottom part than the actual protein.

1.First I changed a random base for a G since it is one of the most well accepted residues and the protein seemed to maintain its shape.

2.After that, I changed 4 residues to Cs (the least accepted residue) and nothing seemed to change too much in the conformation.

3.Changed 4 sets of 6 to Cs

4.Changed 1 random set of 20 to Cs

5.Deleted 4 random residues — one of the six helixes partially uncoiled and smaller horizontal helices formed

6.Deleted 10 random residues — seems to have aggravated the latter step but still resembles the original protein

All these changes were cumulative so this protein seems relatively resilient to mutations

The first problem I ran into was that I couldn’t download the file for the 3D structure of CP43 protein alone, as it is part of photosystem II, all the proteins involved were also included in the in the files. I tried several ways through the RCBS website, but with no success. And so, I resorted to Chat GPT that spat out a python script to run on my computer terminal in order to download the correct file, and it worked.

Once I had the right file (checked it by opening it on the RCBS site), with both the backbone and all ligands to that specific protein. I tried to run the inverse folding code on the Collab notebook but with no success and couldn’t find a way to import hat was missing so I tried to use an online tool for inverse folding from neurosnap.ai and it seemed to work just fine!

Then compared some of the predicted sequences to the original one by performing alignments on Benchling to see what residues were used on the predictions and how similar they were to the original. As observed on the heatmap from earlier the C residues were kept at a minimum and the most used residues fluctuated between the best accepted ones like L and G. It was also interesting to observe that between predicted sequences there were similar areas that have the same residues in common as the original, which must be the most crutial for that type of folding. Also, the predicted sequences have -10 residues at the very beggining, you can see it from the benchling screenshot, and which I interpret as a N-terminus or C-terminus that the inverse folding model didn’t replicate since they probably weren’t solved in the 3D structure.

MVTLSNTSMVGGRDLPSTGFAWWSGNARLINLSGKLLGAHVAHAGLIVFWAGAMTLFEVAHFIPEKPMYEQGLILLPHIATLGWGVGPAGEVTDIFPFFVVGVLHLISSAVLGLGGIYHALRGPEVLEEYSSFFGYDWKDKNQMTNIIGYHLILLGCGALLLVFKAMFFGGVYDTWAPGGGDVRVITNPTLNPAIIFGYLLKAPFGGEGWIISVNNMEDIIGGHIWIGLICISGGIWHILTKPFGWARRALIWSGEAYLSYSLGALSLMGFIASVFVWFNNTAYPSEFYGPTGMEASQSQAFTFLVRDQRLGANIASAQGPTGLGKYLMRSPSGEIIFGGETMRFWDFRGPWLEPLRGPNGLDLDKLRNDIQPWQVRRAAEYMTHAPLGSLNSVGGVITDVNSFNYVSPRAWLATSHFVLGFFFLVGHLWHAGRARAAAAGFEKGIDRETEPTLFMPDLD

MGNDYATTGFPAWLALLRLLNASGRLLGLLVALLGVVLLAIGLGTLYEVFNLDPTVPLYKQGKLLLPLIATLGLGVGKNGKITNLLPFLLVGLLFLVLGLLLLALGLYLLLFGPENLEDISDVLGWDWTDLAKVNRIIGILLILLGLLFLGIAYRAMFAGGLYDPWAPGGPDVRVVKNPNLNPKVIFGYFLRPPVKGFYNLVSIDDMAKYVGLQIWLSILFILLGIYHITTTPNAALKAAFTWSLVALLAYLAWLLSILFFYLSLLAALNNTLFPSEFYGPTLAEAAQAKAFVEYVEAKAAGEDIWTAKGADGTGKYLTKSPDGRVVFGGPAVKYWWTRHPWLEPLRGPDGLDPEKLATGVTPEMIERWREMAAHAPLCTADCVGGPPTAPKDVYYCSPRLVISTTSLILGALFAVLAIILSTFAAAAAAGTANGVDPATAPAAFLPPPA

MGTDLATTGFPPWLAFLRLLDATGRLLGLLIAALGAVSLFIGLTTLYEVANLDPTVPLYKQNHLLLPLIATLGLGVGKNGKITDTSPFLAVGLAHLLLGVVLLALGLYFLLFGPERLEDVSPLLGWRATDRRKVNRWLGVLSILLGLLFFGIVYQAMFAGGLYDPWAPGGPDVRVVKNPNLDLKVILGYFLRPPLPGAYNIWSIDDMETYVGLNIWLSILFILLGIYLLLTTPGAALRASLVWSLLALLAYLAALLAVLFFSLSALVAINNTLFPSEFFGPTAAEAAVAAGFVAYVAARAAGVDIWTAKGADGAGTYLTKDPAGRTILGGPAAAFSWTRHPWLEPLRGPDGLDPAKLATGVTPEMVAAAKEAAAHAPTCTPENIGGPPTAPADVKYCSPRKILSTTFLVLGAAFAALAVLLSTAAVLAALGIWRGVDPATAPWRFLPPLA

MGDNYETTGFPPWLGLLRLLDATGRLLGLLLLLLGLVLLAIGAGTLYEVAHLDPTVPLYLQGHLLIPLIATAGLGVGKDGVITNTLPFLLVGVAHLVLGLVVIAIGLYFLLFGPENLEDVSKVLGWKETNKKKVNRILGILFIILGLLFLGFVYQATFAGGWYDPWAPGGPAVRVVTNPNLNLKTILGYLLRPPTRGHENLVGIDDMETIVGLLIWLSIFFILLGIYHIFTTPNELLKKTLIWSLIALLGYLAALLSALFFYLSRLAALNTTLFPPEFYGPTAAEAAVAAAFVAAVEALAAGVDIWTARGPDGRGRYLTRSPDGRVVFGGPAARHWWTRHPWLEPLRGPDGLDPEKLRTGVTPAMVAAARAAAAHAPTCSADCVGGPPGAPRDVRYCSPRLVISTAHAALGAAFAALAAALLTLAKAIAAGTALGVDPATAPALFLPPAS

IGDNYATTGFTDALGFLRLKDATGRLLGFLILLLGLISLFIGLSTIYEVINLDPDVPLYKQGHLLLPLIGTLGFGVGKNGKITNKLPFLIFGIIHLILGIILIALGLYFLLFGPENLENISKFLGFDWKDLKKVNRIIGILSILLGLIFFAIVYIATKAGGWYDPWAPGGPDVRVVTNPNLNPVAIFGYFLLPPVAGYENLVSIDSMELAVGLFIWLSVFFILLGIYHIFTTPNAALKASLVWSFTAYLGYLFALLGVLFFYLSLLAARNNTLFPSEFFGPTAAEAAVAAAFVAAVEDRAAGVDIWTAKGADGEGKYLTKSPDGRVIFGGPAAAHWYTRHWWFEPLRGEDGIDRDKLDNGVTPEQVAAARDAAAHAPTCTADCWGGPPTAPKNVKYCSPRLIISTTSLVLGALFAIAAAILFAYAKAEAAGTANGVDPATAPALFLPAPA

Regarding the 3D structure, the new predicted proteins were similar to the original, however, the main differences rested on the width of the 6 helices barrel which in wider on the original CP43 and the same helices are not as well organized, straight and vertical as the original.

After generating the 4 sequences I resorted to Chat GPT to generate code to help me input the known sequence into the model so that PepMLM would grade its confidence in the known binder as well.

All the pepties I visualized bonded to the barrel region. The one I was able to generate which had the best perplexity score (of 7) and the best ipTM (of 5) bonded slightly closer to the N-Terminal, but still on the barrel region. I included a molecular surface visualization for that one so it could be better analized. It seems to bind at the surface, doesn’t seem to be partially burried.

The known binder got a perplexity score of 20 and a ipTM score of 0.3, so I’m not sure how indicative these scores are being of the actual binding abilities of these peptides

All ipTM values I got were bellow 0.6 and so it would suggest the predictions failed in terms of relative positions between enzyme and binder peptide. But these might be due to the small nature of this structure and chain where the TM score is very strict. However, binders 2, 3 and the extra one with preplexity score of 7 all got better ipTM scores than the known binder, so that would supposedly indicate peptides that exceed the known binder.

First of all, I used Chat GPT to number the residue sequence in the heatmap for me to compare the residues more easily

6 best LLR scored residues in hydrophilic zone (no changes to transmembrane zone)

• 29 C->S (C->R was the best according to LLR score but was negative for lysis on the experimental data)
• 39 Y->L
• 9 S->Q
• 5 F->Q
• 27 Y->R
• 22 F ->R

METRQPQQQQQTPASTNRRRPRKHEDRPSRRQQRSSTLLVLIFLAIFLSKFTNQLLLSLLEAVIRTVTTLQQLLT

Then I visualized it in Alpha fold to see what had changed. While the hydrophilic region in the original L protein had low accuracy in the visualization and wasn’t uniform, this Mutant 1 had better values of accuracy and more uniformity in the hydrophilic tail.

1. Redered interaction with DnaJ
2. Detail where we can see no bonds between DnaJ and L protein Mutant
3. Multimer made up of 8 Mutant L proteins. The aggregation seems close to what would be expected of wild type L Protein

17 best residues in hydrophilic zone (no changes to transmembrane zone) Added to the previous best LLR scored residues the best mutant for other 11 residues always double checking with experimental data sheet

• 39 Y->L
• 9 S->Q
• 5 F->Q
• 27 Y->R
• 22 F ->R
• 17 N->R
• 26 D->R
• 23 K->R
• 2 E -> A
• 6 P -> Q
• 12 T -> Q
• 24 H -> R
• 25 E -> R
• 32 Q -> R
• 33 Q -> R
• 37 T -> L
• 14 A -> S

MATRQQQQQQQQPSSTRRRRPRRRRRRPSRRRRRSSLLLVLIFLAIFLSKFTNQLLLSLLEAVIRTVTTLQQLLT

This version had slightly better scoring in regards to prediction confidence and was slightly more uniform, but no significant changes in visualization

1. Redered interaction with DnaJ
2. Detail where we can see no bonds between DnaJ and L protein Mutant
3. Multimer made up of 8 Mutant L proteins. The aggregation seems close to what would be expected of wild type L Protein

5 best LLR scored residues in transmembrane region always double checking with experimental data sheet (no changes to hydrophylic zone)

• 50 K -> L
• 53 N -> L
• 61 E -> L
• 52 T -> L
• 45 A -> L

METRFPQQSQQTPASTNRRRPFKHEDYPCRRQQRSSTLYVLIFLLIFLSLFLLQLLLSLLLAVIRTVTTLQQLLT

These residues correspond to the areas in the heatmap where mutation seems to be more accepted. As the transmembrane region already has a well defined structure and good prediction confidence in the original protein, no significant changes were noticeable in the visualization.

1. Redered interaction with DnaJ
2. Detail where we can see no bonds between DnaJ and L protein Mutant
3. Multimer made up of 8 Mutant L proteins. The aggregation here changed a lot, having a circular conformation, but with the 8th monomer at the center, and the hydrophylic tails folded inwards at the end

Added the known positive mutations for lysis from experimental data to mutant 3 (no changes to hydrophylic zone)

• 50 K -> L
• 53 N -> L
• 61 E -> L
• 52 T -> L
• 45 A -> P
• 44 L -> P
• 46 I -> F

METRFPQQSQQTPASTNRRRPFKHEDYPCRRQQRSSTLYVLIFPPFFLSLFLLQLLLSLLEAVIRTVTTLQQLLT

For this version I thought that maybe if the transmembrane region has a mix of better residues for Lysis (from experimental sheet) combined with better structure from best scored residues in the less conserved areas it could have an overall benefit for Lysis and anchoring to the membrane. It was interesting to see that most changes to residues in the transmembrane region in the experimental data resulted in negative impact on lysis, and that the residues that came back positive for lysis in experimental data don’t necessarily correspond to the structural scoring of the LLR

1. Redered interaction with DnaJ
2. Detail where we can see 1 bond to DnaJ on the residue 31 of Mutant 4
3. Multimer made up of 8 Mutant L proteins. The aggregation here changed even more, uncoilig some of the transmembrane portions. It was interesting to understand how mutating a protein might not affect it’s individual folding but can affect a lot its interactions with other proteins, and the low tolerance of the transmembrane zone to mutations.

Combined mutant 2 and 4

MATRQQQQQQQQPSSTRRRRPRRRRRRPSRRRRRSSLLLVLIFPPFFLSLFLLQLLLSLLEAVIRTVTTLQQLLT

It was interesting to observe that in the visualization this version formed a small helix segment in the hydrophilic region

1. Redered interaction with DnaJ
2. Detail where we can see no bonds between DnaJ and L protein Mutant
3. Multimer made up of 8 Mutant L proteins. The aggregation seems close to what would be expected of wild type L Protein, although the monomers bend slightly outwards

In order to model a Golden Gate assembly on Benchling I first got a mUAV plasmid backbone from Addgene

Then created a new linear DNA sequence by copying the amilCP gene from the HTGAA Benchling

Both fragments already had a Type IIS enzyme cut site in the right zones for assembly, using BsaI

Created a Golden Gate assembly, using the mUAV as backbone and the amilCP (Purple) as insert

When the assembly was put together, I realized that it had been put together the wrong way— because of the orientation of the BsaI enzymes, the backbone being used was the 867 bp long instead of 2057 bp long

Created a new Golden Gate assembly and was able to output the plasmid in the correct order, although the recognition sites of BsaI enzymes of the backbone stayed inside the final plasmid which wasn’t supposed and would make a real golden gate assembly not work properly

To solve this the solution would be to create primers for the BsaI enzymes to be sure they pointed in the right direction and that their recognition sites would be eliminated with the process

In the context of this exercise I manually edited the recognition sites of the BsaI enzymes in order to invert their cut site direction and was finally able to get a correct assembly

Some of the key components of the Master Mix are: Phusion High-Fidelity DNA Polymerase (1 unit/50 µl) which are thermostable DNA polymerase with proofreading activity to ensure minimal error rate; dNTPs (200 µM of each) — deoxynucleotide building blocks that allow the synthesizing of DNA strands and HF Reaction Buffer — an optimized buffer which ensures good polymerase activity, including MgCl₂ (magnesium ions, 1.5 mM) – essential cofactor for DNA polymerase and other salts and ions to maintain proper enzyme activity and fidelity. Optionally, if the desired segments have high CG content, DMSO (dimethyl sulfoxide) can be used — is often provided separately and can be added to improve amplification of GC-rich templates

The primary factor that influences the annealing temp. is the melting temperature of the primers — temperature at which 50% of the primer-template duplex dissociates— and which, if met correctly, allows for optimal interaction between DNA and primers resulting in correct binding. This Tm (melting temperature) is heavily influenced by the CG to AT content— high CG content, which bind more strongly to each other result in higher TMs—, also, the longer the primers are the higher Tm is.

In addition to these, the concentration of salts in the master mix also influences the temperature to which the reaction can be heated up.

My mental depiction of these two processes is that PCR is used to identify one target sequence and multiplicate it through synthesizing new segments that are used to synthesize even more, with this process you end up with millions or billions of copies of the original target. While restriction enzyme digests are more of an identify and cut out process, through which you extract the desired segment from the remaining DNA, you don’t end up with more copies of the target segment. In terms of protocol:

PCR amplifies a specific DNA region using primers and a DNA polymerase, through temperature cycling between the following steps

• Denaturation (~95°C) Double-stranded DNA separates.
• Annealing (~50–65°C) Primers bind to complementary regions flanking the target DNA.
• Extension (~72°C) DNA polymerase synthesizes new DNA strands. These steps are repeated 25–35 cycles, producing millions of copies of the target fragment.

Restriction Enzyme Digests

Restriction enzymes are endonucleases that cut DNA at specific recognition sequences (usually palindromic). By using DNA, the specific restriction enzyme, an appropriate buffer and incubating (usually 37°C), DNA is cut into fragments based on enzyme’s recognition sites. This results in fragments dependent on restriction sites of the enzymes used (might produce unintended fragments if restriction sites repeat). Generates sticky ends or blunt ends useful for cloning.

In terms of use, PCR is more useful when you need to isolate and amplify a certain gene from a limited DNA source, ensuring that you have enough quantity and purity of the desired segment, small edits can also be added by creating primers that induce a small “error” to the copying. Restriction Enzyme Digests are preferable when the fragment already exists and you want clean, mutation-free cuts with compatible ends for cloning or to analyze DNA fragments (for gel electrophoresis)

You must ensure the ends of the different sequences you want to assemble through Gibson cloning are homologous matches, by using primers that overlap— the primers for the different segments must have an overlapping site of around 20-40 bp that allow for the formation of complementary sticky ends during assembly. Or, in case of restriction digests, the fragments that are cut must already have zones that are homologous, which is harder because in this method you don’t have a way to induce these sites like in PCR.

During transformation the objective is to be able to disrupt the E.coli’s cell wall just enough for the plasmid to be able to enter and then allow them to return to their natural form. This can be done through heat shock or electroporation that induces formation of reversible aqueous pores through the creating of an external electric field causes a transmembrane potential difference.

The Golden Gate Assembly relies on two Type IIS Restriction Enzymes that cut DNA outside their recognition sites, allowing for the use of unique and custom 4bp long sticky overhangs, and therefore, a really precise assembly of multiple small sequences at the same time. Having the constraint of ensure the used sequences are “domesticated”— free of internal Type IIS recognition sites.

The process goes as follows:

Creation of PCR primers that contain the Type IIS enzyme’s recognition sites and the unique 4bp overhangs in the right order for each of the segments.

(Screenshot from Golden Gate Assembly video by New England Biolabs)

Restriction digestion, where the Type IIS enzyme cuts all fragments and the vector, producing the designed overhangs. Because the cut site is outside the recognition sequence, the recognition sequence itself is removed during assembly.

DNA ligase joins fragments that have matching overhangs. Since the restriction sites are removed after ligation, the assembled DNA cannot be cut again, making the reaction efficient.

Cycling digestion and ligation is performed through alternating temperatures for restriction digestion and ligation. This increases assembly efficiency because incorrectly assembled fragments can be cut again until the correct construct forms.

(Screenshot from Golden Gate Assembly video by New England Biolabs)

Traditional genetic circuits based on digital logic are primarily limited by low complexity of operations and needing an expressive amount of metabolic effort from cells. IANNs have the advantage of using analog logic. By leveraging non-linear functions and using continuous input ranges, instead of Boolean circuits that require discrete thresholds, neuromorphic systems allow for more nuanced decision-making, while reducing the metabolic burden since fewer components are needed. Intracellular Neural Networks take advantage of the type of chaotic organization that already happens inside a cell, instead of trying to impose a translation of logic into more readable inputs and outputs like digital logic does, providing access to more complexity, scalability and adaptability to different environments. The main drawback of neuromorphic systems is noise, like any biologic system, but this can be mitigated through the aggregation of information across a population of cells which diminishes the intrinsic noise of cellular environments.

The most interesting application for IANNs that I see is as preventive therapy that can have an intravenous delivery and regulate the immune system in response to really early and small changes in cells, only activating the immunotherapy in the right tissue when inputs meet certain specific criteria. In this type of approach different circuits with different targets can be introduced into the body and each of them have a small numbers of inputs, lowering the complexity needed for each different circuit but creating a system that can analyze and potentially respond to a wide variety of targets. So, with each circuit having two or three biomarker inputs that only trigger a immunotherapy response if specific non-linear patterns are met, a full “body scan” can be performed with really high precision.

Fungal materials range from textiles like leather — which fungi are really good at mimicking since the formation of mycelium creates really strong and flexible networks— to more structural elements like building block for walls— mycelium has the ability to grow in many types of substrates like grain or straw, and while growing it aggregates these fibers or grains into solid chunks that can be later dried. This creates a strong material which can support some considerable charge and isn’t brittle, also having really good insolation qualities both for sound and heat.

Besides the possibilities for materials derived from fungi, another really interesting scope through which I’m interested in fungi is the possibility of therapeutic solutions by engeneering human microbiomes with adaptogenic fungi that could regulate our imune systems through the amazing methabolic pathways they posess, which bacteria don’t.

Cell-free synthesis excells in enabeling open system manipulation by allowing the direct addition of additives; rapid experimentation and high-throughput since the transformation and cultivation steps can be skiped, moving directly from PCR to protein production; and tolerance to unnatural aminoacids via custom tRNA.

In the case of my final project, cell-free synthesis is more benificial since the first exeriments I would like to perform are based in the synthesis and analisys of chlorophyll-binding light-harvesting proteins, which depend on the presence of chlorophyll to fold properly. If cell-free synthesis is used, chlorophyll can be added as an additive to enable correct folding in a single reaction, otherwise, using cultivation of E.coli, the apoprotein form of the LHP would have to be purified and only after react with chlorophyll.

Another case that I see cell-free synthesis being really usefull is in polymer design, where the possibilities are much more vast and interesting if using de novo amino acids. Cell-free synthesis allows to bipass the in vivo systems that possess established translational machinery adapted for natural occuring amino acids and tRNA.

The main components include:

• Genetic template either in linear segments or plasmid form
• Cell Extract (Lysate) which contains necessary transcriptional/translational machinery: ribosomes, aminoacyl-tRNA synthetases, translation factors, and enzymes. This can be either whole cell extract, which also contain metabolic enzymes and other cellular components typically from E.coli or can be a PURE system which only contains the needed purified machinery.
• Buffer which maintains optimal PH for enzymatic activity and reaction stability
• ATP that provides energy; GTP, CTP, and UTP contribute to RNA synthesis.
• Nucleotides for transcription and Amino acids for translation
• Co-folding factors if needed to ensure proper folding of proteins

Energy regeneration is essential in cell-free systems because ATP is rapidly consumed during transcription and translation, and without replenishment the reaction quickly stops. One way to maintain ATP levels is to add creatine kinase and phosphocreatine, which regenerate ATP from ADP and provide a continuous energy supply during the experiment.

Prokaryotic cell-free systems (e.g., E. coli) are fast, cost-effective, and high-yielding, and allow direct control over reaction conditions, but they lack post-translational modifications. Eukaryotic systems are more complex and lower-yielding, but enable proper folding, disulfide bond formation, and post-translational modifications such as glycosylation.

A prokaryotic system can be used to express, e.g., chlorophyll-binding proteins such as CP43 or PcbA, since chlorophyll can be added directly to the reaction as a folding cofactor, promoting correct assembly of these hydrophobic proteins.

In contrast, a eukaryotic system is suitable for expressing, e.g., fungal lectins (glycoproteins), as their biological activity depends on correct folding, disulfide bond formation, and glycosylation. These modifications are essential for carbohydrate-binding function and are only supported in eukaryotic cell-free systems.

Using chlorophyll-binding membrane proteins such as CP43 or PcbA as example. To optimize their expression in a cell-free system, I would screen different reaction conditions while supplying a membrane-mimicking environment, such as detergent micelles, liposomes, or nanodiscs, so the hydrophobic transmembrane regions can insert properly instead of aggregating. Because these proteins also depend on chlorophyll as a folding cofactor, I would add chlorophyll directly into the reaction to promote correct folding and assembly.

The main challenges are poor solubility, aggregation, misfolding, and low functional yield, since membrane proteins are highly hydrophobic. In the case of CP43 or PcbA, another challenge is that without chlorophyll the protein may not fold correctly or remain stable. To address this, I would optimize variables such as temperature, magnesium concentration, reaction time, membrane mimetics, and chlorophyll concentration, and then evaluate expression by SDS-PAGE/Western blot and functionality by measuring pigment binding or spectroscopic properties. This makes cell-free expression especially useful because the folding environment and cofactors can be controlled directly in one reaction.

Low yield can arise from degraded or low-concentration DNA, as well as from poorly designed constructs (e.g., weak promoter, inefficient RBS, incorrect spacing, or unfavorable codon usage). These issues reduce transcription and translation efficiency. This can be addressed by using high-quality DNA and optimizing the construct with a stronger promoter, improved RBS, and codon optimization.

Energy depletion or suboptimal reaction conditions, given that cell-free systems rapidly consume ATP and cofactors, which can limit protein synthesis. This can be improved by optimizing the energy regeneration system and adjusting parameters such as Mg concentration, temperature, and reaction time.

Protein misfolding or aggregation, especially for membrane or complex proteins, improper folding can reduce yield. This can be addressed by lowering the reaction temperature, adding chaperones, or including detergents, liposomes, or nanodiscs.

First of all, to check for the expression of WSCPs, a gel electrophoresis analisys would be conducted, this would be done after purification of the protein using a platform like Revvity LabChip (as used at Ginkgo Bioworks). The preparation steps for the electrophoresis input go as follows:

• Prepare Denaturing Solution: Prepare a mixture of Protein Express Sample Buffer and a reducing agent (if needed, such as BME, DTT, or TCEP).
• Mix Sample with Buffer: Add 2 µL of protein sample to 7 µL of the denaturing solution in a 96-well PCR plate or 0.6 mL centrifuge tube.
• Heat Denature: Seal the plate or tube and heat to 100°C for 5 minutes to denature the proteins.
• Dilute and Mix: After cooling, add 35 µL of molecular biology grade water (or 32 µL for High Sensitivity) to each sample and mix by pipetting up and down.
• Remove Bubbles: Centrifuge the sample plate at 3000 rpm for 5 minutes to eliminate bubbles, which can cause erratic results. The expected output would be 20-22 kDa per subunit. This would allow for a first assessment of correct protein expression.

The next measurement I would take would be a uv–vis absorbance spectrum analisys, comparing the pure chlorophyll vs the chlorophyll-bond WSCPs. In this case I believe absorbance would be enough to determine if there was a correct folding of WSCPs and consequent binding to chlorophylls since the main absorption bands of chlorophyll, including the Soret and Qy bands, will be compared in terms of peak position, intensity, and shape. Binding to WSCP is expected to induce shifts in absorption maxima and produce sharper, more defined peaks due to the organized protein environment.

• To prepare the samples a chlorophyll stock solution would be prepared in a suitable solvent and diluted into buffer to generate the free chlorophyll sample. The purified WSCP–chlorophyll complex would be prepared in the same buffer conditions and adjusted to comparable chlorophyll concentrations.

Third, I would measure the reaction with iron after light exposure in free chlorophyll compared with chlorophyll-bound WSCPs. This is central to the photographic concept, because chlorophyll is expected to degrade under illumination into derivatives capable of chelating iron and forming a darker final product. To test this, I would expose both samples to controlled light conditions for defined times, then add iron salts under standardized conditions and monitor the reaction. This could be achieved in solution and measured by UV–Vis spectroscopy to detect changes in absorbance associated with iron complex formation, or by embedding the two different samples in polymer and performing a visual test on the change of color after reaction with iron.

According to Expasy Compute pI/Mw tool the molecular weight of eGFP with added linker and His-tag is 28006.60 Da

I chose to work with the two peaks circled in green

$$ \begin{aligned} z &= \frac{m/z_{n+1}}{(m/z_n - m/z_{n+1})} \ z &= \frac{848.9758}{875.4421 - 848.9758} \ z &= \frac{848.9758}{26.4663} = 32.08 \end{aligned} $$

Using the 875.4421 peak with $z = 32$:

$$ MW = 32(875.4421 - 1.0073) $$

$$ MW = 32(874.4348) $$

$$ MW \approx 27981.9\ \text{Da} $$

Check with the second peak:

$$ MW = 33(848.9758 - 1.0073) $$

$$ MW = 33(847.9685) $$

$$ MW \approx 27982\ \text{Da} $$

So the protein molecular weight is:

$$ MW \approx 27982\ \text{Da} $$

Using:

$$ \text{Accuracy} = \frac{|MW_{\text{experiment}} - MW_{\text{theory}}|}{MW_{\text{theory}}} $$

Assuming the theoretical MW of eGFP of 28006 Da, then:

$$ \text{Accuracy} = \frac{|27982 - 28006|}{28006} $$

$$ \text{Accuracy} = \frac{24}{28006} = 8.5 \times 10^{-4} $$

$$ \text{Accuracy} \approx 0.00085 $$

No, the charge state cannot be determined for the zoomed-in peak from this figure alone. Determining the charge state requires at least two adjacent peaks, so their spacing can be used to calculate z. In the zoomed region, only a single isolated peak is shown and no neighboring charge-state peak is visible. Therefore, there is not enough information to assign its charge state.

There are 20 Lysines (K) and 6 Arginines (R) highlighted bellow:

MVSKGEEL FTG VVPILVELDG DVNGHKFSVS GEGEGDATYG KLTLKFICTT GKLPVPWPTL VTTLTYGVQC FSRYPDHMKQ HDFFKSAMPE GYVQERTIFF KDDGNYKTRA EVKFEGDTLV NRIELKGIDF KEDGNILGHK LEYNYNSHNV YIMADKQKNG IKVNFKIRHN IEDGSVQLAD HYQQNTPIGD GPVLLPDNHY LSTQSALSKD PNEKRDHMVL LEFVTAAGIT LGMDELYKLE HHHHHH

According to the Expasy PeptideMass tool, 19 peptides will be generated

Divided the highest peak in 10

There seem to be 19, which would match the prediction

If all peaks are taken into account then there would be 22 which is more than the prediction

In mass spectrometry, the instrument measures the mass-to-charge ratio (m/z) of a peptide that carries multiple protons, so to recover the true peptide mass (as the singly charged [M+H]+), we multiply the measured m/z by the charge z to remove the charge scaling and then subtract the mass of the extra proton that was added during ionization.

The most abundant peak is: $$ m/z = 525.76712 $$ Adjacent isotope peaks:

• (526.25918 - 525.76712 = 0.49206)
• (526.76845 - 526.25918 = 0.50927)

Average spacing ≈ 0.50 $$ \Delta(m/z) \approx \frac{1}{z} ;\Rightarrow; z \approx \frac{1}{0.5} = 2 $$ Charge state: ( z = 2 ) $$ [M + H]^+ = z(m/z) - (z - 1)\times 1.0073 $$

$$ [M + H]^+ = 2(525.76712) - 1.0073 $$

$$ [M + H]^+ = 1051.53424 - 1.0073 $$

$$ [M + H]^+ \approx 1050.53 $$

$$ MW_{\text{experiment}} = 1050.52438 $$

The closest predicted peptide mass from PeptideMass is:

$$ MW_{\text{theory}} = 1050.5214 $$ From peptide FEGDTLVNR

Predicted peptide with theoretical ([M + H]^+)

Mass accuracy calculation

$$ \text{Accuracy} = \frac{|MW_{\text{experiment}} - MW_{\text{theory}}|}{MW_{\text{theory}}} $$

$$ \text{Accuracy} = \frac{|1050.52438 - 1050.5214|}{1050.5214} $$

$$ \text{Accuracy} = \frac{0.00298}{1050.5214} = 2.84 \times 10^{-6} $$ Error in ppm

$$ \text{ppm error} = \text{Accuracy} \times 10^6 $$

$$ \text{ppm error} = 2.84 $$

Final answer

• Observed peptide mass: 1050.52438
• Closest predicted peptide mass: 1050.5214
• Mass error: 2.84 ppm which is well bellow the <10ppm threshold

It’s 88%

In a cell-free master mix, the lysate, in this case E. coli, has all the machinery needed to process DNA to RNA to protein. In order for this to work, there is a mix of salts such as Potassium Glutamate, Magnesium Glutamate and Potassium phosphate, which maintain proper ionic concentrations to allow for correct DNA/RNA folding, enzymes to bind, and protein synthesis to occur efficiently, and buffers (HEPES + Potassium phosphate monobasic/dibasic) which maintain the optimal ph conditions for enzymatic activity.

The main differences rest on how they supply energy and nucleotides, as well as in their intended performance (for fast expressions or for expressions that might need more time) The 1-hour PEP–NTP system, uses pre-supplied NTPs (ATP, GTP, CTP, UTP) and the energy comes from PEP (phosphoenolpyruvate)— which is a high-energy molecule that can donate a phosphate group to regenerate ATP—, designed for rapid expression, by including high-energy, ready-to-use components.

While the 20-hour NMP–Ribose–Glucose system uses NMPs (AMP, CMP, UMP) instead of full NTPs. Nucleotides and energy are generated in situ from Ribose and Glucose, by relying on native enzymatic pathways in the extract. It is designed for longer expressions, having fewer synthetic additives— includes nicotinamide— which supports metabolic pathways that convert glucose into usable energy— instead of multiple cofactors. Therefore, being more metabolically integrated and sustainable.

1. sfGFP — Engineered for robust folding, even under non-optimal conditions, making it highly reliable in cell-free systems
2. mRFP1 — Has a relatively slow maturation time, meaning fluorescence appears later, which can delay readout despite successful expression.
3. mKO2 — Exhibits pH sensitivity, so fluorescence intensity can decrease in more acidic environments typical of some cell-free reactions over time
4. mTurquoise2 — Known for high quantum yield and brightness, but requires proper folding, making its performance sensitive to reaction conditions
5. mScarlet_I — Designed for fast maturation and high brightness, allowing rapid and strong fluorescence readout in cell-free systems.
6. Electra2 — Displays oxygen-dependent chromophore formation, so fluorescence requires sufficient oxygen availability, which can be limiting in dense or sealed reactions.

mKO2 — Exhibits pH sensitivity, so fluorescence intensity can decrease in more acidic environments typical of some cell-free reactions over time As mKO2 exhibits sensitivity to ph shifts which decrease fluorescence over-time as the reaction gets more acidic, one solution could be making a stronger buffer concentration, both with HEPES and the potassium phosphate (mono/dibasic) In order to validate and optimize this hypothesis the following experimental set could be performed:

Sample 1 — Control

• HEPES-KOH pH 7.5: 45 mM
• K-phosphate dibasic: 5.63 mM
• K-phosphate monobasic: 5.63 mM

Sample 2 — Moderate buffer increase

• HEPES-KOH pH 7.5: 60 mM
• K-phosphate dibasic: 5.63 mM
• K-phosphate monobasic: 5.63 mM

Sample 3 — Stronger buffer increase

• HEPES-KOH pH 7.5: 75 mM
• K-phosphate dibasic: 5.63 mM
• K-phosphate monobasic: 5.63 mM

Sample 4 — HEPES + phosphate support

• HEPES-KOH pH 7.5: 60 mM
• K-phosphate dibasic: 7.5 mM
• K-phosphate monobasic: 7.5 mM

mTurquoise2 — Exhibits high brightness and quantum yield but depends on efficient translation and proper protein folding, making its fluorescence sensitive to ionic conditions in cell-free reactions. As mTurquoise2 fluorescence depends on correct folding and efficient translation, one approach to improve its performance is to optimize magnesium concentration, since Mg²⁺ plays a key role in ribosome function and protein folding in cell-free systems. In order to validate and optimize this hypothesis, the following experimental set could be performed:

Sample 1 — Control Magnesium Glutamate: 6.975 mM

Condition 2 — Moderate Mg increase Magnesium Glutamate: 8.000 mM

Condition 3 — Strong Mg increase Magnesium Glutamate: 10.000 mM

Condition 4 — Very strong Mg increase Magnesium Glutamate: 12.000 mM