Week 1 HW: Principles and Practices

I am curious about exploring the potential of HTGAA utilising the Design Build Test and Learn strategy. I have recently done an Innovate UK project in which I used MoClo kits to design the synthesis of xanthone in yeast. I am curious about exploring the potential of the phenylpropanoid pathway and the plethora of useful chemicals that could be produced.

Hence, I am proposing this study “Engineered Naringenin production through Design Build Test and Learn Strategy”, as a committed listener, for which I would be interested to work in Victoria,Genspace or Designer Cells.I am currently based in UK and hence intereted in working on nodes in UK.

The study involving the metabolic engineering of Naringenin will be conducted under approved BSL-1 laboratory conditions, in accordance with institutional and biosafety guidelines. For in vitro cell cultures, commercially available human-derived cell lines will be used. No primary human subjects will be directly involved. All data collection, analysis and reporting will adhere to the principles of ethical research conduct.

Purpose

Naringenin (C15H12O5) is a novel bioactive compound within the flavonoid group, found naturally in the skin of citrus fruits. This citrus flavonoid is well known for its anti-inflammatory, antioxidant, and anticarcinogenic properties, and can support skin against the detrimental impacts of inflammaging. Outside skin care research, Naringenin has shown promising results in treating issues like obesity and cardiovascular diseases.

The primary source of Naringenin is citrus fruits, hence consuming a lot of natural resources and labour. What if we make the process economical and produce naringenin at lab scale? Sounds great and doable through synthetic biology.

Design

I propose engineering the phenylpropanoid pathway in S. cerevisiae to produce naringenin, utilizing the Design Build Test and Learn (DBTL) strategy of MoClo kits.

Work Packages

• WP1: Engineering the key enzymes in the phenylpropanoid to flavonoid pathway for naringenin biosynthesis
• WP2: Confirming the expression through proteomic and metabolomic analysis
• WP3: Formulation development for enhanced solubility and bioavailability
• WP4: Biological evaluation on skin models

Assumptions

The proposed study holds the potential to revolutionise the cosmetic industry by providing an economical source of Naringenin without relying on natural resources, hence a step towards preventing the depletion of natural resources.

Risks of Failure & Success

The study requires step-by-step, careful pathway optimisation through omics approaches to identify any bottlenecks in the engineering strategy in a heterologous host. If not followed accurately, the pathway will not lead to Naringenin production.

With attention to detail and proper record keeping, the project holds the potential to revolutionize the cosmetic industry.

References

• Cai, J., Wen, H., Zhou, H., Zhang, D., Lan, D., Liu, S., & Zhang, J. (2023). Naringenin: A flavanone with anti-inflammatory and anti-infective properties. Biomedicine & Pharmacotherapy, 164, 114990.
• Manchope, M. F., Casagrande, R., & Verri Jr, W. A. (2016). Naringenin: an analgesic and anti-inflammatory citrus flavanone. Oncotarget, 8(3), 3766.
• Österlund, C., Hrapovic, N., Lafon-Kolb, V., Amini, N., Smiljanic, S., & Visdal-Johnsen, L. (2023). Protective effects of naringenin against UVB irradiation and air pollution-induced skin aging and pigmentation. Cosmetics, 10(3), 88.
• Rashmi, R., Magesh, S. B., Ramkumar, K. M., Suryanarayanan, S., & SubbaRao, M. V. (2018). Antioxidant potential of naringenin helps to protect liver tissue from streptozotocin-induced damage. Reports of Biochemistry & Molecular Biology, 7(1), 76.

Biosecurity & Safety Considerations

I will propose constructive applications of the proposed research in the healthcare and beauty sectors, and not impede research and researchers. The governance action to be addressed should focus on the rights of the worker/researcher. As many projects are led by industry, researchers must not be treated as paid labour but are provided fair recognition, intellectual property rights, and equal participation in patent ownership.

I am extremely thankful to be a part of HTGAA-2026, looking forward to learning from world leading researchers in the field of synthetic biology.

Kind Regards,
Tehseen

Homework 2 - DNA READ,WRITE,& EDIT

Part 1: Benchling & In-silico Gel Art

Simulate Restriction Enzyme Digestion

Part 3: DNA Design Challenge

3.1. Choose your protein

Retrieved Germin like protein sequence located on chromosome 8, important in plants biotic and abiotic stresses.

>sp|Q6YZY5|GL811_ORYSJ Germin-like protein 8-11 OS=Oryza sativa subsp. japonica OX=39947 GN=Os08g0190100 PE=1 SV=1

MASSSFLLLATLLAMASWQGMASDPSPLQDFCVADMHSPVLVNGFACLNPKDVNADHFFK
AAMLDTPRKTNKVGSNVTLINVMQIPGLNTLGISIÁRIDYAPLGQNPPHTPHRÁTEILIV
LEGTLYVGFVTSNPDNKFFSKV LNKGDVFVFPVGLIHFQFNPNPYKPAVAIAALSSQNPG
AITIANÀVFGSKPPISDDVLAKÁFQVEKGTIDWLQAQFWENNHY

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

>AF141878.1 Oryza sativa germin-like protein 1 precursor (RGLP1) mRNA, complete cds

CATCTCAAACACACCACCTGAATTAACAAGCAGGTATATATAAGCTACTAGCTAATTAAGAAGG
GCATTAGAATGGCTTCGTCTTCCTTCCTGCTCCTCGCCACTCTTCTTGCCATGGCCTCATGGC
AAGGCATGGCTTCGGATCCCAGCCCTCTGCAGGACTTCTGCGTTGCCGACATGCACTCACCTG
TGCTTGTCAATGGAAATTTGCTTGCCTGAACCCAAAGGACGTGAACGCGGATCACTTCTTCAA
GGCAGCAATGTTGGACACTCCTCGGAAGACGAACAAAGTTTGGGTCTAACGTCACACTGATCA
ACGTCATGCAGATCCCTGGCCTCAACACACTCGGCATCTCAATTGCACGTATTGACTATGCAC
CGTTAGGTCAAAACCCTCCCCACACTCATCCTCGTGCCACCGAAATCCTCACGGTGCTTGAAGG
AACACTCTATGTCGGCTTCGTCACGTCTAACCCGATAACAAGTTTTTCTCCAAAGTGCTCAAT
AAGGGTGATGTGTTCGTATTTCCTGTGGGGCTCATCCACTTCCAATTTAACCCKAACCCCTA
CAAGCCCGCGGTCGCAATTGCCGCACTTAGCAGCCAGAACCCTGGTGCCATCACCATTGCAAAT
GCGGTGTTTGGGTCGAAGCCACCAATCTCCGATGATGTTTTTGGCCAAGGCATTCCAGGTCGAA
AAGGGGGACAATAGATTGGCTCCAAGCCCAGTTCTGGGAGAACAACCATTACTGATCATCTTAC
TGATTGCATTTAAGAAAAAGTTTATGTGCAGTTACAGAGATCCTGCATAATTAACTTTATATA
CTACAATAAAATGACTTCTATTTCGTGTACCACTACTAGCTATATTCTGTGATGTAATTCCAA
TACATGTGTATGGTTTTTCCTGTTTAATTAATGAAATACAGTGTTCTTTGTGACAAAAAAAAAA
AAAAAAAAAAAAAAAAA

3.3. Codon optimization

Done using IDT tool as Twist Bioscience link provided was not working, optimised for expression in S. cerevisiae, removed type II restriction enzymes BsaI, BsmBI and BpsI.

The sequence entered of OsRGLP1 having start codon and a stop codon:

ATG GCT TCT GAT CCA TCT CCA TTA CAA GAT TTC TGT GTT GCT GAC ATG CAT TCT
CCA GTT TTG GTC AAC GGT TTT GCT TGT TTG AAC CCA AAG GAT GTC AAT GCT GAT
CAT TTC TTC AAG GCT GCT ATG TTG GAC ACT CCA AGA AAG ACC AAC AAG GTT GGT
TCC AAT GTC ACT TTG ATC AAC GTT ATG CAA ATT CCA GGT TTG AAC ACT TTG GGT
ATC TCC ATT GCC AGA ATT GAT TAT GCT CCA TTG GGT CAA AAC CCA CCA CAC ACA
CAC CCA AGA GCC ACT GAA ATT TTG ACT GTT TTG GAA GGT ACT TTG TAT GTT GGT
TTT GTC ACT TCC AAC CCA GAC AAC AAG TTC TTC TCC AAG GTT TTG AAC AAG GGT
GAT GTC TTT GTC TTC CCA GTT GGT TTG ATT CAC TTC CAA TTC AAC CCA AAC CCA
TAC AAG CCA GCT GTT GCT ATT GCT GCT TTA TCT TCT CAA AAC CCA GGT GCC ATC
ACC ATT GCC AAT GCT GTT TTT GGT TCC AAG CCA CCA ATT TCT GAT GAT GTT TTG
GCC AAG GCT TTC CAA GTT GAA AAG GGT ACC ATT GAT TGG TTA CAA GCT CAA TTC
TGG GAA AAC AAC CAC TAC TAA

These are the parameters used on the IDT codon optimisation tool; restriction sites to avoid were entered. However, when checking the sequence on Benchling for the absence of these enzymes, BsmBI and BsaI are not present, but one restriction site of BbsI does exist. The codon optimisation might have been more accurate if Twist or GenWiz software had been used.

3.4. You have a sequence! Now what?

The gene sequence can be recombinantly expressed in S. cerevisiae by using a protocol adapted from Gietz, R. D et al., 2007.

Part 4: Prepare a Twist DNA Synthesis Order

Build Your DNA Insert Sequence

Added 5’ and 3’ sequences for Type II restriction digestion and have annotated the sequence 5’ end - CDS - 3’ end. Can be subsequently used for ordering through Twist Bioscience.

Benchling sequence link

Used vectors of YTK kit and used the combinatorial assembly feature of Benchling to design the final vector: pYtk009 vector (pTDH3) promoter, pYTK051 vector (tENO1) terminator.

Homework 4 – Protein Design I

Part A. Conceptual Questions

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)

From 500 g of meat, you ingest approximately 1 mole of protein, which equals 6 × 10²³ molecules according to Avogadro’s constant.

2. Why do humans eat beef but do not become cows, eat fish but do not become fish?

Humans do not become cows or fish because food is broken down during digestion into biomolecules such as carbohydrates, fats, proteins, and simple sugars. These molecules are then reassembled into human tissues according to our own genetic code.

3. Why are there only 20 natural amino acids?

The genetic code naturally fits 20 amino acids while providing enough functional diversity to encode proteins. Proteins are encoded by triplet codons composed of four nucleotides:

4³ = 64 codons

61 codons encode amino acids and 3 codons function as stop signals.

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

Yes, synthetic amino acids can be designed by maintaining the standard backbone:

NH₂–CH–COOH

while modifying the R side chain to provide new chemical properties.

Examples:

1. Fluorinated amino acids

   • CF₃ side chain
   • Highly hydrophobic and chemically stable
   • Could increase protein resistance to degradation

2. Long hydrophobic amino acids

   • Side chain: (CH₂)₁₂CH₃
   • Promote membrane interactions
   • Useful for membrane-active peptides

3. Redox-active amino acids

   • Quinone-containing side chain
   • Capable of electron transfer
   • Useful in artificial enzymes or bioelectronic systems

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

Before enzymes and life existed, amino acids likely formed through prebiotic chemical reactions on early Earth and in space.

Simple molecules such as:

• methane
• ammonia
• carbon dioxide
• water
• hydrogen

could react under energy sources such as:

• lightning
• volcanic heat
• UV radiation

This concept was demonstrated in the Miller–Urey experiment (1953). Additionally, amino acids have been detected in meteorites, suggesting that space chemistry may have contributed to the origin of biomolecules used by early life.

6. If you make an α-helix using D-amino acids, what handedness would you expect?

An α-helix composed entirely of D-amino acids would form a left-handed helix, which is the mirror image of the right-handed helices formed by L-amino acids in natural proteins.

7. Can you discover additional helices in proteins?

Proteins contain several types of helices besides the α-helix, including:

• 3₁₀ helices
• polyproline helices
• collagen triple helices
• other engineered or synthetic helical structures

8. Why are most molecular helices right-handed?

Most natural helices are right-handed because proteins are composed primarily of L-amino acids. The stereochemistry of L-amino acids makes the right-handed helix sterically and energetically favourable.

9. Why do β-sheets tend to aggregate?

β-sheets aggregate because their backbone hydrogen bonds and side-chain interactions favour tight stacking, which can lead to structures such as amyloid fibrils.

Driving forces of β-sheet aggregation:

• intermolecular hydrogen bonding
• van der Waals interactions
• hydrophobic interactions

These forces stabilise the aggregated state.

Part B — Protein Analysis and Visualisation

1. Protein description

Germin and germin-like proteins are ubiquitous plant proteins expressed during various biotic and abiotic stresses.

The OsRGLP1 protein has confirmed superoxide dismutase activity, although additional activities such as oxalate oxidase remain under investigation.

2. Amino acid sequence

MASSSFLLLATLLAMASWQGMASDPSPLQDFCVADMHSPVLVNGFACLNPKDVNADHFFKAAMLDTPRKTNKVGSNVTLINVMQIPGLNTLGISIARIDYAPLGQNPPHTHPRATEILTVLEGTLYVGFVTSNPDNKFFSKVLNKGDVFVFPVGLIHFQFNPNPYKPAVAIAALSSQNPGAITIANAVFGSKPPISDDVLAKAFQVEKGTIDWLQAQFWENNHY

3. Sequence analysis

Sequence length: 224 amino acids

Most frequent amino acid: Alanine (A) — 23 occurrences

Amino acid frequencies

A: 23 L: 21 P: 18 V: 18 N: 17 S: 15 F: 15 G: 13 T: 12 D: 12 I: 12 K: 11 Q: 9 M: 6 H: 6 Y: 4 E: 4 W: 3 R: 3 C: 2

4.Protein sequence homologues

BLAST search parameters:

• Program: blastp
• Matrix: BLOSUM62
• Alignments: 250
• Scores: 250

OsRGLP1 belongs to the germin-like protein (GLP) family. Germins share 30–70% sequence similarity with wheat germins. Protein sequence homologues

Reference:

Zhang, Y. et al. (2018). Overexpression of germin-like protein GmGLP10 enhances resistance to Sclerotinia sclerotiorum in transgenic tobacco. Biochemical and Biophysical Research Communications.

5. Structure information (RCSB)

Structure determination

3. Identify the structure page of your protein in RCSB
4. • 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)
5. https://www.rcsb.org/3d-view/2ETE • CRYSTAL STRUCTURE OF GERMIN (OXALATE OXIDASE) Woo, E.J., Dunwell, J.M., Goodenough, P.W., Marvier, A.C., Pickersgill, R.W.2000) Nat Struct Biol 7: 10 https://www.rcsb.org/3d-view/2ETE

• Does your protein belong to any structure classification family Germin and GLPs are described as archetypal members of the cupin superfamily (Dunwell 1998). The cupin superfamily of proteins is named based on this conserved B barrel fold (Cupa; Latin word for barrel), discovered using a conserved motif found in germins.

4. Open the structure of your protein in any 3D molecule visualisation software Used Alphafold to generate the secondary structure (Helices and Sheets), the protein has more β-sheets than α- helixs. Forming a central β-sheets core surrounded by helices and loops. Hydrophobic residues are buried in the core, and hydrophilic on the surface

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.

1. Copy the HTGAA_ProteinDesign2026.ipynb notebook and set up a colab instance with GPU. i) Deep mutational Scan

ii) Latent Space Analysis Scan

iii) Inverse fold modelling

iv. The heat map was generated in C1-protein protein language modelling, specifically from a deep mutational scan using the ESM-2 protein language model, which predicts the probability of every amino acid mutation at each position in the protein sequence

Week 3 Homework

You can view/download my presentation here: Week 3 Homework PPT

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project