Week 2 HW: DNA Read, Write, & Edit

Part 1: Benchling & In-silico Gel Art

For this week’s Part 1 homework, students are tasked with doing several tasks. First, create a Benchling account. Second, simulate restriction enzyme digestion of a predetermined sequence using several enzymes. Third, once students are able to simulate the restriction enzyme digestion, they can create a simple art form named the Latent Figure Protocol.

For the first part of the task, I would need to create an account for Benchling. It was a relatively simple process of using my Gmail account to sign up for Benchling. A few minutes later, after entering my details, I am now the proud owner of a Benchling account.

Next, I would need to import the predetermined sequence into Benchling. For this exercise, we were given a Lambda DNA template from New England Biolabs (NEB). According to the website, the Lambda DNA is a commonly used DNA substrate isolated from bacteriophage lambda (cI857ind 1 Sam 7) with a length of 48,502 base pairs.

I entered the FASTA format of the sequence into the Create DNA/RNA Sequence tab and clicked Create, creating my first (and hopefully not my last) Benchling project. I named the project Lambda_NEB for this exercise, the same name as the product listed in NEB.

Now comes the first challenge, simulating a restriction enzyme digestion using several of the following enzymes:

• EcoRI

• HindIII

• BamHI

• KpnI

• EcoRV

• SacI

• SalI

Bioinformatics was one of my weakest subjects during my university days, years ago. And due to switching to the Fast-moving Consumer Goods industry for several years, I didn’t keep up with the current biotechnology trends as much as I’d liked. For this reason, I needed some time to refresh my memories and get acquainted with the UI of Benchling, a bioinformatics tool I never used before. Luckily, Ice’s YouTube video on Benchling basics helped me start on the task at hand.

After familiarizing myself with the UI of the website, I managed to find the Digest function and start with the next part of this homework. I entered EcoRI as my first restriction enzyme digestion simulation and made my first virtual gel image

After this, I repeat this with the rest of the enzymes listed.

Then I started to think about what pattern I would like to create. Due to my novice nature with Benchling, I listed several criteria to help narrow down a pattern I would create. It has to be simple but distinctive enough to be unique or at least memorable, have some connection to me to give it an identity, and tell a story. After some deliberation, I decided to try making a number 13 for my gel art due to being born at Friday the 13th. For many people, the number 13 is often associated as an omen of bad luck. I used to believe in that too when I was very young. I don’t anymore and am now a firm believer of lucky number 13.

After several tries of experimenting with different restriction enzymes, I chose the image above as my final draft. I used the ladder as the number 1, AjuI as the three overhang strips for the 3, and a combination of BamHI, EcoRI, and EcoRV as the back segments of the 3. Though, as seen above, it isn’t a perfect three with a few segments missing to complete it. While I would love to have a more accurate number 13 picture, finding the right restriction enzyme to fill the last top third of the number 3 segments is already taking too much time. And with my unfamiliarity with Benchling, it would be like trying to find a needle in a haystack to create the perfect number 13.

Unfortunately, due to not having access to a laboratory, I wouldn’t be able to perform a gel electrophoresis and gel visualization and would have to make do with the in-silico visualization.

Part 3

sp|P02754|LACB_BOVIN Beta-lactoglobulin OS=Bos taurus OX=9913 GN=LGB PE=1 SV=3 MKCLLLALALTCGAQALIVTQTMKGLDIQKVAGTWYSLAMAASDISLLDAQSAPLRVYVE ELKPTPEGDLEILLQKWENGECAQKKIIAEKTKIPAVFKIDALNENKVLVLDTDYKKYLL FCMENSAEPEQSLACQCLVRTPEVDDEALEKFDKALKALPMHIRLSFNPTQLEEQCHI

The amino acid sequence above is for the protein Beta-lactoglobulin (BLG). BLG is the most abundant whey protein in milk, responsible for most of dairy milk’s emulsion stability, viscosity, and gelling properties (Hoppenreijs, 2024). It can be equated that BLG is responsible for dairy milk’s taste and texture, leaving plant-based milk often with inferior palatability compared to its conventional counterpart. As stated in my previous homework, Indonesia faces a problem of increasing dairy demand and its inability to meet domestic demand. The precision fermentation of BLG would be a step towards resolving this problem without overly relying on dairy imports and further burdening the national budget.

Using the reverse translation tools of Bioinformatics.org, I managed to reverse translate the BLG protein into the most statistically likely used DNA sequence and the degenerate DNA sequence that uses IUPAC degenerate nucleotide codes to show the different nucleotide possibilities.

The image above shows the sequence of most likely codons.

While this one shows the sequence of consensus codons. Notice how there are letters other than atgc? That is the IUPAC degenerate nucleotide codes. Due to codon degeneracy, in which multiple codons code for the same amino acids. R could either code for A or G, Y for C and T, and so on and so forth.

Next, I run the sequence through a codon optimizer tool and selected Saccharomyces cerevisiae or commonly known as Baker’s Yeast, as the intended organism modified with the BLG sequence. I specifically chose Baker’s Yeast over the easier-to-grow bacteria such as Escherichia coli due to one reason. Using bacteria as the host organism can lead to several post-translational modification issues, such as the generation of inclusion bodies due to their prokaryotic nature and the complexity of eukaryotic proteins and other more complex bioactive compounds, such as bovine BLG (Gao et. al., 2025). Yeasts and fungi have advantages over bacteria as cell factories due to their eukaryotic nature capable of post-translational modification and high yield (Deng et. al., 2026).

As previously stated, I intend to use precision fermentation to produce BLG protein, hopefully on an industrial scale in the future. To do this, I would need a genetically modified microorganism able to synthesize BLG. With precision fermentation, I could selectively ferment BLG protein in a bioreactor and recover the protein using different recovery methods, whether the particular protein is intracellular or extracellular (Deng et. al., 2026).

optimised reverse translation of sp|P02754|LACB_BOVIN Beta-lactoglobulin ATGAAATGCTTGTTGTTGGCGTTAGCATTGACATGTGGTGCTCAAGCTTTGATTGTTACCCAAACAATGAAAGGTTTGGATATTCAAAAAGTTGCTGGTACTTGGTACTCCTTGGCAATGGCTGCCTCTGACATTTCTTTATTGGATGCCCAGTCTGCACCATTGAGAGTATATGTCGAAGAATTGAAGCCAACTCCTGAGGGTGATTTAGAAATCCTTTTGCAAAAATGGGAAAATGGTGAATGCGCCCAAAAAAAAATTATTGCCGAAAAGACAAAAATCCCAGCAGTCTTTAAAATTGACGCATTGAACGAAAATAAGGTATTAGTTTTGGATACTGATTACAAGAAATACTTGTTGTTTTGTATGGAAAATTCAGCTGAACCAGAACAATCATTGGCCTGTCAATGCCTTGTTAGAACCCCAGAAGTGGACGATGAAGCTTTAGAAAAGTTTGATAAAGCCTTAAAAGCACTACCAATGCATATTAGATTATCTTTTAACCCAACCCAACTTGAAGAACAATGTCATATT

As stated in the previous lectures and homework, each organism has a preferential use of certain codons for synonymous amino acids, known as codon usage bias. This is due to the fact that different codons are decoded by tRNAs that exist at different cellular abundances. The tRNAs abundant in a mammal, such as a cow, would be very different from those in yeasts, such as Baker’s Yeast. Without this optimization would be slower translation speed, produce ribosome stallings, be more prone to protein misfolding, and have lower yields, not ideal for industrial scale-up.

4.1. Create a Twist account and a Benchling account