If you haven’t checked out my content library yet, it includes all the bits and bobs of information covered on The Binding Brief to date, categorized by theme!!!

This week, we have some exciting things to discuss.

1. Raw Data: What’s the real role of scientists in this AI world?

2. This Week’s Deep Dive: How do you choose a PhD research topic?

3. Bench Notes: A quick way to check if your protein has disulphide bonds

4. Antibody Spotlight: Antibodies used against p16^INK4a or p16-ARC?

5. In Silico: What do we know so far in de novo protein design?

6. Antibody Term of the Week: Native versus recombinant antibodies

1. The Raw Data

As I write this, I just finished Day 1 of the AI x Bio conference by Wellcome Connecting Science (#AIxBio26). Today’s sessions covered solving the DNA and RNA gene regulatory code, as well as solving proteins and protein data.

The day opened with a fantastic keynote by Pushmeet Kohli (Google DeepMind) on leveraging AI to advance biology. He spoke about the sheer complexity of the human body making biology a perfect application for AI, ranging from understanding proteins (structure, function, binders) to decoding the genome (missense and non-coding variants) and building a virtual cell. All of this underscored DeepMind's core purpose: to build AI responsibly, for the benefit of humanity.

I came into this conference with a specific mindset that I wouldn't get swept up in the hullabaloo of AI, but instead ask whether AI is actually the right tool for a specific biological problem. What struck me is that every single presenter embodied what I'd call "cautious optimism." Nothing over-the-top, which is exactly what the field needs right now. It was a perfect blend of using AI to fast-track data output, paired with rigorous checks and balances in validating it.

AI is progressing faster than the human mind can fully comprehend. Pushmeet put it well:

”The real role of scientists now is not to ask the "how" (which is what we're trained to do) but rather the "what."
What is an AI hallucination, and what is a real result?”

That discernment is where scientists hold their credibility.

Check out this big news that was announced today at the conference. ⤵️ Looking forward to Days 2 and 3.

2. This Week’s Deep Dive

No one tells you how different a Masters program is from a PhD. Even if your Masters degree was a research-based one, a PhD is a whole different ballgame. It feels like the next step in a logical progression of education, but that is far from the truth. In a Masters program, you sign up for the pool membership and by the end of it, you are taught the rules and regulations of the club and you learn where to get the towels from. In a PhD, you are expected to know all of it and dive directly into the deep end of the pool. Forget the learning to swim part. In a nutshell, your PhD teaches you how to do research and become an independent thinker.

A common question I get from students started out their PhD - “how do I choose my PhD research topic?” The banal answer is - find a knowledge gap in the topic of your interest and that becomes your PhD. But the real question comes next, “how do I find a knowledge gap in a field that is quite new to me?” It seems like and it is a daunting process. But you are not alone in thinking so. There are some tips and tricks to navigate this and I have put together a brief on how YOU can choose your PhD research topic and be happy with it as well. Check it out.

The Binding Site

How Do I Choose My PhD Research Topic

Choosing a research topic for your PhD can be a daunting experience. We are meant to find a 'knowledge gap'. But how does one find the gap when we are still trying to grasp all the knowledge of a field that is entirely new to us? It can feel complicated and overwhelming. But you are not alone in thinking so. However, it doesn't have to be this complex. …

Read more

2 months ago · 15 likes · 4 comments · Madhuri Manohar, PhD

3. Bench Notes

If your protein has cysteine residues that form disulfide bonds integral to its structure and function, checking for their appropriate formation is essential. There are many ways to do so: mass spectrometry, NMR, Raman/FTIR spectroscopy, circular dichroism, SDS-PAGE, and more. Most of these require expensive instrumentation, take time, and are better suited for validation. A really quick way of estimating disulfide bond formation is the Ellman’s assay, run in a 96-well plate format.

Pioneered in 1959 by George L. Ellman, the assay specifically measures free sulfhydryl (thiol) groups. Ellman’s reagent (5,5’-dithio-bis-(2-nitrobenzoic acid), known as DTNB) reacts with free sulfhydryl groups to form a yellow-colored product measurable by absorbance at 412 nm. Importantly, checking for disulphide bond formation in proteins is a negative reaction wherein no yellow-colored product indicates that disulphide bonds are present in the native folded protein. Cleaving those bonds using β-mercaptoethanol (BME) denatures the protein, exposes the thiol groups, producing a measurable yellow signal at 412 nm.

Thus, the Ellman’s assay is a quick, quantifiable way of measuring disulphide bond formation, before proceeding to the more-involved validation methods mentioned above.

Tip: Since this is a negative reaction, always include a negative and positive control (L-cysteine in this case). Another indicator that the assay is functioning correctly is the isosbestic point at 356 nm, the wavelength reported in literature for the Ellman’s reagent. At the isosbestic point, reactants and products are at equal concentrations and in equilibrium.

Check out Ellman’s 1959 paper to know more. I also found this protocol quite useful that I adapted for my PhD research.

4. Antibody Spotlight

Science released an article just a few days back talking about how the similarity in names between two proteins has created an antibody mix up that is potentially affecting the science and data on 300+ research papers. Yikes! Researchers who intended to buy antibodies against p16INK4a may have bought antibodies against p16-ARC instead. Oopsy! p16INK4a is a tumor suppressor protein that regulates the cell cycle by inhibiting CDK4 and CDK6, while P16-ARC regulates the cell’s cytoskeleton by controlling actin polymerization.

Where this is problematic?

Some researchers reported results that only made sense with the correct antibody, suggesting that data was possibly shaped to fit expectations. With many of these papers cited hundreds of times, the misinformation is already compounding, and if ingested by AI systems, flawed findings could be treated as ground truth and propagated even further. Mistakes in peer-reviewed papers are easy to find but very hard to fix.

Where this is easily fixable?

Some teams simply listed the wrong product code in their methods section while actually using the right antibody, as confirmed by purchase receipts and lab notebook records, without impacting data integrity. Another reason why good documentation practices must be mandated not just in clinical labs, but even in academic labs.

Where we shall never know?

Think of all those abandoned results and experiments that never saw the light of the day because the wrong antibody was used. Real valuable scientific data that could have come out of those experiments! I guess we’ll never know.

To read more about it, check out the article on Science as well as detailed analysis on the For Better Science blog.

5. In Silico

When I sat in the AI x Bio conference listening to all the distinguished speakers, I realized the breakneck speed of the use of artificial intelligence in biology. From DNA to RNA to protein applications, from compressing drug discovery timelines to understanding the molecular dynamics better, from allowing old problems to disappear to creating novel binders for undruggable targets, the AI research is galloping at a rate faster than I can comprehend. The amount of models out there with each having its unique advantages and datasets, and caveats and limitations - the research is mind-boggling. Whenever I feel so, I just sit down with a good old review paper (and my coffee) and get on with my reading. Of all the solutions AI is being applied in biology, my core interest has always been protein design. To get to know more, I chanced upon this article from the David Baker lab (2024 Nobel Laureate in Chemistry) that details de novo protein design. Check out the article on this link to know more.

6. Antibody Term of the Week

This week we delve into native versus recombinant antibodies. Native antibodies are sourced from a biological source, either from immunized animals or traditional hybridoma cell lines. Recombinant antibodies, on the other hand, are synthesized using recombinant DNA technology by cloning the genes encoding the heavy and light chains of the antibody into expression systems. Native antibodies are inherently variable as they could be polyclonal or monoclonal. However, recombinant antibodies are highly reproducible and are well-defined monoclonal antibodies. Think of it like a tailor-made suit versus an off-the-rack blazer. They both fit, but one is made just for you.

This site is free and I intend to keep it that way for as long as I can because good science communication should be accessible, especially for students. If you enjoy what you read and feel like buying me a coffee along the way, I won't say no. ☕ And if you're not a student, consider paying it forward to one who is.

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1. Raw Data: All the raw unfiltered thoughts and conversations from me

2. This Week’s Deep Dive: The long-form informative content on antibodies

3. Bench Notes: Tips and tricks for lab experiments

4. Antibody Spotlight: The antibody of the week and why it was important

5. In Silico: Antibody development in the AI world

6. Antibody Terms: Commonly used terminology

1. Raw Data

• Does it matter what you work on (or) who you work with?

• Is it alright to rest and not be productive?

• Does someone’s success take away from us?

• What’s the real role of scientists in this AI world?

2. This Week’s Deep Dive

• Antibodies and their elegant structure

• Antibodies: How are they named?

• 10 tips for navigating lab meetings

• How do you choose a PhD research topic?

3. Bench Notes

• Leakproof your SDS-PAGE gels in the lab

• Benchling: Integrate your molecular biology suite and lab notebook into one

• Calculating accurate protein concentrations using a NanoDrop spectrophotometer

• CamSol: To predict protein solubility score

• Ellman’s Assay: A quick way to check disulfide bond formation in your proteins

4. Antibody Spotlight

• Inmazeb (REGN-EB3) and Ebanga (mAb114): Will they work against the 2026 Ebola outbreak in DRC and Uganda?

• MBP134: An experimental Ebola drug for the Bundibugyo virus

• ❗️p53 Antibodies by Thermo Fisher Scientific

• ❗️Antibodies used against p16^INK4a or p16-ARC?

5. In Silico

• How does DeepMind decide which problems to solve: Interview with Pushmeet Kohli (VP, Science and Strategic Initiatives at Google DeepMind)

• Nature publishes 3 AI papers on the same day: Robin, Co-Scientist, and ERA

• CamSol: To predict protein solubility score

• Review article on de novo protein design?

6. Antibody Terms

• Polyclonal versus Monoclonal Antibodies

• Affinity versus Avidity

• Epitope versus Paratope

• Native versus Recombinant Antibodies

This week, we have some exciting things to discuss.

1. Raw Data: Science is for everyone

2. This Week’s Deep Dive: Communication is key in science - 10 tips!

3. Bench Notes: Are you getting accurate protein concentrations?

4. Antibody Spotlight: p53 Antibodies by Thermo Fisher Scientific

5. In Silico: Using CamSol to study the protein solubility score

6. Antibody Term of the Week: Epitope versus Paratope

1. The Raw Data

As always, I love this section of the newsletter, where I share my raw, unfiltered thoughts. I had a chance to chat with (The SuperAdvisor) yesterday, and one thing we touched upon is lab environment. We all love a happy lab!

Yes, the supervisor plays a central role in keeping the lab a happy and collegiate space. But, each one of us has a part to play too. One of the subtler things that contributes to a joyful lab culture is being genuinely happy for another person’s success. We all know how hard it is to get good data. It involves a lot of sweat and tears, and every result that adds to the body of scientific knowledge is hard-won.

Yet there’s often a small tinge of jealousy when we’ve been struggling for months and someone else gets that pretty bar graph with tiny error bars. But someone’s success doesn’t take away anything from us. It doesn’t block our path or reduce our chances. If anything, it expands what’s possible. This isn’t competition because there is space for everyone in science.

2. This Week’s Deep Dive

Being a researcher demands an extraordinary breadth of skills. You need to think clearly, plan meticulously, design sound experiments, execute diverse protocols, apply statistics to make sense of your data, situate your findings within existing literature, navigate the push and pull between your own instincts and your supervisor's expectations, and constantly upskill, often learning new techniques on the fly. This is what makes finishing a PhD feel unlike anything else: you develop all of this within a compressed three to four years, even if it begins with the unsettling sensation of being thrown into the deep end.

And yet, in spite of doing alllllll this, none of it lands if you can’t communicate it.

The first real opportunity most researchers get to convey their science is in routine lab meetings. A relatively low-stakes space to grow your scientific voice and develop your communication skills. Depending on your lab culture, though, these meetings can feel daunting. To mark my 10th article on Substack, I’ve put together 10 tips to help you navigate these dreaded meetings. I hope you find them useful.

3. Bench Notes

There are many ways to quantify your protein: Bradford, BCA, A280 absorbance, Qubit, SDS-PAGE densitometry, mass-spectrometry and more, each based on different principles. Measuring the absorbance at 280 nm was my personal preference, which can be done in a 96-well plate format or on a NanoDrop. Working with 14 antibodies each day, the NanoDrop was my go-to: it was fast and needed only 2 µL of sample.

One thing to watch out: the NanoDrop defaults to 1 Abs = 1 mg/mL, which assumes all proteins have the same amino acid composition. That is rarely the case, if ever, true. UV absorbance at 280 nm is driven by aromatic amino acids (tryptophan, tyrosine, and phenylalanine), and reduced cystines, each of which each absorb differently. Phenylalanine’s contribution is negligible and is typically omitted from calculation. To account for this differential absorbance for a more accurate concentration estimate, input your protein's molar extinction coefficient (ε) and molecular weight (kDa) directly into the NanoDrop software. Both can be calculated using the ExPASy ProtParam tool or Benchling (see Issue #2 of the Binding Brief). The output measurement would give a protein concentration (mg/mL or M) that is different from A-280. Check it out next time you’re in the lab.

Pro Tip: Consistency is key throughout your research project. Use the same protein quantitation technique always.

4. Antibody Spotlight

If you’ve been following social media and LinkedIn this past week, there’s been a notable discussion around one of the Anti-p53 monoclonal antibodies sold by Thermo Fisher Scientific. Sholto David, PhD, an analytical scientist in the UK, flagged a potentially fabricated western blot in the internal validation data of a Thermo Fisher p53 antibody. What initially appeared to be an isolated case was subsequently identified as a pattern across multiple p53 antibodies, documented on the blog For Better Science. Johan Duchêne and Reese Richardson have since flagged concerns across additional Thermo Fisher antibodies, suggesting this extends well beyond a single product.

Thermo Fisher has not yet issued any comments. They are likely investigating internally. Hopefully! 👀 To be clear, these antibodies may well bind their targets; I can’t speak to that without testing them myself. But fabricated validation data is a serious integrity issue regardless of whether the product performs. Researchers and PhD students worldwide rely on vendor-supplied validation when selecting antibodies, trusting that the data reflects real experiments. If you’ve ever run a western blot, you know the weight of spending two full days on an experiment when it ends in heartbreak 💔 (a WB heartbreak is worse than a real one). The last thing anyone needs is to discover that the foundation it was built on was unreliable. Science depends on trust: in the data we share and in the data we use. This situation is a reminder of why independent internal quality checks are not optional but are essential. I’ll be following this news to see how it unfolds.

For further reading: This article by Ayoubi et al., 2013 is an interesting read on antibody validation.

5. In Silico

Protein solubility is an extremely important biophysical parameter to assess during protein and antibody design. We could prepare the most elegant antibody with the highest binding to a given substrate, but if the protein aggregates during production, that factor alone can impact its utility.

CamSol is a sequence-based method for predicting protein solubility and generic aggregation propensity, developed by Dr. Pietro Sormanni in the lab of Professor Michele Vendruscolo at the University of Cambridge (my alma mater, which makes it even cooler). Using CamSol, one can predict protein solubility as a function of pH and in the presence of post-translationally modified and/or non-canonical amino acids. And beyond sequence-based predictions, CamSol also defines a solubility profile based on structure, taking into account the proximity of amino acids and their solvent exposure. How cool is that?

Mapping the solubility profile of every individual amino acid based on both its intrinsic solubility and its positioning in the 3D structure, allows us to assess the impact of aggregation on binding properties. This is an example of an scFv (single chain variable fragment antibody) I was working on during my PhD. Excellent binding profile, with K_d values in the lower nM range. However, the CDR3 regions of the heavy chain (the most important region for binding) had a low solubility profile. Having this knowledge helped me make better decisions about which antibodies to take forward for testing.

I had the chance to work briefly in Dr. Pietro's lab during my PhD, learning from peers who were equally passionate about antibody research. That experience gave me a deep appreciation for how tools like CamSol can bridge rigorous biophysics with practical protein engineering decisions. Check out these papers, Sormanni et al., 2015 and Sormanni et al., 2017 to know more about CamSol.

6. Antibody Term of the Week

Today we’re diving into the most swapped-out words in immunology.

Epitope: Breaking this down to its Greek roots - epi means “on top of” and topos means “place.” So, quite literally, it translates to “a place on top”. This is the site on the antigen where an antibody binds.

Paratope: In Greek, para means “beside” or “alongside”. So, the paratope is the complementary site on the antibody that does the binding, the place that fits alongside the epitope.

Now you might ask: couldn’t epi just as easily refer to something on top of an antibody? Fair enough. But think about who came first. Without an antigen, your immune system would have nothing to respond to, and your B cells would never produce an antibody in the first place. The antigen is the original, the target, and the reason the whole response kicks off. So in keeping with seniority, the antigen earned the epi tag. Snooze, you lose! Thus, the antibody got the para tag, always defined in relation to something else. Hope this helps.

This site is free and I intend to keep it that way for as long as I can because good science communication should be accessible, especially for students. If you enjoy what you read and feel like buying me a coffee along the way, I won't say no. ☕ And if you're not a student, consider paying it forward to one who is.

Buy me a coffee ☕️

Subscribe now

This week, we have some exciting things to discuss.

1. Raw Data: Is it alright to rest and not be productive? 🔗

2. This Week’s Deep Dive: Plagiarism or taking inspiration? 🔗

3. Bench Notes: How can you integrate your lab notebook and molecular biology suite into one system? 🔗

4. Antibody Spotlight: Experimental Ebola drugs - what do we have so far? 🔗

5. In Silico: How do you feel about using AI in Biomedical Research? 🔗

6. Antibody Term of the Week: “Do I have affinity or avidity towards you?”, asked an antibody to an antigen. 🔗

1. The Raw Data

If you’re reading this newsletter edition right as it goes live, I am probably at the beach enjoying the ocean waves, the date palm trees, staring at the hard rocky mountains of Oman, while my body makes copious amounts of Vitamin D in the 46 ℃ weather. Today is Eid al-Adha, a major festival in the Islamic calendar celebrating faith, sacrifice, and generosity observed by Muslims around the world. We are off for almost the entire week and decided on a last minute staycation at a beach resort. As I grow older (although I still feel like a kid sometimes), I have started to appreciate staycations — quiet quality time with the family, a stroll at sunrise and sunset, no digital devices, and reading a book before bed. The hustle culture has made such slow periods of time seem unproductive. But, each time I go on vacation, I come back feeling more energized and most importantly, focused and enthused. So, may be next week’s “Raw Data” section might have something more profound for you all after an intense 2-day period filled with reflections. Haha. Just kidding. I hope I don’t pass out from the heat.

Wishing all my readers a very happy Eid al-Adha from Oman.
May this occasion bring peace and love to all of humanity.

2. This Week’s Deep Dive

I had an interesting experience this week. I was scrolling through LinkedIn (hello job search!), and I noticed that a page had written an article. My eyes of course popped open when I saw ‘antibodies’. As I read through the post, it felt eerily similar to the one I had written on Substack few weeks back on the naming convention of monoclonal antibodies. I was little suspicious at first — being an informative article, it is possible that someone wrote it all by themselves. Some of the wordings sounded the way I would write something. A little verbose to add some extra clarity… is my style (working on the art of brevity). But when I looked at the image, I was sure they had plagiarized taken inspiration from my article. Although I had used Google Gemini to create the main infographic, I had also used Canva subsequently to make edits to the image. And lo and behold, the same edits were also present on the image on the article, just with some color modifications.

I thought a lot about whether I should reach out to the page owner and mention how similar it is. My mind had 100 thoughts of course! What if they deny any plagiarism? What if they think I am being petty? What if they just block me and move on with their lives? I guess this article felt very personal because I had put in a lot of time, effort, and thought while writing it. I reached out with a kind message. Within minutes, the editor responded that they were unaware and it was an oversight on their part. They immediately credited me for it and added a link to my Substack article.

Not that that credit brought any extra views or subscribers to my Substack; that is beside the point. But, this small incident taught me something that I struggle with many times - if something is yours, own up to it, small or big. Ask for what is yours, for it could be a genuine oversight.

If you haven’t read this article yet (it’s also my most viewed article!), head over and check out how antibodies are named.

3. Bench Notes

Enough reflections for today. Getting back to science now. On June 26, 2000, when the Human Genome Project had a “rough draft” ready, it was announced as a cool press release. Rough draft? But in this case, a rough draft meant volumes of books that contained a strings of As, Gs, Ts, and Cs catalogued by the human chromosome number. If you skipped a single letter ‘A’, it would create a frameshift error1 on the entire human genome. Imagine the catastrophe!

But today, we need not write down every single letter of the DNA sequence. We have a whole suite of modern molecular biology tools. The one I use (and I’d highly recommend) is Benchling. Of course, it provides a lot of basic tools to go with your cloning needs. But, more specifically, for those working with antibodies in the lab, it has several features such as:

1. Antibody Annotation: delineating the variable complementarity-determining regions (CDRs) and conserved framework regions (FRs) of the antibody.

2. Antibody Numbering: based on either the Chothia, Kabat, or IMGT numbering scheme (more details on that in an upcoming article).

3. Chemical Liability Sites: where in the sequence are antibodies sensitive to degradation caused by either asparagine deamidation, tryptophan oxidation, methionine oxidation, and aspartate isomerisation.

An added advantage of Benchling is that also has an Electronic Lab Notebook functionality. A good way to have it all in one place and cross-link sequences to lab notes! Make sure to sign up on Benchling using your academic email ID for many extra perks.

PS: This is not a sponsored post. If I love a new software or a new coffee shop, I can’t help but share it with all.

4. Antibody Spotlight

Last week on The Binding Brief, we touched upon the low therapeutic efficacy of Inmazeb and Ebanga (the two FDA-approved monoclonal antibodies against the Zaire Ebolavirus; EBOV), for the Bundibugyo virus (BDBV), the Ebola strain currently ravaging through the Democratic Republic of the Congo and Uganda, underscoring the need for broad spectrum antibodies. Dr. addressed the Ebola vaccine gap on the previous edition of The Biotech Tea. With no approved monoclonal antibody-based therapies or vaccines ready for deployment for BDBV specifically, given the urgency, the next best option is the use of experimental therapies.2

One such experimental drug being considered is MBP134 developed by Mapp Biopharmaceutical, a San Diego-based Biotech company, in collaboration with the US Government’s Biomedical Advanced Research and Development Authority (BARDA). MBP134 is a pan-ebolavirus two-antibody cocktail comprising antibodies ADI-15878 and ADI-23774. A single 25 mg/kg dose showed sufficient protection in ferrets and non-human primates against the Zaire, Sudan, and Bundibugyo ebolavirus. MBP134 was administered in the 2022 outbreak in Uganda that primarily involved the Sudan virus. Being an investigational therapeutic drug, it was administered under ‘compassionate use’ in consenting patients. There is a lot of talk currently that MBP134 and Remdesivir (an antiviral) are the experimental forerunner drugs for a clinical trial being sponsored by the WHO with the approval of the local governments. This is the news I will be following in the coming days!

5. In Silico

The buzz in the AI x Bio world this week was that Nature published 3 articles on the same day! FutureHouse introduced Robin, a multi-agent system to provide an iterative lab-in-a-loop framework that can conduct hypothesis generation, experimental design, results interpretation, and updated hypotheses. Google DeepMind introduced Empirical Research Assistance (ERA) to help scientists write expert-level softwares, solving the bottlenecks in computational experiments. In other big news, Google DeepMind also introduced Co-Scientist, similar to Robin, to assist in hypothesis generation and provide structured thinking support.

I know I am most excited to try out Co-Scientist. I will revert back when I get to try these out and apply it to solve a tangible problem. But, before that, I know there has been a lot of reticence about using AI for Biomedical Research. And that’s how It should be! As scientists, it’s our job to question everything. We have to query everything, test it, validate it, and most importantly, know the constraints of any tool or system. Speaking on the bio world, the narrative that AI is coming for our jobs seems like a little bit of a hype. No AI system can be built to do media changes over the weekend on our behalf (IYKYK). I don’t think anyone will lose their jobs to AI, but they might lose their jobs to someone who knows how to use AI effectively.

At the end of the day, we as humans have something that AI can’t take away from us and that is the power of intuition! Every scientist has felt this power in their career when logic did not make sense to piece together the reasoning to do an experiment or test a hypothesis, and our lab mates thought we were psychos. But we still went down that path because something in us told us that there was value in exploring and may be, it might provide new insights and answers. In this AI world, a superpower would be to hone this skill of intuition while you use AI to compress drug discovery timelines.

“It is through science that we prove, but through intuition that we discover.”
- Henri Poincaré (French Mathematician and Theoretical Physicist)

6. Antibody Term of the Week

Today we shall learn the difference between the terms affinity and avidity. Affinity refers to the binding strength between an antibody and antigen at a single binding site. Whereas, avidity refers to the cumulative binding strength between an antibody and antigen taking into account all the binding sites and valency. An easy way to remember it - think of the strength of the bond formed when you give someone a handshake (affinity) versus when you hug them with both hands (avidity).

This site is free and I intend to keep it that way for as long as I can because good science communication should be accessible, especially for students. If you enjoy what you read and feel like buying me a coffee along the way, I won't say no. ☕ And if you're not a student, consider paying it forward to one who is.

Buy me a coffee ☕️

Subscribe now

This week, we have some exciting things to discuss.

1. Raw Data: Does it matter what you work on (or) who you work with? 🔗

2. This Week’s Deep Dive: Antibodies and their elegant structure 🔗

3. Bench Notes: Is there a way to leakproof your SDS-PAGE gels in the lab? 🔗

4. Antibody Spotlight: Can we use the existing monoclonal antibodies for the recent Ebola outbreak in the DRC and Uganda? 🔗

5. In Silico: Where are we today in the world of artificial intelligence for biological applications? 🔗

6. Antibody Term of the Week: A surprise segment to build our antibody vocabulary 🔗

1. The Raw Data

We’ve always been taught to ‘find our passion’ - to decipher what really gets us going each day, what makes our hearts beat with inspiration, what is the one thing that makes our soul happy, etc. (The real answer is coffee is what gets us going each day. But, I digress.) This whole ‘find your passion’ narrative has always felt a little over the top to me. There are enough health challenges in humanity that biology is never going to run out of problems to solve. And individual interests change over time anyway. That’s just being human.

What I think matters more is where your energy comes from. Passion is energy. But energy doesn’t only flow from what you work on. Think about the colleague who works on something you find completely uninteresting but shows up every day with genuine enthusiasm, versus the one who shares your exact research focus but drains every room they walk into. Which environment actually moves you forward?

We are a sum of the people we surround ourselves with. Surround yourself with people who genuinely want to make a difference, and you’ll find your thinking expanding well beyond your own project and professional pursuits. What you work on matters. But the most important factor in that equation is who you work with.

2. This Week’s Deep Dive

In the world of antibodies, every interaction between an antigen and antibody can be considered as a lock-and-key mechanism in all simplicity. Consequently, the structure of the antibody is extremely important, that dictates its functionality. Just to appreciate the exquisiteness of antibody creation: there are about ~1500 pathogen species (disease causing species) all around us and a massive amount of sub-species as well. But, just with the 6 CDR loops on the antibody, our immune system is able to bind and attack this wide range of pathogens. It all comes down to the elegant structure of antibodies with this diversity in action created by the process of homologous recombination. Check out this article to learn more on the elegant structure of antibodies (my first article on Substack!).

3. Bench Notes

SDS-PAGE gels are a routine part of any molecular biology lab. Every student runs them at some point, and a sharp band at the right position can genuinely make your day. But for labs that cast their own gels, the process can be tedious. Nothing is more frustrating than a leaking gel right as you’re about to load your protein samples.

Here’s a tip a labmate passed on to me: pour a thin plug layer before your resolving layer. The plug is roughly a 50% acrylamide solution and solidifies within seconds. It was an absolute game-changer for making leakproof gels. Some labs do buy ready-made gels, which can be quite pricey. However, even for those labs, casting gels in-house is a more economical option especially when you’re playing around with different experimental conditions (and gel compositions).

Share this tip with the PhD students in your life and help them bid adieu to leaky SDS-PAGE gels.

4. Antibody Spotlight

If you’ve been following the news, Ebola is in the news again as of May 2026. Ebola in humans is primarily caused by 4 orthoebolaviruses namely Zaire ebolavirus (EBOV), Sudan ebolavirus (SUDV), Taï Forest ebolavirus (TAFV), and Bundibugyo ebolavirus (BDBV). On May 16, 2026, the World Health Organization has determined that the Ebola disease caused specifically by the Bundibugyo virus in the Democratic Republic of the Congo and Uganda as public health emergency of international concern.

We currently have 2 FDA-approved monoclonal antibodies (mAb) to combat the Zaire ebolavirus: Inmazeb (REGN-EB3) and Ebanga (mAb114). The main question is: will the existing monoclonal antibodies work for the recent Ebola outbreak in the DRC and Uganda which is the Bundibugyo virus?

When we look at the structure of EBOV, it has a glycoprotein (GP) on its surface whose main function is to bind the receptors on a cell surface, thereby allowing the RNA genome to get inside. If we can stop entry, we can stop infection, right? Inmazeb (REGN-EB3) contains a mixture of 3 mAbs atoltivimab, maftivimab, and odesivimab-ebgn that binds non-overlapping epitopes of the glycan cap domains in GP1, receptor binding region for the host receptor, and internal fusion loop, respectively. Ebanga (mAb114) contains ansuvimab and its epitope specificity spans the receptor binding region. Thus, use of these mAbs results in the inhibition of the binding of the virus to the host (humans) receptor NPC1-C (Rayaprolu et al., 2023; Rijal et al., 2023).

So, why can’t we use the same monoclonal antibodies against the Bundibugyo virus? I was curious about this too. I ran an Multiple Sequence Alignment to compare the amino acid sequence of the glycoproteins of EBOV and BDBV and it showed only a 67.07% identity. Sadly, that’s an average identity between the glycoproteins to expect these FDA-approved mAbs to neutralize the Bundibugyo virus as well. This underscores the need to develop broad-spectrum mAbs capable of acting on different members of the same virus family.

The May 2026 Ebola outbreak is an evolving story. For most accurate information, always check out authentic sources only: WHO, African CDC, and National Health Authorities. I’d recommend epidemiologists Jessica Malaty Rivera, MS and Katelyn Jetelina, and virologist Dr. Angela Rasmussen here on Substack.

Also, please don’t spread misinformation.
That’s probably the bigger epidemic that scientists are trying to fight!!!

5. In Silico

There is a lot of buzz about Artificial Intelligence (AI) in every field. In protein and antibody development, AlphaFold is already a game changer in structural predictions of proteins. I was reading a recent interview with Pushmeet Kohli, VP of Science and Strategic Initiatives at Google DeepMind. AlphaFold, AlphaGenome, AI Co-Scientist are some of the things that he has worked on, with so much more AI-for-science initiatives under his portfolio. How DeepMind decides which science problems to solve was an interesting insight for me. Read this interview by Pablo Lubroth, a fellow Substacker.

Decoding Science

The Three Forms of Scientific Intelligence: A Conversation with DeepMind's Pushmeet Kohli

Welcome to Decoding Science: every other week our writing collective highlight notable news—from the latest scientific papers to the latest funding rounds in AI for Science —and everything in between. All in one place…

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a month ago · 32 likes · Pablo Lubroth

6. Antibody Term of the Week

Just felt like giving my readers a little extra this week. Let’s build our antibody vocabulary together. Going back to basics, what’s the difference between polyclonal and monoclonal antibodies?

An antigen contains different segments called epitopes (the colored triangles), which are sites that antibodies can target. Polyclonal antibodies are a mixture of antibodies that can bind different epitopes of the same antigen. Monoclonal antibodies, on the other hand, target exactly one kind of epitope on the antigen.

This site is free and I intend to keep it that way for as long as I can because good science communication should be accessible, especially for students. If you enjoy what you read and feel like buying me a coffee along the way, I won't say no. ☕ And if you're not a student, consider paying it forward to one who is.

Buy me a coffee ☕️

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Off late, I have been rewiring myself to be more actionable. Less talk, more outcomes. My past default would be to read tons of blogs and watch YouTube videos about productivity, and not really get anything done. Hence before I even figured out every single in-and-out of Substack, I went ahead and posted my first article on March 26, 2026 on the elegant structure of Antibodies. Thereafter, I kept coming back to the platform to see what else is out there. In just a month and a half, I have fallen in love with the science community on Substack.

It’s these scientists who made me think about ‘The Binding Site’ and what I want it to stand for. At this stage, I know I want ‘The Binding Site’ to be a place where I can translate my antibody expertise into practical knowledge for the next generation of researchers. Having just finished my PhD and getting into the professional world, I have a steep learning curve ahead of me. As I learn new things, I’d like to take my readers too on that learning journey with me, and pick up more experienced researchers on the way to collectively learn and grow in this field.

To that end (and to celebrate my 100 subscribers), it only felt only appropriate to launch my newsletter: The Binding Brief.

The Binding Brief, a weekly newsletter will drop every Wednesday in your inbox and will have a lot of scoop and sections about the world of antibodies.

1. Raw Data

Scientists are also humans and we often forget that. We are ‘raw data’ versions who come with feelings, emotions, thoughts, reflections, and so much more than just the papers we put out. The newsletter will start off with a personal musing.

2. This Week’s Deep Dive

Worry not, I will not stop writing the long form content that I do. I loved writing the piece on how antibodies get their names, and such informative pieces shall continue. I learn a lot while I research for the writing and it would still remain the bread and butter of this site.

3. Bench Notes

During the course of my PhD, I had lab mates introduce me to so many research protocols — the experiments that actually give us answers. I would love to be your digital lab mate giving you protocols and tips on how to carry out a science experiment from start to end, along with annotated cheat sheets.

4. Antibody Spotlight

Antibodies are forever in journal articles and news now! So much new and exciting work is being carried out, and I'd love to curate some of the most interesting antibodies of the week for you and explain why they're worth watching.

5. In Silico

In this world of Generative AI, we have a parallel stream of antibody development happening. It is still quite early to tell the impact this field will have on speeding up antibody and drug development, but if it does, I'd love for my readers to be up to date on all this information.

6. Paper Worth Reading

There have been some cool papers associated with the field — true game changers that changed the course of our thinking and pivoted the course of research and discovery. This section will feature my favourite reads of the week.

That's it from me for now. I shall add in more sections as they come along.

This site is free and I intend to keep it that way for as long as I can because good science communication should be accessible, especially for students. If you enjoy what you read and feel like buying me a coffee along the way, I won't say no. ☕ And if you're not a student, consider paying it forward to one who is.

Buy me a coffee ☕️

Subscribe now