Colin Mann

🌲St.Paul🌲✈️ 🌴 San Diego🌴 ✈️ 🏛️Boston🏛️
Harvard Virology PhD Candidate - Abraham Lab - Structural Virology & Antibodies

[Colin Mann @TheColinMann]

So glad to finally present our work on JUNV and MACV GPC in @NatureMicrobiol! Thankful for the contributions of all our co-authors, including collaborators @florian_krammer and Yuemin Bian. And special thanks to @HC2EM for their help with the Cryo-EM!

[@AbrahamLabHMS @abrahamlabhms]

Excited to share our new @NatureMicrobiol paper revealing the most complete arenavirus glycoprotein complex (GPC) structures to date 🤩@TheColinMann @HarvardMicro @VirologyHarvard https://t.co/ab7N7ux5Rc

[Colin Mann @TheColinMann]

@QueenDeejahh @By_CJewett @nytimes The bar IS insanely high.

[Colin Mann @TheColinMann]

@Abhinavsns This is actually pretty close to my experience too and I've really enjoyed it.

[@AbrahamLabHMS @abrahamlabhms]

We are excited to share our new @CellCellPress paper revealing mechanisms of action of promising drugs against the helicase–primase of herpes simplex virus 1 (HSV-1) 🤩@HarvardMicro https://t.co/gI9eIKPeEL

[Colin Mann @TheColinMann]

@jrkelly @JChengWSJ To do what though? I don't see how it's cost effective or feasible for most basic or semi-translational labs. Where would you put the automation? Pipetting? I feel the cost and maintenance of tech will be a complete waste and you loose both the humanity and creativity

[Colin Mann @TheColinMann]

This is cool. Instead of getting a 99% success rate and 100 colonies per plate, I can get functionally the same thing by spending three times the effort, using more of an expensive reagent, and outsourcing basic math to an AI.

[Niko McCarty. @NikoMcCarty]

There's a recent blog from @OpenAI where they used GPT-5 to optimize a common biology experiment, called Gibson Assembly. I've seen criticisms online from people who say things like, "Who cares? A human totally could have done that" or whatever. And that's true. But I still think this blog is nice for a couple reasons.

First, faster iterations / more reliable experiments is one of the best ways to accelerate biotechnology progress more broadly! Experiments take much too long, and are often much too unreliable, for scientists to move quickly. Therefore, we should invest more resources toward optimizing and improving common methods that seem "mundane".

Second, this is a simple experimental system in which to test AI; indeed, that's the whole point! Gibson Assembly has been around for nearly two decades, is widely-used, and only requires three enzymes. It is therefore a natural fit for AI companies to benchmark their models on biological questions. (The parameter space is not too large!)

To understand what OpenAI actually did, I first need to tell you about Gibson Assembly, a common method biologists use to stitch DNA molecules together. Originally developed in 2009, most scientists use Gibson because it's dead simple: Everything works at one temperature (50°C) and it requires only three enzymes. The DNA molecules to be joined together are designed such that they have 15-40 nucleotides, at either end, which overlaps with the other DNA molecule. All the DNA is then added to a tube and an enzyme, exonuclease, "chews back" several dozen nucleotides from the 5' ends of each molecule, leaving behind long single-stranded "arms." These arms float around in the liquid, collide with a matching arm in another DNA sequence, and hug each other tightly. A second enzyme, DNA polymerase, runs along these touching DNA strands and fills in parts of the arms that don't overlap or are still single-stranded. Finally, DNA ligase seals the "nick" and heals the strands, thus forming a newly assembled, double-stranded piece of DNA.

OpenAI collaborated with a new biosecurity startup, Red Queen Bio (co-founded by Hannu Rajaniemi, an excellent science fiction writer), to build the evaluation framework. The metric they settled on is called cloning efficiency, which just means this: For a fixed amount of input DNA (like one picogram) transformed into cells, how many colonies successfully grow and contain the correctly assembled DNA molecule? By the end of their blog post, the OpenAI team claims that they were able to boost this number 79x relative to a "baseline protocol" from New England Biolabs, or NEB, a common purveyor of the Gibson enzymes.

An important note is that OpenAI says no humans were involved in optimizing the reaction; all the humans did was carry out protocols generated by GPT-5, and also upload experimental results back into the model. They repeated this several times, coaxing the model to iterate each time. Their Gibson Assembly was remarkably simple, involving just two DNA molecules: a gene encoding a fluorescent protein and a plasmid to hold the gene.

(The OpenAI team, intriguingly, also set up a set up a robot to automate the Gibson Assembly and transformation, but couldn't get it to work as well as a human. "We compared the robot's work to human-performed experiments at each step. The robot successfully handled the transformation process…When compared directly with human-performed transformations, the robot generated similar quality data with equivalent improvements over baseline, showing early potential for automating and accelerating biological experiment optimization." However. "while the fold-changes between the robot and human experiments were similar, absolute colony counts from the robot were approximately ten-fold lower than manual execution.")

After several rounds of iteration, the model made two notable proposals:

First, it added two additional enzymes to the normal Gibson Assembly reaction. Specifically, it added "the recombinase RecA from E. coli, and phage T4 gene 32 single-stranded DNA–binding protein (gp32)." The blog continues: "Working in tandem, gp32 smooths and detangles the loose DNA ends, and RecA then guides each strand to its correct match." This tweak improved the "cloning efficiency" metric by 14x over the standard NEB protocol.

Second, it made a subtle change to how the assembled DNA molecules were inserted into living cells. Specifically, GPT-5 told the humans to spin down cells in a centrifuge, thus forming a pellet, prior to transforming them. This is typically not recommended because competent cells are "fragile," but the OpenAI team writes that "the cells tolerated concentration well and the increased molecular collisions boosted transformation efficiency substantially (>30-fold on final validation)."

Now, recall that at the start of this little blog I said I really liked this experiment! (Do not crucify me, ye AI optimists.) But no internet commentary is truly complete without some nitpicking, so here goes.

One criticism is that the largest improvement made by the model was not related to Gibson Assembly at all! It was related to how the DNA gets delivered into cells. And, indeed, prior studies have shown something similar. (This research paper, for example, says that one of the best ways to improve transformation is to concentrate cells beforehand. Fair play to the OpenAI team for linking to this in their blog post.) And if you are a human reading this blog, and you are planning to spin down your competent cells before transformation, just be sure to aliquot everything into small tubes first; repeated spins will, over time, kill everything.

Another issue is that adding RecA and gp32 to a Gibson Assembly reaction complicates things quite a bit. For a normal Gibson reaction, everything comes in a single kit from NEB with the enzymes, and the whole experiment is done at one temperature: 50°C! But doing a Gibson Assembly this way would require one to buy purified RecA and gp32, and also change incubation temperatures to get everything working (RecA and gp32 work best at 37°C.) This is more expensive and more complicated, but maybe worthwhile in some cases.

And lastly, the selected metric — namely, how many colonies one gets from a given amount of DNA — doesn't actually seem all that useful in most scenarios. A scientist stitching together two strands of DNA doesn't actually care if they only get five colonies because, often, they only need to get ONE colony that works, and then they can grow up those cells in large beakers and extract a huge amount of the plasmid. A more useful metric might be to increase the total number of unique DNA strands that can be joined together in a single Gibson Assembly reaction, without reducing overall quality, instead.

Still, I liked this blog post as a whole. I'm glad people are optimizing the "small" things, and I don't blame OpenAI for not trying to solve cancer, in its overwhelming magnitude of manifestations, on their first attempt! Gibson Assembly is a much better starting point.

[Colin Mann @TheColinMann]

I was bored in CC and the shiny allure of working in a fried chicken restaurant began to tarnish, so I applied for basically any med sci related job in the area. Got one single interview, at TSRI (throwback), fell in love with vaccine design, and stayed in the space for 10 years.

[Afolabi Sokeye 🧱 @SokeyeA]

What’s the lore behind choosing your career path ?

[Colin Mann @TheColinMann]

@mbeisen This is blown out of proportion a bit. UCSD has some outstanding students. may be a bit biased as an alum but I think broad judgement by math classes is a bit callous. With the pace of their system, a lot take remedial classes to reset into that pace before taking "real" classes.

[Colin Mann @TheColinMann]

@Jethawkgames @PirateSoftware @Bucky_cm His point is that you did not invest. When you give money to a kickstarter you are not gaining equity (it's not shark tank). You aren't even guaranteed an "alpha". I'd argue a kickstarter that delivers on their initial goal and expands the scope of the product is successful.

[Colin Mann @TheColinMann]

@NotoriousPeach Virology... All of science really but especially virology

[Colin Mann @TheColinMann]

@KateShemirani As a protein biochemist I honestly don't even know where to start with this one

[Colin Mann @TheColinMann]

@OttoKurect @jboysen0 I'm not sure what you're saying. Does AF3 not do sequence alignment and compare with PDB for structural homology?

[Colin Mann @TheColinMann]

@baym I was hoping to find someone who worked over there (I'm in VSC) what type of research is based on that floor?

[Colin Mann @TheColinMann]

@GamewithDave

[Colin Mann @TheColinMann]

@gebsaar @pdhsu @Harvard Most is earmarked

[Colin Mann @TheColinMann]

@lizbwood @llallawg Thats great. In the near future will we be able to order geneblocks for pennies on the dollar then?

[Colin Mann @TheColinMann]

@lizbwood @llallawg Safe-in-human as in just human vs humanized? So, it's essentially an AI-driven in silico yeast/phage display to identical primary candidates for in vitro evaluation?

[Colin Mann @TheColinMann]

@lizbwood @llallawg From what you posted my understanding is that your eliciting antibodies against TAAs being presented natively on HLAs as a form of targeted therapeutic, similar to the TCR of say a CAR-T cell, yes.

[Colin Mann @TheColinMann]

@lizbwood @llallawg Would this not limit your antigen to short peptides? If many great targets are complexes or "have complex biology", how do you capture their quaternary antigenicity through peptide presentation? Is there concern with lack of immunogenicity or Self reactivity due to the HLA?

[Judith Aberle @AberleJudith]

Unsere Studie zu FSME Erkrankungen in Österreich (2000-2024) wurde jetzt in “Vaccine“ veröffentlicht
Die Impfung verhinderte in Österreich mehr als 10.000 Hospitalierungen und > 4.000 schwere Enzephalitisfälle

https://t.co/8DKtfFRbUo https://t.co/oOzLrM9jOP