Jinwoo Leem Profile picture
Jun 6 5 tweets 3 min read
Exciting work from @KathyYWei1, @AmeyaHarmalkar & @proteinrosh on predicting scFv developability. TLDR: transformers and CNNs can potentially help prioritise mutations sites for enhancing stability (1/5) #antibodies #machinelearning #proteineng
Context: a single-chain Fv (scFv) is an antibody construct whose heavy and light chains are linked. It's not the conventional "Y" shape molecule, and is useful for engineering / phage display, etc. See @AlissaHummer's post blopig.com/blog/2021/07/a… (2/5)
Thermostability (measured by TS50, the temperature when scFv loses binding) is weakly predicted by 0-shot and fine-tuning via transformers (ESM-1v + ESM-1b). CNNs using sequence and structural (energy) convolutions perform better (?) [hard to tell, sorry!🙈] (3/5)
The crazy result in my mind is how both models did a good job at picking out the positions that enhance thermostability. The correct mutation isn't always predicted, but this can dramatically reduce the search space. Could be a game changer. (4/5)
Several Qs remain: which germlines were predicted better? Were they the ones naturally stable to begin with? Where are the ROC curves for the transformer models? Will the models perform better for IgGs vs scFv? Will CNNs increasingly replace LMs? (5/5)

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More from @ideasbyjin

May 26
A "negative" result, but phenomenal thought piece from @naturalantibody / @antibodymap. TLDR predicting antibody-antigen interactions is pretty darn hard (1/5) #antibodies #machinelearning #alphafold

naturalantibody.com/use-case/deepm…
Predicting Ab-Ag interactions is a sub-problem of the protein-protein interaction problem. There are many facets to consider here, including but not limited to, identifying the correct antigen (let alone the correct epitope), the correct paratope, orientation, etc (2/5)
@antibodymap's team show first that true Ab-Ag pairs (i.e. those where we know the Ab binds antigen) and false Ab-Ag pairs (i.e. Ag was randomly given to an Ab), the pIDDT scores are incomparable, suggesting score-based discrimination is HARD. (3/5)
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