Scientist, Assistant Professor at MIT biology, #FirstGen
Sergey Ovchinnikov
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Finally, the code for anyone wanting to run alphafold3 locally with openfold3 weights (including convert script) can be found here:
github.com/sokrypton/al...
To get OpenFold3 (pytorch weights) working in AlphaFold3 (jax code base) was not super easy. Required an entire weekend and $40 of Claude Code credit. See breakdown of what had to change in AF3 code base to get OF3 weights to work: github.com/sokrypton/al...
Commit:
github.com/sokrypton/al...
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New preprint🚨
Imagine (re)designing a protein via inverse folding. AF2 predicts the designed sequence to a structure with pLDDT 94 & you get 1.8 Å RMSD to the input. Perfect design?
What if I told u that the structure has 4 solvent-exposed Trp and 3 Pro where a Gly should be?
Why to be wary🧵👇
Our paper with @sokrypton.org using AlphaFold2 to predict small-molecule binding sites in proteins is now out in Nature Methods! 🧵
rdcu.be/e7SnX
www.nature.com/articles/s41...
1/ Excited to share our new paper in Science @science.org: “Toward life with a 19-amino acid alphabet through generative artificial intelligence design.” A great collab w/ Sergey's group @sokrypton.org at MIT
@columbiasysbio.bsky.social science.org/doi/10.1126/... 🦠🧬🛠️🖥️💥
As a bonus, here's a video of ProteinEBM folding up the fast-folder NTL9, rendered in stunning 2D by py2Dmol from @sokrypton.org! We hope models like ProteinEBM can serve as a step toward solving the "real" protein folding problem.
🚀 Excited to share our new work: Absolute Stability Predictor!
📊: forms.gle/4ZnXZSnTBvay...
Built the MGnify Stability Dataset (1.8M+ measurements) and developed stability prediction models, together with
@grocklin.bsky.social @KotaroTsuboyama, @sokrypton.org and teams.
I'm excited to announce some major updates to our ProteinEBM paper with Chenxi Ou @sokrypton.org!
New Experimental Google Colab Notebook now integrates AlphaFold3 with OpenFold3 and py2Dmol:
colab.research.google.com/github/sokry...
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Sergey Ovchinnikov
Sergey Ovchinnikov
Yours truly is a proper scientist now!
TL;DR: we used AI to redesign parts of essential cell machinery with only 19 canonical amino acids instead of 20.
Why? Great thread by @harriswang.bsky.social provides more context and details.
Let me talk a bit about the AI design part of this. 1/
Because all known living organisms are made from at least 20 canonical amino acids, the feasibility of life using a more simplified alphabet remains unclear. In this work, we leveraged computational d...
science.org
Video
Max Fürst
AlphaFold 3 inference pipeline. Contribute to sokrypton/alphafold3 development by creating an account on GitHub.
Nature Methods - AF2BIND is a logistic regression model trained on AlphaFold2 pair features to predict small-molecule binding-site residues in proteins, without multiple sequence alignments,...
1/ Excited to share our new paper in Science @science.org: “Toward life with a 19-amino acid alphabet through generative artificial intelligence design.” A great collab w/ Sergey's group @sokrypton.org at MIT
@columbiasysbio.bsky.social science.org/doi/10.1126/... 🦠🧬🛠️🖥️💥
I'm super excited to announce the first preprint of my PhD, together with Chenxi Ou and @sokrypton.org!
ML has revolutionized protein modeling, but crucial challenges remain. For example, we can't reliably predict complicated protein structures without MSAs, which limits what we can design.
