Excited to share our now published paper @cp-cell.bsky.social highlighting advances in modeling the evolution of protein-protein interactions with MSA Pairformer. Big thanks to Zhidian Zhang, Olivia Tang, @eunbelivable.bsky.social @milot.bsky.social @martinsteinegger.bsky.social and @sokrypton.org!

Expanding the scope of protein language modeling to protein-protein interactions with MSA Pairformer

Protein language models have excelled at modeling individual proteins, but extending these capabilities to protein complexes remains a major challenge. MSA Pairformer, a parameter-efficient protein la...

cell.com

🍹Long weekend Project: Since Claude Fable is banned for Science, I thought it might be fun to see if it can be used for something less scientific. 😎 Introducing Age of Epochs! ⚔️ An attempted reproduction of Age of Empires II in Javascript. ageofepochs.com (1/4)

New Experimental Google Colab Notebook now integrates AlphaFold3 with OpenFold3 and py2Dmol: colab.research.google.com/github/sokry... (1/3)

🚀 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.

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/

Harris Wang@harriswang.bsky.social · 3mo ago

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/... 🦠🧬🛠️🖥️💥

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/... 🦠🧬🛠️🖥️💥

Toward life with a 19–amino acid alphabet through generative artificial intelligence design

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

I'm excited to announce some major updates to our ProteinEBM paper with Chenxi Ou @sokrypton.org!

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...

AF2BIND: predicting small-molecule binding sites using the pair representation of AlphaFold2

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,...

rdcu.be

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🧵👇

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.

jproney@jproney.bsky.social · 8mo ago

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.

An energy-based model of protein conformational space can be used to predict structure from sequence, sample from the conformational landscape, rank structures, and predict mutation effects. @sokrypton.org www.biorxiv.org/content/10.6...

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.

An interesting study from @aidenkzj.bsky.social, @abulnaga.bsky.social and @sokrypton.org that builds on our Progres model to find pairs of proteins with circular permutations: www.biorxiv.org/content/10.1...

CIRPIN: Learning Circular Permutation-Invariant Representations to Uncover Putative Protein Homologs

Protein structure-based homology detection has been revolutionized by deep learning methods that can rapidly search massive databases. However, current structural search tools often miss proteins rela...

biorxiv.org

Thrilled to share that the final piece of my PhD work is now on bioRxiv! biorxiv.org/content/10.1... With support from @nvidia and the @NSF, we used AlphaFold to screen 1.6M+ protein pairs, revealing thousands of potential novel PPIs. All data can be viewed at predictomes.org/hp

Proteome-wide in silico screening for human protein-protein interactions

Protein-protein interactions (PPIs) drive virtually all biological processes, yet most PPIs have not been identified and even more remain structurally unresolved. We developed a two-step computational...

biorxiv.org

A few py2Dmol updates 🧬 py2dmol.solab.org Integration with AlphaFoldDB (will auto fetch results). Drag and drop results from AF3-server or ColabFold for interactive experience! (1/4)

Is 3D dragging you down? Wish you could instead use the 2D ColabFold representation for all your work? 🤓 Introducing: py2Dmol 🧬 (feedback, suggestions, requests are welcome)

Working on the protein-hunter-chai google colab notebook. 😈 @yehlincho.bsky.social

Will it bind? A little worried about all the "TTTTTTT" 🧐 But looks cool 😎

Thrilled to announce our new preprint, “Protein Hunter: Exploiting Structure Hallucination within Diffusion for Protein Design,” in collaboration with @Griffin, @GBhardwaj8 and @sokrypton.org 🧬Code and notebooks will be released by the end of this week. 🎧Golden- Kpop Demon Hunters

Looks like someone has already tried to replace me with an AI agent 🫣

Exciting to see our protein binder design pipeline BindCraft published in its final form in @Nature ! This has been an amazing collaborative effort with Lennart, Christian, @sokrypton.org, Bruno and many other amazing lab members and collaborators. www.nature.com/articles/s41...

Now that OpenCRISPR is in nature and rekindled the 'what's-a-novel-sequence' debate, I'm happy to share an app to check this, which I built for fun some time ago. fuerstlab.shinyapps.io/SeqNovelty/ quick 🧵

Diego del Alamo@delalamo.xyz · last yr.

Apparently this tendency of PLMs to cut and paste across multiple sequnces during de novo design mimics what we see in natural sequences. Not sure what to make of it yet

MMseqs2 v18 is out - SIMD FW/BW alignment (preprint soon!) - Sub. Mat. λ calculator by Eric Dawson - Faster ARM SW by Alexander Nesterovskiy - MSA-Pairformer’s proximity-based pairing for multimer prediction (www.biorxiv.org/content/10.1...; avail. in ColabFold API) 💾 github.com/soedinglab/M... & 🐍

Excited to re-share work from @yoakiyama.bsky.social and Zhidian Zhang on MSA pairformer. (1/4)

Yo Akiyama@yoakiyama.bsky.social · last yr.

Excited to share work with Zhidian Zhang, @milot.bsky.social, @martinsteinegger.bsky.social, and @sokrypton.org biorxiv.org/content/10.1... TLDR: We introduce MSA Pairformer, a 111M parameter protein language model that challenges the scaling paradigm in self-supervised protein language modeling🧵

Excited to share work with Zhidian Zhang, @milot.bsky.social, @martinsteinegger.bsky.social, and @sokrypton.org biorxiv.org/content/10.1... TLDR: We introduce MSA Pairformer, a 111M parameter protein language model that challenges the scaling paradigm in self-supervised protein language modeling🧵

Scaling down protein language modeling with MSA Pairformer

Recent efforts in protein language modeling have focused on scaling single-sequence models and their training data, requiring vast compute resources that limit accessibility. Although models that use ...

biorxiv.org

looks cool but they should really have cited and compared to gLM and gLM2, which are very conceptually similar: www.nature.com/articles/s41... www.biorxiv.org/content/10.1... I'll leave a biorxiv comment for the authors. It's hard to find all prior literature but this one is kinda an oof.

Genomic language model predicts protein co-regulation and function - Nature Communications

A gene’s function is governed by its sequence, structure and context. Here, the authors develop a genomic language model that learns contextualized functional representations from diverse and large-sc...

nature.com

Kevin K. Yang 楊凱筌@kevinkaichuang.bsky.social · last yr.

- Model bacterial genomes as sequences of proteins - predict protein-protein interactions, operon structure, and protein function - infer phenotypic traits - design synthetic genomes with desired properties @macwiatrak.bsky.social @mariabrbic.bsky.social @andresfloto.bsky.social

PSA = Google Colab Pro free for one year for academic use (US only): blog.google/outreach-ini...

New Google Colab features for higher education

Google Colab offers free Colab Pro for students, interactive slideshows and AI controls in notebooks.

blog.google

Alright, last one, now I need to get back to real work... 😸

How metamorphic proteins change conformation. 😸

Molecular cat tweezers? 😺