Super excited to share a new preprint from our lab on design of small-molecule binding proteins using neural networks! The paper has a bit of everything. A new graph neural network, new design algorithms, and experimental validation. www.biorxiv.org/content/10.1... 🧵🧪 - ThreadSky

nickpolizzi.bsky.social • 4 days ago

Super excited to share a new preprint from our lab on design of small-molecule binding proteins using neural networks! The paper has a bit of everything. A new graph neural network, new design algorithms, and experimental validation. https://www.biorxiv.org/content/10.1101/2025.04.22.649862v1
🧵🧪

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nickpolizzi.bsky.social•4 days ago

Ben Fry (@benf549.bsky.social) was excited when proteinMPNN came out, which motivated him to train a new gNN called LASErMPNN to design sequences given protein-ligand co-structure. LASErMPNN does pretty well at this! The repo is available and even has the training code! https://github.com/polizzilab/LASErMPNN

nickpolizzi.bsky.social•4 days ago

Ben used LASErMPNN in combination with a protein-ligand co-structure predictor, RFAA, in an iterative algorithm called NISE that refines designs. NISE optimizes the sequence, structure, and ligand conformer together to improve the confidence of both models. It's a neural-network-only algorithm

nickpolizzi.bsky.social•4 days ago

We all know in protein design about the goal of self consistency. That is, we want the predicted structure to look like the structure for which we designed the sequence.

nickpolizzi.bsky.social•4 days ago

With the new co-structure predictors like RFAA, Boltz-1, and AF3, we can now extend self-consistency into the ligand dimension. And Ben's NISE algorithm maximizes this. Code repo here: https://github.com/polizzilab/NISE

nickpolizzi.bsky.social•4 days ago

Ben used NISE and LASErMPNN to design binders to exatecan, an anticancer drug prone to inactivation by hydrolysis. We also used a more "traditional" approach using COMBS and Rosetta to design binders. We could compare the methods head to head.

nickpolizzi.bsky.social•4 days ago

Kaia Slaw (no bluesky) experimentally tested 4 designs from NISE and 16 from COMBS. All 4 NISE designs bound! The highest affinity binder- which Ben and Kaia call "EPIC" - was pretty tight (0.1 uM Kd). Compared to COMBS (3 of 16 bound, tightest was 10 uM), NISE and LASErMPNN did a much better job!

anush-chp.bsky.social•4 days ago

Congratulations!! Very nice!

nickpolizzi.bsky.social•4 days ago

thanks, Anush!

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