In v0.3.0, we solve *both* of these issues, enabling efficient inference on both personal computers and multi-GPU HPC systems. The secret? Our new Blocked Multi-GPU Parallel Inference (BMPI) procedure, led by Daniel Schaffer (github.com/schafferde).

Our key hypothesis is that these motions carry information about allosteric networks, despite not explicitly measuring larger conformational change. Inspired by terrific work from Federica Maschietto, we show that in silico predictions of these networks closely matches DMS results in KRAS.

And we think we can! Thanks to excellent data curation efforts from @hkws.bsky.social, we show that RocketSHP-predicted fluctuations correlate well with experimental hetNOE measurements that capture fast + local movement.

I constantly reference their Supp. Fig. 3 when I’m trying to describe the method to people— maybe not exactly what you’re looking for but imo extremely informative.

Finally, we show how users can use MINT through two case studies:

MINT predictions align with 23/24 experimentally validated oncogenic PPIs impacted by cancer mutations, and MINT estimates SARS-CoV-2 antibody cross-neutralization with high accuracy.

We show MINT works for diverse and challenging interaction tasks!

It outperforms IgBert & IgT5 in predicting antibody binding affinity and estimating antibody expression.

Fine-tuning MINT beats TITAN, PISTE and other TCR-specific models on
TCR–Epitope and TCR–Epitope–MHC interaction prediction.

MINT sets new benchmarks!

It outperforms existing PLMs in:

✅ Binary PPI classification

✅ Binding affinity prediction

✅ Mutational impact assessment

Across yeast, human, & complex PPIs, we see up to 29% gains vs. baselines! 📈

MINT is built on ESM-2 but adds a cross-chain attention mechanism to preserve inter-sequence relationships.

We trained MINT on 96 million high-quality PPIs (from STRING-db). Instead of masked language modeling on single sequences, we now capture interaction-specific signals.

Traditional PLMs struggle with PPIs since they model proteins independently. Previous approaches concatenated embeddings or sequences—leading to lost inter-residue context. We fix this with 🌿 MINT, which allows multiple interacting sequences as input.

In my personal favorite figure, we annotate a diverse set of ~3.5 million sequences across kingdoms to see the distribution of symmetry use across life -- another example of the cool things you can do with PLM fine-tuning + lightweight classification + genome scale inference!

I'm at @biophysicalsoc.bsky.social #BPS2025 with a bunch of folks from the Structural and Molecular Biophysics @flatironinstitute.org group-- come say hi and check out our posters/talks! @sonyahanson.bsky.social @pilarcossio.bsky.social @miroastore.bsky.social

If you're at @neuripsconf.bsky.social @workshopmlsb.bsky.social tomorrow, make sure you stop by our poster presenting a paired-protein language model for modeling protein-protein interactions. Varun did a great job spearheading this work! #NeurIPS2024