Su Datt Lam

Modeling protein-small molecule conformational ensembles with ChemNet @UWproteindesign 🚀 New preprint from David Baker!🚀 - ChemNet is a breakthrough deep learning model that accurately predicts the conformational ensembles of small molecules in complex with proteins. - It outperforms existing methods like AlphaFold and RoseTTAFold by focusing on atomic-level accuracy, generating ensembles rather than single static structures. - The key innovation is ChemNet’s stochastic nature, which allows it to model conformational heterogeneity, making it ideal for protein-small molecule docking. - ChemNet generates diverse structures based on random initializations, providing deep insights into the flexibility of molecules at binding sites. - The model is designed to assess the pre-organization of catalytic sites in enzymes, which is crucial for improving enzyme activity. - In enzyme design applications, ChemNet-guided designs have achieved impressive success rates, including a retroaldolase with a kcat/KM of 11,000 M-1min-1, the highest pre-deep learning design rate for this reaction. - The ability to rapidly generate conformational ensembles makes ChemNet a valuable tool for drug discovery and computational enzyme design. - This system provides a faster, more generalizable alternative to computationally expensive methods like molecular dynamics simulations. - ChemNet is expected to have broad applications in predicting protein-small molecule interactions, and guiding de novo design of enzymes and drug binders. @ikalvet @gyurielee @alchemist_an @JustasDauparas 📜Paper: biorxiv.org/content/10.1101/…