JAMUN: Transferable Molecular Conformational Ensemble Generation with Walk-Jump Sampling

Ameya Daigavane, Bodhi P. Vani, Joseph Kleinhenz, Joshua A Rackers

Keywords: transferable, conformation, ensembles, 3D structure, equivariance, sampling, proteins

TL;DR: We present JAMUN, a new model for generating conformational ensembles for small proteins at rapid rates.

Abstract:

Conformational ensembles of protein structures are immensely important to understanding protein function. Current techniques for sampling ensembles are computationally inefficient, or do not transfer to systems outside their training data. We present walk-Jump Accelerated Molecular ensembles with Universal Noise (JAMUN), a step towards the goal of efficiently sampling the Boltzmann distribution of arbitrary proteins. By extending Walk-Jump Sampling to point clouds, JAMUN enables ensemble generation at orders of magnitude faster rates than traditional molecular dynamics or state-of-the-art generators. Further, JAMUN is able to predict the stable basins of small peptides that were not seen during training.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Submission Number: 11228