Str2Str: A Score-based Framework for Zero-shot Protein Conformation Sampling

Published: 16 Jan 2024, Last Modified: 11 Mar 2024ICLR 2024 posterEveryoneRevisionsBibTeX
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Keywords: proteins, conformational sampling, diffusion models, score-based models, generative modeling, equivariant network
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TL;DR: We built a score-based protein conformation sampling method via equivariant structure-to-structure translation with no reliance of simulation data
Abstract: The dynamic nature of proteins is crucial for determining their biological functions and properties, for which Monte Carlo (MC) and molecular dynamics (MD) simulations stand as predominant tools to study such phenomena. By utilizing empirically derived force fields, MC or MD simulations explore the conformational space through numerically evolving the system via Markov chain or Newtonian mechanics. However, the high-energy barrier of the force fields can hamper the exploration of both methods by the rare event, resulting in inadequately sampled ensemble without exhaustive running. Existing learning-based approaches perform direct sampling yet heavily rely on target-specific simulation data for training, which suffers from high data acquisition cost and poor generalizability. Inspired by simulated annealing, we propose Str2Str, a novel structure-to-structure translation framework capable of zero-shot conformation sampling with roto-translation equivariant property. Our method leverages an amortized denoising score matching objective trained on general crystal structures and has no reliance on simulation data during both training and inference. Experimental results across several benchmarking protein systems demonstrate that Str2Str outperforms previous state-of-the-art generative structure prediction models and can be orders of magnitude faster compared with long MD simulations.
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Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
Submission Number: 3973