AlphaFold Meets Flow Matching for Generating Protein Ensembles

• URL: http://arxiv.org/abs/2402.04845v2
• Date: Mon, 2 Sep 2024 22:43:33 GMT
• Title: AlphaFold Meets Flow Matching for Generating Protein Ensembles
• Authors: Bowen Jing, Bonnie Berger, Tommi Jaakkola,
• Abstract summary: We develop a flow-based generative modeling approach for learning and sampling the conformational landscapes of proteins. Our method provides a superior combination of precision and diversity compared to AlphaFold with MSA subsampling. Our method can diversify a static PDB structure with faster wall-clock convergence to certain equilibrium properties than replicate MD trajectories.
• Score: 11.1639408863378
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The biological functions of proteins often depend on dynamic structural ensembles. In this work, we develop a flow-based generative modeling approach for learning and sampling the conformational landscapes of proteins. We repurpose highly accurate single-state predictors such as AlphaFold and ESMFold and fine-tune them under a custom flow matching framework to obtain sequence-conditoned generative models of protein structure called AlphaFlow and ESMFlow. When trained and evaluated on the PDB, our method provides a superior combination of precision and diversity compared to AlphaFold with MSA subsampling. When further trained on ensembles from all-atom MD, our method accurately captures conformational flexibility, positional distributions, and higher-order ensemble observables for unseen proteins. Moreover, our method can diversify a static PDB structure with faster wall-clock convergence to certain equilibrium properties than replicate MD trajectories, demonstrating its potential as a proxy for expensive physics-based simulations. Code is available at https://github.com/bjing2016/alphaflow.

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