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Workshop: AI for Science: from Theory to Practice

AlphaFold Meets Flow Matching for Generating Protein Ensembles

Bowen Jing · Bowen Jing · Bonnie Berger · Bonnie Berger · Tommi Jaakkola


The significant success of AlphaFold2 at protein structure prediction has pointed to structural ensembles as the next frontier towards a more complete computational understanding of protein structure. At the same time, iterative refinement-based techniques such as diffusion have driven significant breakthroughs in generative modeling. We explore the synergy of these developments by combining highly accurate protein structure prediction models with flow matching, a powerful modern generative modeling framework, in order to sample the conformational landscape of proteins. Preliminary results on membrane transporters, ligand-induced conformational change, and disordered ensembles show the potential of the approach. Importantly, and unlike MSA-based methods, our method also obtains similar distributions even when used with language model-based algorithms such as ESMFold, which are otherwise deterministic given an input sequence. These results open exciting avenues in the computational prediction of conformational flexibility.

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