Accurate prediction of protein side-chain conformations is necessary to understand protein folding, proteinprotein interactions and facilitate de novo protein design. Here we apply torsional flow matching and equivariant graph attention to develop FlowPacker, a fast and performant model to predict protein sidechain conformations conditioned on the protein sequence and backbone. We show that FlowPacker outperforms previous state-of-the-art baselines across most metrics with improved runtime. We further show that FlowPacker can be used to inpaint missing side-chain coordinates and also for multimeric targets, and exhibits strong performance on a test set of antibody-antigen complexes. Code is available at https://gitlab.com/mjslee0921/flowpacker .
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Lee et al. (2024) studied this question.
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