Bioinformatics (Oxford, England) · 2025
FlowPacker: protein side-chain packing with torsional flow matching.
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Lee Jin Sub, Kim Philip M
Аннотация
MOTIVATION: Accurate prediction of protein side-chain conformations is necessary to understand protein folding, protein-protein interactions and facilitate de novo protein design.
RESULTS: Here, we apply torsional flow matching and equivariant graph attention to develop FlowPacker, a fast and performant model to predict protein side-chain 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.
AVAILABILITY AND IMPLEMENTATION: Code is available at https://gitlab.com/mjslee0921/flowpacker.
RESULTS: Here, we apply torsional flow matching and equivariant graph attention to develop FlowPacker, a fast and performant model to predict protein side-chain 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.
AVAILABILITY AND IMPLEMENTATION: Code is available at https://gitlab.com/mjslee0921/flowpacker.