FlowPacker: Protein side-chain packing with torsional flow matching.

Jin Sub Lee, Philip M Kim
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引用次数: 0

Abstract

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: Code is available at https://gitlab.com/mjslee0921/flowpacker.

Supplementary information: Supplementary data are available at Bioinformatics online.

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FlowPacker:具有扭流匹配的蛋白质侧链填料。
动机:准确预测蛋白质侧链构象对于理解蛋白质折叠、蛋白质相互作用和促进蛋白质从头设计是必要的。结果:本文应用扭转流匹配和等变图关注开发了FlowPacker模型,该模型能够快速预测蛋白质侧链构象,并以蛋白质序列和主链为条件。我们发现,FlowPacker在大多数指标上都优于之前最先进的基准,并改善了运行时间。我们进一步表明,FlowPacker可以用于填补缺失的侧链坐标,也可以用于多聚靶标,并且在抗体-抗原复合物的测试集上表现出很强的性能。可用性:代码可在https://gitlab.com/mjslee0921/flowpacker.Supplementary上获得;补充数据可在Bioinformatics在线上获得。
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