Abstract
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.
Competing Interest Statement
PMK is a co-founder and consultant to several biotechnology companies, including Fable Therapeutics, TBG Therapeutics, and Zymedi. JSL reports no competing interests.
Footnotes
jinsub.lee{at}mail.utoronto.ca; pm.kim{at}utoronto.ca
Add code URL and new model with new training data
↵2 see rotate_side_chain function in https://github.com/DeepGraphLearning/DiffPack/blob/main/diffpack/rotamer.py