Randolph Nicholas Z, Kuhlman Brian
Department of Bioinformatics and Computational Biology, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.
Department of Biochemistry and Biophysics, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.
bioRxiv. 2023 Dec 21:2023.08.03.551328. doi: 10.1101/2023.08.03.551328.
Protein side chain packing (PSCP) is a fundamental problem in the field of protein engineering, as high-confidence and low-energy conformations of amino acid side chains are crucial for understanding (and designing) protein folding, protein-protein interactions, and protein-ligand interactions. Traditional PSCP methods (such as the Rosetta Packer) often rely on a library of discrete side chain conformations, or rotamers, and a forcefield to guide the structure to low-energy conformations. Recently, deep learning (DL) based methods (such as DLPacker, AttnPacker, and DiffPack) have demonstrated state-of-the-art predictions and speed in the PSCP task. Building off the success of geometric graph neural networks for protein modeling, we present the Protein Invariant Point Packer (PIPPack) which effectively processes local structural and sequence information to produce realistic, idealized side chain coordinates using -angle distribution predictions and geometry-aware invariant point message passing (IPMP). On a test set of ~1,400 high-quality protein chains, PIPPack is highly competitive with other state-of-the-art PSCP methods in rotamer recovery and per-residue RMSD but is significantly faster.
蛋白质侧链堆积（PSCP）是蛋白质工程领域的一个基本问题，因为氨基酸侧链的高可信度和低能量构象对于理解（和设计）蛋白质折叠、蛋白质-蛋白质相互作用以及蛋白质-配体相互作用至关重要。传统的PSCP方法（如Rosetta Packer）通常依赖于离散侧链构象库（即旋转异构体）和力场来引导结构达到低能量构象。最近，基于深度学习（DL）的方法（如DLPacker、AttnPacker和DiffPack）在PSCP任务中展现出了最先进的预测能力和速度。基于用于蛋白质建模的几何图神经网络的成功，我们提出了蛋白质不变点堆积器（PIPPack），它使用角度分布预测和几何感知不变点消息传递（IPMP）有效地处理局部结构和序列信息，以生成逼真的、理想化的侧链坐标。在一个包含约1400条高质量蛋白质链的测试集上，PIPPack在旋转异构体恢复和每个残基的均方根偏差方面与其他最先进的PSCP方法具有高度竞争力，但速度要快得多。
