Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing, China.
Protein Sci. 2022 Dec;31(12):e4484. doi: 10.1002/pro.4484.
Atomic interactions play essential roles in protein folding, structure stabilization, and function performance. Recent advances in deep learning-based methods have achieved impressive success not only in protein structure prediction, but also in protein sequence design. However, highly efficient and accurate protein side-chain prediction methods that can give detailed atomic interactions are still lacking. In the present study, we developed a deep learning based method, GeoPacker, that uses geometric deep learning coupled ResNet for protein side-chain modeling. GeoPacker explicitly represents atomic interactions with rotational and translational invariance for information extraction of relative locations. GeoPacker outperformed the state-of-the-art energy function-based methods in side-chain structure prediction accuracy and runs about 10 and 700 times faster than the deep learning-based method DLPacker and OPUS-rota4 with comparable prediction accuracy, respectively. The performance of GeoPacker does not depend on the secondary structures that the residues belong to. GeoPacker gives highly accurate predictions for buried residues in the protein core as well as protein-protein interface, making it a useful tool for protein structure modeling, protein, and interaction design.
原子相互作用在蛋白质折叠、结构稳定和功能表现中起着至关重要的作用。基于深度学习的方法最近取得了令人瞩目的成功，不仅在蛋白质结构预测方面，而且在蛋白质序列设计方面也是如此。然而，仍然缺乏能够提供详细原子相互作用的高效准确的蛋白质侧链预测方法。在本研究中，我们开发了一种基于深度学习的方法 GeoPacker，该方法使用几何深度学习和 ResNet 来进行蛋白质侧链建模。GeoPacker 利用旋转和平移不变性来明确表示原子相互作用，以便对相对位置的信息进行提取。GeoPacker 在侧链结构预测准确性方面优于最先进的基于能量函数的方法，与基于深度学习的方法 DLPacker 和 OPUS-rota4 的预测准确性相当，但运行速度分别快 10 倍和 700 倍。GeoPacker 的性能不依赖于残基所属的二级结构。GeoPacker 对蛋白质核心中的埋藏残基以及蛋白质-蛋白质界面都能给出高度准确的预测，使其成为蛋白质结构建模、蛋白质和相互作用设计的有用工具。
