Sorbonne Université, CNRS, IBPS, Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Paris 75005, France.
Bioinformatics. 2022 Sep 30;38(19):4505-4512. doi: 10.1093/bioinformatics/btac551.
With the recent advances in protein 3D structure prediction, protein interactions are becoming more central than ever before. Here, we address the problem of determining how proteins interact with one another. More specifically, we investigate the possibility of discriminating near-native protein complex conformations from incorrect ones by exploiting local environments around interfacial residues.
Deep Local Analysis (DLA)-Ranker is a deep learning framework applying 3D convolutions to a set of locally oriented cubes representing the protein interface. It explicitly considers the local geometry of the interfacial residues along with their neighboring atoms and the regions of the interface with different solvent accessibility. We assessed its performance on three docking benchmarks made of half a million acceptable and incorrect conformations. We show that DLA-Ranker successfully identifies near-native conformations from ensembles generated by molecular docking. It surpasses or competes with other deep learning-based scoring functions. We also showcase its usefulness to discover alternative interfaces.
http://gitlab.lcqb.upmc.fr/dla-ranker/DLA-Ranker.git.
Supplementary data are available at Bioinformatics online.
随着蛋白质 3D 结构预测技术的最新进展，蛋白质相互作用变得比以往任何时候都更加重要。在这里，我们解决了确定蛋白质如何相互作用的问题。更具体地说，我们通过利用界面残基周围的局部环境来研究区分近天然蛋白质复合物构象与不正确构象的可能性。
深度局部分析（DLA）-Ranker 是一个深度学习框架，它将 3D 卷积应用于一组表示蛋白质界面的局部定向立方体。它明确考虑了界面残基的局部几何形状及其相邻原子以及具有不同溶剂可及性的界面区域。我们在三个由五十万个可接受和不正确构象组成的对接基准上评估了它的性能。我们表明，DLA-Ranker 能够成功地从分子对接生成的集合中识别近天然构象。它优于或可与其他基于深度学习的评分函数竞争。我们还展示了它用于发现替代界面的有用性。
http://gitlab.lcqb.upmc.fr/dla-ranker/DLA-Ranker.git。
补充数据可在 Bioinformatics 在线获得。
