Dai Bowen, Bailey-Kellogg Chris
Computer Science Department, Dartmouth, Hanover, NH 03755, USA.
Bioinformatics. 2021 Sep 9;37(17):2580-2588. doi: 10.1093/bioinformatics/btab154.
Protein-protein interactions drive wide-ranging molecular processes, and characterizing at the atomic level how proteins interact (beyond just the fact that they interact) can provide key insights into understanding and controlling this machinery. Unfortunately, experimental determination of three-dimensional protein complex structures remains difficult and does not scale to the increasingly large sets of proteins whose interactions are of interest. Computational methods are thus required to meet the demands of large-scale, high-throughput prediction of how proteins interact, but unfortunately, both physical modeling and machine learning methods suffer from poor precision and/or recall.
In order to improve performance in predicting protein interaction interfaces, we leverage the best properties of both data- and physics-driven methods to develop a unified Geometric Deep Neural Network, 'PInet' (Protein Interface Network). PInet consumes pairs of point clouds encoding the structures of two partner proteins, in order to predict their structural regions mediating interaction. To make such predictions, PInet learns and utilizes models capturing both geometrical and physicochemical molecular surface complementarity. In application to a set of benchmarks, PInet simultaneously predicts the interface regions on both interacting proteins, achieving performance equivalent to or even much better than the state-of-the-art predictor for each dataset. Furthermore, since PInet is based on joint segmentation of a representation of a protein surfaces, its predictions are meaningful in terms of the underlying physical complementarity driving molecular recognition.
PInet scripts and models are available at https://github.com/FTD007/PInet.
Supplementary data are available at Bioinformatics online.
蛋白质-蛋白质相互作用驱动着广泛的分子过程，在原子水平上表征蛋白质如何相互作用（不仅仅是它们相互作用这一事实），可以为理解和控制这一机制提供关键见解。不幸的是，三维蛋白质复合物结构的实验测定仍然困难，并且无法扩展到对其相互作用感兴趣的越来越多的蛋白质集合。因此，需要计算方法来满足大规模、高通量预测蛋白质相互作用方式的需求，但不幸的是，物理建模和机器学习方法都存在精度和/或召回率低的问题。
为了提高预测蛋白质相互作用界面的性能，我们利用数据驱动方法和物理驱动方法的最佳特性，开发了一个统一的几何深度神经网络“PInet”（蛋白质界面网络）。PInet输入编码两个相互作用蛋白质结构的点云对，以预测它们介导相互作用的结构区域。为了进行此类预测，PInet学习并利用捕捉几何和物理化学分子表面互补性的模型。在一组基准测试中，PInet同时预测了两个相互作用蛋白质上的界面区域，对于每个数据集，其性能与现有最佳预测器相当甚至更好。此外，由于PInet基于蛋白质表面表示的联合分割，其预测在驱动分子识别的潜在物理互补性方面具有意义。
PInet脚本和模型可在https://github.com/FTD007/PInet获取。
补充数据可在《生物信息学》在线获取。
