Geometric Transformers for Protein Interface Contact Prediction
Geometric Transformers for Protein Interface Contact Prediction
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发表时间：
2021-10
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通讯作者：
Alex Morehead;Chen Chen-Chen;Jianlin Cheng
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作者：
Alex Morehead;Chen Chen-Chen;Jianlin Cheng
预测蛋白质间界面接触的计算方法在药物发现中备受追捧，因为它们可以显著提高替代方法的准确性，如蛋白质-蛋白质对接、蛋白质功能分析工具和其他蛋白质生物信息学的计算方法。在这项工作中，我们提出了一种新的几何进化图形转换器，用于旋转和平移不变的蛋白质界面接触预测，封装在端到端预测管道DeepInteract中。DeepInteract预测伙伴特定的蛋白质界面接触(即蛋白质间残基-残基接触)，给定两个蛋白质的3D三级结构作为输入。在严格的基准测试中，DeepInteract在第13和14次CASP-Capri实验中挑战蛋白质复杂目标以及对接基准5，分别达到了L/5的14%和1.1%的高精度(L：蛋白质单位在复合体中的长度)。在这方面，DeepInteract以几何变形器为其基于图形的主干，其性能优于现有的界面接触预测方法以及与DeepInteract兼容的其他基于图形的神经网络主干，从而验证了几何变形器在学习3D蛋白质结构下游任务的丰富关系几何特征方面的有效性。
Computational methods for predicting the interface contacts between proteins come highly sought after for drug discovery as they can significantly advance the accuracy of alternative approaches, such as protein-protein docking, protein function analysis tools, and other computational methods for protein bioinformatics. In this work, we present the Geometric Transformer, a novel geometry-evolving graph transformer for rotation and translation-invariant protein interface contact prediction, packaged within DeepInteract, an end-to-end prediction pipeline. DeepInteract predicts partner-specific protein interface contacts (i.e., inter-protein residue-residue contacts) given the 3D tertiary structures of two proteins as input. In rigorous benchmarks, DeepInteract, on challenging protein complex targets from the 13th and 14th CASP-CAPRI experiments as well as Docking Benchmark 5, achieves 14% and 1.1% top L/5 precision (L: length of a protein unit in a complex), respectively. In doing so, DeepInteract, with the Geometric Transformer as its graph-based backbone, outperforms existing methods for interface contact prediction in addition to other graph-based neural network backbones compatible with DeepInteract, thereby validating the effectiveness of the Geometric Transformer for learning rich relational-geometric features for downstream tasks on 3D protein structures.