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 new Enhanced Database of Interacting Protein Structures (DIPS-Plus) and the 13th and 14th CASP-CAPRI experiments, achieves 17% and 13% 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.
翻译:用于预测蛋白质之间界面接触的计算方法在药物发现后极需要大量寻找,因为它们能够大大提高替代方法的准确性,例如蛋白质蛋白对接、蛋白功能分析工具和其他蛋白生物信息学计算方法。在这项工作中,我们介绍了几何变换器,这是用于轮换和翻译变异蛋白界面接触预测的新型几何进化图变异变异变异器,包装在深海内,一个端到端预测管道。深 Interact预测了特定伙伴的蛋白质接口接触(即蛋白质残留-异同源接触),因为有两种蛋白质作为投入的三维三级结构。在严格的基准中,深内在互换,关于新的增强的互换蛋白结构数据库(DIPS-Plus)中具有挑战性的蛋白质复杂目标,以及CASP-CAPRI第13和第14次实验,分别实现了17%和13%的顶级L/5精确度(一个综合体的蛋白质单位的长度)。为此,在深度变异基体的深度变基体结构结构结构中,在深度变压的深度变压结构中,与深度变压的深度变压结构的深度变压关系中,在现有的基底基底基底基底基质结构中,增加了现有基质变压的基质结构的对比中学习关系,以其他基底基质的基质的基底基数列。