瑞士洛桑生物信息学研究所B. E. Correia研究团队利用几何深度学习实现对蛋白质分子表面相互作用特征的破译。2019年12月9日《自然—方法学》在线发表了这项成果。

研究人员表示，仅基于结构来预测蛋白质与其他生物分子之间的相互作用仍然是生物学中的挑战。蛋白质结构的高级表现，即分子表面，显示出化学和几何特征的模式，从而可以识别蛋白质与其他生物分子相互作用的模式。

研究人员假设参与相似相互作用的蛋白质可能共享相同的特征，而与它们的进化历史无关。特征可能很难通过视觉分析来掌握，但可以从大规模数据集中学习。研究人员提出了MaSIF（分子表面相互作用特征），这是一个基于几何深度学习方法的概念框架，用于捕获对于特定生物分子相互作用非常重要的指纹。研究人员展示了MaSIF面临的三个预测挑战：蛋白质口袋配体预测、蛋白质-蛋白质相互作用位点预测以及蛋白质表面超快扫描以预测蛋白质-蛋白质复合物。研究人员认为这一概念框架将导致我们对蛋白质功能和设计的理解得到改善。

附：英文原文

Title: Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning

Author: P. Gainza, F. Sverrisson, F. Monti, E. Rodol, D. Boscaini, M. M. Bronstein, B. E. Correia

Issue&Volume: 2019-12-09

Abstract: Predicting interactions between proteins and other biomolecules solely based on structure remains a challenge in biology. A high-level representation of protein structure, the molecular surface, displays patterns of chemical and geometric features that fingerprint a protein’s modes of interactions with other biomolecules. We hypothesize that proteins participating in similar interactions may share common fingerprints, independent of their evolutionary history. Fingerprints may be difficult to grasp by visual analysis but could be learned from large-scale datasets. We present MaSIF (molecular surface interaction fingerprinting), a conceptual framework based on a geometric deep learning method to capture fingerprints that are important for specific biomolecular interactions. We showcase MaSIF with three prediction challenges: protein pocket-ligand prediction, protein–protein interaction site prediction and ultrafast scanning of protein surfaces for prediction of protein–protein complexes. We anticipate that our conceptual framework will lead to improvements in our understanding of protein function and design.

DOI: 10.1038/s41592-019-0666-6

Source: https://www.nature.com/articles/s41592-019-0666-6