Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
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DOI：
10.1038/s41592-019-0666-6
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发表时间：
2020-02-01
期刊：
影响因子：
48
通讯作者：
Correia, B. E.
中科院分区：
文献类型：
--
作者：
Gainza, P.;Sverrisson, F.;Correia, B. E.
MaSIF是一种基于深度学习的方法，可以发现生物分子表面的化学和几何特征的共同模式，用于预测蛋白质-配体和蛋白质-蛋白质相互作用。仅基于结构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一个挑战。蛋白质结构的高级表示，分子表面，显示化学和几何特征的模式，这些特征是蛋白质与其他生物分子相互作用的模式。我们假设参与类似相互作用的蛋白质可能具有共同的指纹，而与它们的进化历史无关。指纹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。我们提出了MaSIF（分子表面相互作用指纹），这是一个基于几何深度学习方法的概念框架，用于捕获对特定生物分子相互作用重要的指纹。我们展示了MaSIF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和蛋白质表面的超快扫描预测蛋白质-蛋白质复合物。我们预计，我们的概念框架将导致我们对蛋白质功能和设计的理解的改进。
MaSIF, a deep learning-based method, finds common patterns of chemical and geometric features on biomolecular surfaces for predicting protein-ligand and protein-protein interactions.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.