Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning.,Nature Methods

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Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning.
Nature Methods ( IF 36.1 ) Pub Date : 2019-12-09 , DOI: 10.1038/s41592-019-0666-6
P Gainza ₁ , F Sverrisson ₁ , F Monti _{2,

3} , E Rodolà ₄ , D Boscaini ₅ , M M Bronstein _{2,

3,

6} , B E Correia ₁

Affiliation

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.

中文翻译：

使用几何深度学习来解密蛋白质分子表面的相互作用指纹。

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示，即分子表面，显示出化学和几何特征的模式，这些模式和特征为蛋白质与其他生物分子相互作用的模式提供了指纹。我们假设参与相似相互作用的蛋白质可能共享共同的指纹，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，用于捕获对于特定生物分子相互作用非常重要的指纹。我们向MaSIF展示了三个预测挑战：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超快扫描以预测蛋白质-蛋白质复合物。我们预期我们的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。

更新日期：2019-12-11

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