蛋白质功能预测
计算机科学
卷积神经网络
Web服务器
图形
蛋白质测序
序列(生物学)
蛋白质结构
人工智能
蛋白质功能
数据挖掘
蛋白质结构预测
理论计算机科学
肽序列
生物
基因
互联网
万维网
生物化学
遗传学
作者
Vladimir Gligorijević,P. Douglas Renfrew,Tomasz Kościółek,Julia Koehler Leman,Daniel Berenberg,Tommi Vatanen,Chris Chandler,Bryn C. Taylor,I. Fisk,Hera Vlamakis,Ramnik J. Xavier,Rob Knight,Kyunghyun Cho,Richard Bonneau
标识
DOI:10.1038/s41467-021-23303-9
摘要
Abstract The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted from a protein language model and protein structures. It outperforms current leading methods and sequence-based Convolutional Neural Networks and scales to the size of current sequence repositories. Augmenting the training set of experimental structures with homology models allows us to significantly expand the number of predictable functions. DeepFRI has significant de-noising capability, with only a minor drop in performance when experimental structures are replaced by protein models. Class activation mapping allows function predictions at an unprecedented resolution, allowing site-specific annotations at the residue-level in an automated manner. We show the utility and high performance of our method by annotating structures from the PDB and SWISS-MODEL, making several new confident function predictions. DeepFRI is available as a webserver at https://beta.deepfri.flatironinstitute.org/ .
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