蛋白质功能预测中的深度学习方法：结合序列与互作网络的深层分类模型（DeepGO）资源-CSDN文库

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Databases and ontologies

DeepGO: predicting protein functions from

sequence and interactions using a deep

ontology-aware classifier

Maxat Kulmanov, Mohammed Asif Khan and Robert Hoehndorf*

Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research

Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia

*To whom correspondence should be addressed.

Associate Editor: Jonathan Wren

Received on May 10, 2017; revised on September 23, 2017; editorial decision on September 25, 2017; accepted on September 27, 2017

Abstract

Motivation: A large number of protein sequences are becoming available through the application

of novel high-throughput sequencing technologies. Experimental functional characterization of

these proteins is time-consuming and expensive, and is often only done rigorously for few selected

model organisms. Computational function prediction approaches have been suggested to ﬁll this

gap. The functions of proteins are classiﬁed using the Gene Ontology (GO), which contains over

40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-

scale, multi-class, multi-label problem.

Results: We have developed a novel method to predict protein function from sequence. We use

deep learning to learn features from protein sequences as well as a cross-species protein–protein

interaction network. Our approach speciﬁcally outputs information in the structure of the GO and

utilizes the dependencies between GO classes as background information to construct a deep

learning model. We evaluate our method using the standards established by the Computational

Assessment of Function Annotation (CAFA) and demonstrate a signiﬁcant improvement over base-

line methods such as BLAST, in particular for predicting cellular locations.

Availability and implementation: Web server: http://deepgo.bio2vec.net, Source code: https://

github.com/bio-ontology-research-group/deepgo

Contact: robert.hoehndorf@kaust.edu.sa

Supplementary information: Supplementary data are available at Bioinformatics online.

1 Introduction

Advances in sequencing technology have led to a large and rapidly

increasing amount of genetic and protein sequences, and the amount

is expected to increase further through sequencing of additional

organisms as well as metagenomics. Although knowledge of protein

sequences is useful for many applications, such as phylogenetics and

evolutionary biology, understanding the behavior of biological sys-

tems additionally requires knowledge of the proteins’ functions.

Identifying protein functions is challenging and commonly requires

in vitro or in vivo experiments (Costanzo et al., 2016), and it is

obvious that experimental functional annotation of proteins will not

scale with the amount of novel protein sequences becoming

available.

One approach to address the challenge of identifying proteins’

functions is the computational prediction of protein functions

(Radivojac et al., 2013). Function prediction can use several sources

of information, including protein–protein interactions (Hou, 2017;

Jiang and McQuay, 2012; Kirac and Ozsoyoglu, 2008; Nguyen

et al., 2011; Sharan et al., 2007), genetic interactions (Costanzo

et al., 2016), evolutionary relations (Gaudet et al., 2011), protein

structures and structure prediction methods (Konc et al., 2013), lit-

erature (Verspoor, 2014) or combinations of these (Sokolov and

The Author 2017. Published by Oxford University Press. 660

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits

unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Bioinformatics, 34(4), 2018, 660–668

doi: 10.1093/bioinformatics/btx624

Advance Access Publication Date: 3 October 2017

Original Paper

Downloaded from https://academic.oup.com/bioinformatics/article-abstract/34/4/660/4265461 by University of Michigan user on 15 January 2020

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