DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier

By Maxat Kulmanov, Mohammed Asif Khan, Robert Hoehndorf. 2017

471 citations recorded by the source corpus.

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 baseline 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 Supplementary information Supplementary data are available at Bioinformatics online.

Subfields: Molecular Biology

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In Molecular Biology

Citations in the corpus

UDSMProt: universal deep sequence models for protein classification (2019, methodology): “provided evaluation scripts [15], [27].”
Machine learning for discovering missing or wrong protein function annotations (2019, methodology): “On a similar note, Kulmanov et al. [36] proposed to train a predictive model for each sub-ontology of the Gene Ontology, combining features automatically learned from the sequences and features based on protein interactions.”
Deep learning in bioinformatics: introduction, application, and perspective in big data era (2019, methodology): “For recurrent neural networks, the more advanced architectures help people deal with the gradient vanishing or explosion issue and accelerate the execution of RNN.”
Leveraging implicit knowledge in neural networks for functional dissection and engineering of proteins (2019, methodology): “We also constructed subsets of the CAFA3 test dataset with limited local sequence identity (Supplementary Note 1).”
Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures (2021, methodology): “…neural networks (CNN) to learn protein representations directly from an amino acid sequence, for tasks such as protein function prediction (Kulmanov et al., 2017; Kulmanov & Hoehndorf, 2019), protein-compound interaction (Tsubaki et al., 2018), or protein fold classiﬁcation (Hou et al.,…”