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. 2020 Jan 15;36(2):422-429.
doi: 10.1093/bioinformatics/btz595.

DeepGOPlus: improved protein function prediction from sequence

Maxat Kulmanov  1 , Robert Hoehndorf  1
Affiliations

DeepGOPlus: improved protein function prediction from sequence

Maxat Kulmanov et al. Bioinformatics. .

Erratum in

Abstract

Motivation: Protein function prediction is one of the major tasks of bioinformatics that can help in wide range of biological problems such as understanding disease mechanisms or finding drug targets. Many methods are available for predicting protein functions from sequence based features, protein-protein interaction networks, protein structure or literature. However, other than sequence, most of the features are difficult to obtain or not available for many proteins thereby limiting their scope. Furthermore, the performance of sequence-based function prediction methods is often lower than methods that incorporate multiple features and predicting protein functions may require a lot of time.

Results: We developed a novel method for predicting protein functions from sequence alone which combines deep convolutional neural network (CNN) model with sequence similarity based predictions. Our CNN model scans the sequence for motifs which are predictive for protein functions and combines this with functions of similar proteins (if available). We evaluate the performance of DeepGOPlus using the CAFA3 evaluation measures and achieve an Fmax of 0.390, 0.557 and 0.614 for BPO, MFO and CCO evaluations, respectively. These results would have made DeepGOPlus one of the three best predictors in CCO and the second best performing method in the BPO and MFO evaluations. We also compare DeepGOPlus with state-of-the-art methods such as DeepText2GO and GOLabeler on another dataset. DeepGOPlus can annotate around 40 protein sequences per second on common hardware, thereby making fast and accurate function predictions available for a wide range of proteins.

Availability and implementation: http://deepgoplus.bio2vec.net/ .

Supplementary information: Supplementary data are available at Bioinformatics online.

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Figures

Fig. 1.
Fig. 1.
Overview of the CNN in DeepGOPlus. The CNN uses multiple filters of variable size to detect the presence of sequence motifs in the input amino acid sequence
Fig. 2.
Fig. 2.
Comparison of DeepGOPlus with CAFA3 top 10 methods
Fig. 3.
Fig. 3.
Distribution of Information Content (IC) values for 5220 GO classes that are predicted for CAFA3 dataset
See this image and copyright information in PMC

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