Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function

Bioinformatics, vol. 37, pp. 162–170

Abstract

MOTIVATION: Protein function prediction is a difficult bioinformatics problem. Many recent methods use deep neural networks to learn complex sequence representations and predict function from these. Deep supervised models require a lot of labeled training data which are not available for this task. However, a very large amount of protein sequences without functional labels is available. RESULTS: We applied an existing deep sequence model that had been pretrained in an unsupervised setting on the supervised task of protein molecular function prediction. We found that this complex feature representation is effective for this task, outperforming hand-crafted features such as one-hot encoding of amino acids, k-mer counts, secondary structure and backbone angles. Also, it partly negates the need for complex prediction models, as a two-layer perceptron was enough to achieve competitive performance in the third Critical Assessment of Functional Annotation benchmark. We also show that combining this sequence representation with protein 3D structure information does not lead to performance improvement, hinting that 3D structure is also potentially learned during the unsupervised pretraining. AVAILABILITY AND IMPLEMENTATION: Implementations of all used models can be found at https://github.com/stamakro/GCN-for-Structure-and-Function. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Authors 5

1. Amelia Villegas-Morcillo corresponding

   Universidad de Granada

   Affiliation as printed

   Department of Signal Theory, Telematics and Communications, University of Granada , 18071 Granada, Spain

2. Stavros Makrodimitris

   KeyGene (Netherlands) · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology , 2628XE Delft, The Netherlands

   Keygene N.V. , 6708PW Wageningen, The Netherlands

3. Roeland C. H. J. van Ham

   KeyGene (Netherlands) · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology , 2628XE Delft, The Netherlands

   Keygene N.V. , 6708PW Wageningen, The Netherlands

4. Angel Manuel Gomez

   Universidad de Granada

   Affiliation as printed

   Department of Signal Theory, Telematics and Communications, University of Granada , 18071 Granada, Spain

5. Marcel J. T. Reinders Aachen Leiden Computational Biology Center

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology , 2628XE Delft, The Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center , 2333ZC Leiden, The Netherlands