Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks

Ananthan Nambiar, Maeve Heflin, Simon Liu, Sergei Maslov, Mark Hopkins, Anna Ritz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The scientific community is rapidly generating protein sequence information, but only a fraction of these proteins can be experimentally characterized. While promising deep learning approaches for protein prediction tasks have emerged, they have computational limitations or are designed to solve a specific task. We present a Transformer neural network that pre-Trains task-Agnostic sequence representations. This model is fine-Tuned to solve two different protein prediction tasks: protein family classification and protein interaction prediction. Our method is comparable to existing state-of-The-Art approaches for protein family classification while being much more general than other architectures. Further, our method outperforms all other approaches for protein interaction prediction. These results offer a promising framework for fine-Tuning the pre-Trained sequence representations for other protein prediction tasks.

Original languageEnglish (US)
Title of host publicationProceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450379649
DOIs
StatePublished - Sep 21 2020
Event11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020 - Virtual, Online, United States
Duration: Sep 21 2020Sep 24 2020

Publication series

NameProceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020

Conference

Conference11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020
Country/TerritoryUnited States
CityVirtual, Online
Period9/21/209/24/20

Keywords

  • Neural networks
  • protein family classification
  • protein-protein interaction prediction
  • COVID-19

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Biomedical Engineering
  • Health Informatics

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