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Towards Interpreting Zoonotic Potential of Betacoronavirus Sequences With Attention

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 01 Jan 2021Embargo end date: 01 Jan 2021Publisher:arXiv

Authors: Wadhawan, Kahini; Das, Payel; Han, Barbara A.; Fischhoff, Ilya R.; Castellanos, Adrian C.; Varsani, Arvind; Varshney, Kush R.;

doi: 10.48550/arxiv.2108.08077

arXiv: http://arxiv.org/abs/2108.08077

Towards Interpreting Zoonotic Potential of Betacoronavirus Sequences With Attention

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Abstract

Current methods for viral discovery target evolutionarily conserved proteins that accurately identify virus families but remain unable to distinguish the zoonotic potential of newly discovered viruses. Here, we apply an attention-enhanced long-short-term memory (LSTM) deep neural net classifier to a highly conserved viral protein target to predict zoonotic potential across betacoronaviruses. The classifier performs with a 94% accuracy. Analysis and visualization of attention at the sequence and structure-level features indicate possible association between important protein-protein interactions governing viral replication in zoonotic betacoronaviruses and zoonotic transmission.

11 pages, 8 figures, 1 table, accepted at ICLR 2021 workshop Machine learning for preventing and combating pandemics

Keywords

FOS: Computer and information sciences, Computer Science - Machine Learning, FOS: Biological sciences, Quantitative Biology - Quantitative Methods, Quantitative Methods (q-bio.QM), Machine Learning (cs.LG)

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