Towards Interpreting Zoonotic Potential of Betacoronavirus Sequences With Attention

• URL: http://arxiv.org/abs/2108.08077v1
• Date: Wed, 18 Aug 2021 10:11:11 GMT
• Title: Towards Interpreting Zoonotic Potential of Betacoronavirus Sequences With Attention
• Authors: Kahini Wadhawan and Payel Das and Barbara A. Han and Ilya R. Fischhoff and Adrian C. Castellanos and Arvind Varsani and Kush R. Varshney
• Abstract summary: 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. 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.
• Score: 17.406451433347527
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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