Mutation prediction in the SARS-CoV-2 genome using attention-based neural machine translation

Darrak Moin Quddusi; Sandesh Athni Hiremath; Naim Bajcinca; Darrak Moin Quddusi; Sandesh Athni Hiremath; Naim Bajcinca

doi:10.3934/mbe.2024264

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 5: 5996-6018. doi: 10.3934/mbe.2024264

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Mutation prediction in the SARS-CoV-2 genome using attention-based neural machine translation

Chair of Mechatronics in the Faculty of Mechanical and Process Engineering, Rheinland-Pfalz Technical University of Kaiserslautern-Landau, Kaiserslautern 67663, Germany

Academic Editor: Pedro Carmona Sáez

Received: 31 January 2024 Revised: 05 April 2024 Accepted: 30 April 2024 Published: 20 May 2024

Severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) has been evolving rapidly after causing havoc worldwide in 2020. Since then, it has been very hard to contain the virus owing to its frequently mutating nature. Changes in its genome lead to viral evolution, rendering it more resistant to existing vaccines and drugs. Predicting viral mutations beforehand will help in gearing up against more infectious and virulent versions of the virus in turn decreasing the damage caused by them. In this paper, we have proposed different NMT (neural machine translation) architectures based on RNNs (recurrent neural networks) to predict mutations in the SARS-CoV-2-selected non-structural proteins (NSP), i.e., NSP1, NSP3, NSP5, NSP8, NSP9, NSP13, and NSP15. First, we created and pre-processed the pairs of sequences from two languages using k-means clustering and nearest neighbors for training a neural translation machine. We also provided insights for training NMTs on long biological sequences. In addition, we evaluated and benchmarked our models to demonstrate their efficiency and reliability.
- neural machine translation,
- recurrent neural networks,
- long short-term memory,
- gated recurrent units,
- mutation prediction,
- SARS-CoV-2
Citation: Darrak Moin Quddusi, Sandesh Athni Hiremath, Naim Bajcinca. Mutation prediction in the SARS-CoV-2 genome using attention-based neural machine translation[J]. Mathematical Biosciences and Engineering, 2024, 21(5): 5996-6018. doi: 10.3934/mbe.2024264

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Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) has been evolving rapidly after causing havoc worldwide in 2020. Since then, it has been very hard to contain the virus owing to its frequently mutating nature. Changes in its genome lead to viral evolution, rendering it more resistant to existing vaccines and drugs. Predicting viral mutations beforehand will help in gearing up against more infectious and virulent versions of the virus in turn decreasing the damage caused by them. In this paper, we have proposed different NMT (neural machine translation) architectures based on RNNs (recurrent neural networks) to predict mutations in the SARS-CoV-2-selected non-structural proteins (NSP), i.e., NSP1, NSP3, NSP5, NSP8, NSP9, NSP13, and NSP15. First, we created and pre-processed the pairs of sequences from two languages using k-means clustering and nearest neighbors for training a neural translation machine. We also provided insights for training NMTs on long biological sequences. In addition, we evaluated and benchmarked our models to demonstrate their efficiency and reliability.

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