Mutation prediction and phylogenetic analysis of SARS-CoV2 protein sequences using LSTM based encoder-decoder model

Abstract

The ongoing evolution and mutation of SARS-CoV2 pose a significant challenge to the development of effective medication, as genetic changes can render previously developed drugs ineffective. To address this issue, researchers are exploring various strategies to predict and assess the emergence of novel SARS-CoV2 strains through phylogenetic analysis and mutation prediction. In recent years, deep learning approaches have been applied to studying viruses, including SARS-CoV2, to improve our understanding of virus evolution, structure, categorization, and prediction. In this study, a novel deep learning approach is proposed to predict and assess SARS-CoV2 protein sequences. Specifically, Long Short-Term Memory (LSTM) is utilized to predict protein sequences from aligned input sequences, with a bioinformatics tool used to detect mutations. The deep learning model proposed in this study exhibits high accuracy in predicting several key SARS-CoV2 protein sequences, including spike, replicase, putative, ORF1a, and nucleocapsid. The study uses genome sequencing data from the National Center for Biotechnology Information (NCBI) and demonstrates a 98% accuracy in predicting genomic sequences, with minimal changes observed in protein sequences. This study represents a significant improvement over previous research, which has focused only on predicting mutations in viral RNA sequences using datasets from other viruses.