Journal Article Epublish
PubMed: 40352476 DOI: 10.1016/j.csbj.2025.04.029
Transfer learning towards predicting viral missense mutations: A case study on SARS-CoV-2.
Comput Struct Biotechnol J 27:1686-1692
Authors: Shaylyn Govender, Emily Morgan, Rabelani Ramahala, Kevin Lobb, Nigel T Bishop, Özlem Tastan Bishop
Affiliations
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry, Microbiology and Bioinformatics, Rhodes University, Makhanda 6139, South Africa.
- Department of Chemistry, Rhodes University, Makhanda 6139, South Africa.
- Department of Pure and Applied Mathematics, Rhodes University, Makhanda 6139, South Africa.
- National Institute for Theoretical and Computational Studies (NITheCS), South Africa.
Abstract
Understanding viral evolution and predicting future mutations are crucial for overcoming drug resistance and developing long-lasting treatments. Previously, we established machine learning (ML) models using dynamic residue network (DRN) metric data and leveraging a vast amount of existing mutation data from the SARS-CoV-2 main protease (M). Here, we sought to assess the generalizability and robustness of the current models across other SARS-CoV-2 proteins. To achieve this, for the first time, we employed a transfer learning (TL) approach, allowing us to determine the extent to which M trained models could be applied to other SARS-CoV-2 proteins. The TL results were highly promising, with artificial neural network (ANN) and random forest (RF) correlation coefficients for M closely matching those of NSP10, NSP16, and PL. The ANN |R| value for M was 0.564, while NSP10, NSP16, and PL had values of 0.533, 0.527, and 0.464, respectively. Similarly, the RF |R| value for M was 0.673, compared to 0.457, 0.460, and 0.437 for NSP10, NSP16, and PL, respectively. Interestingly, we did not observe a strong correlation for the spike (S) protein monomer and its domains. The low p-values that are associated with the correlation |R| values show that the linear correlations between predicted and actual mutation frequencies are statistically significant. This indicates that TL may generalize well across structurally related viral proteins using DRN-derived ML model from M. Overall, we aim to develop a universal ML model for predicting missense mutation frequencies in viral proteins, and this study lays the foundation for that goal.
MeSH
Mutation Rate, Sars-Cov-2, Mutation, Missense, Viral Proteins, Learning, Severe Acute Respiratory Syndrome-Related Coronavirus, Mutation