Transfer learning towards predicting viral missense mutations: A case study on SARS-CoV-2

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^pro). 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^pro 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^pro closely matching those of NSP10, NSP16, and PL^pro. The ANN |R| value for M^pro was 0.564, while NSP10, NSP16, and PL^pro had values of 0.533, 0.527, and 0.464, respectively. Similarly, the RF |R| value for M^pro was 0.673, compared to 0.457, 0.460, and 0.437 for NSP10, NSP16, and PL^pro, 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^pro. 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.