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

Shaylyn Govender1, Emily Morgan1, Rabelani Ramahala1

  • 1Research Unit in Bioinformatics (RUBi), Department of Biochemistry, Microbiology and Bioinformatics, Rhodes University, Makhanda 6139, South Africa.

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|May 12, 2025
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Summary

Transfer learning successfully applied machine learning models trained on SARS-CoV-2 main protease to other viral proteins, showing promise for predicting mutations and guiding drug development against viral evolution.

Keywords:
Dynamic residue networkMachine learningMain proteaseNSP10-NSP16 complexPapain-like proteaseSpike protein

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Area of Science:

  • Virology
  • Computational Biology
  • Machine Learning

Background:

  • Predicting viral mutations is vital for combating drug resistance and developing effective treatments.
  • Machine learning (ML) models using dynamic residue network (DRN) data were previously developed for SARS-CoV-2 main protease (Mpro) mutations.

Purpose of the Study:

  • To evaluate the generalizability of ML models across different SARS-CoV-2 proteins using transfer learning (TL).
  • To establish a foundation for a universal ML model for predicting viral missense mutation frequencies.

Main Methods:

  • Employed a transfer learning (TL) approach to test Mpro-trained ML models on other SARS-CoV-2 proteins (NSP10, NSP16, PLpro, S protein).
  • Utilized dynamic residue network (DRN) metric data and existing mutation data.
  • Assessed model performance using correlation coefficients (R) and p-values.

Main Results:

  • TL showed high promise, with artificial neural network (ANN) and random forest (RF) models trained on Mpro exhibiting strong correlations with NSP10, NSP16, and PLpro.
  • ANN |R| values for Mpro (0.564) closely matched NSP10 (0.533), NSP16 (0.527), and PLpro (0.464).
  • RF |R| values for Mpro (0.673) were comparable to NSP10 (0.457), NSP16 (0.460), and PLpro (0.437).
  • A strong correlation was not observed for the spike (S) protein.
  • Statistically significant linear correlations were confirmed by low p-values.

Conclusions:

  • Transfer learning demonstrates potential for generalizing ML models across structurally related viral proteins.
  • This study provides a foundational step towards a universal ML model for predicting viral mutations.
  • The findings support the use of DRN-derived ML models for predicting mutation frequencies in viral proteins.