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Rapid response to fast viral evolution using AlphaFold 3-assisted topological deep learning.

JunJie Wee1, Guo-Wei Wei1,2,3

  • 1Department of Mathematics, Michigan State University, East Lansing, MI 48824, United States.

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Summary

This study introduces an AlphaFold 3-assisted computational strategy to predict viral mutations. The method accurately forecasts changes in binding free energy, aiding rapid responses to evolving infectious viruses.

Keywords:
AlphaFold 3SARS-CoV-2 variantsdeep mutational scanningprotein–protein interactionstopological deep learning

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Area of Science:

  • Computational biology
  • Virology
  • Structural biology

Background:

  • Rapid evolution of viruses like SARS-CoV-2 necessitates efficient computational tools for tracking, diagnostics, and therapeutic development.
  • Current methods for variant forecasting often rely on deep mutational scanning (DMS) and 3D protein-protein interaction (PPI) complex structures, which can be time-consuming and costly.

Purpose of the Study:

  • To develop an efficient computational approach for predicting the impact of viral mutations on protein-protein interactions and binding.
  • To enhance the prediction of deep mutational scanning (DMS) and binding free energy (BFE) changes upon virus mutations using topological deep learning (TDL).

Main Methods:

  • Proposed an AlphaFold 3 (AF3)-assisted multi-task topological Laplacian (MT-TopLap) strategy.
  • Combined deep learning with topological data analysis (TDA) models, such as persistent Laplacians (PL), to extract topological and geometric features of PPIs.
  • Validated the strategy using experimental DMS datasets of SARS-CoV-2 spike receptor-binding domain (RBD) and human angiotensin-converting enzyme-2 (ACE2) complexes.

Main Results:

  • The AF3-assisted MT-TopLap strategy demonstrated robust performance, with minimal decrease in Pearson correlation coefficients (PCC) and slight increase in root mean square errors (RMSE) compared to experimental structures.
  • Achieved a PCC of 0.81 when tested with a SARS-CoV-2 HK.3 variant DMS dataset, indicating accurate prediction of BFE changes.
  • Showcased adaptability to new experimental data, confirming its potential for real-time application.

Conclusions:

  • The AF3-assisted MT-TopLap strategy offers an efficient and accurate computational approach for predicting the effects of viral mutations.
  • This method can accelerate the response to emerging infectious viruses by improving viral tracking, diagnostics, and the design of therapeutics.
  • The strategy holds significant potential for rapid and effective adaptation to the challenges posed by fast-evolving viruses.