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

JunJie Wee1, Guo-Wei Wei1,2,3

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A new computational strategy using AlphaFold 3 and topological deep learning accurately predicts viral mutations and binding energy changes, aiding rapid response to evolving infectious viruses like SARS-CoV-2.

Keywords:
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Area of Science:

  • Computational biology
  • Virology
  • Structural biology

Background:

  • Rapid viral evolution, including SARS-CoV-2, necessitates faster methods for tracking, diagnostics, and developing countermeasures like vaccines and monoclonal antibodies (mAbs).
  • Current computational tools, such as topological deep learning (TDL), require extensive experimental data like deep mutational scanning (DMS) and 3D protein-protein interaction (PPI) complex structures, which are time-consuming and costly to obtain.

Purpose of the Study:

  • To develop an efficient computational approach, the AlphaFold 3 (AF3)-assisted multi-task topological Laplacian (MT-TopLap) strategy, to predict the impact of viral mutations on protein-protein interactions (PPIs) and binding free energy (BFE).
  • To enhance the prediction of deep mutational scanning (DMS) and BFE changes using topological data analysis (TDA) and deep learning, reducing reliance on experimental structures.

Main Methods:

  • The proposed MT-TopLap strategy integrates AlphaFold 3 (AF3) for structural prediction with topological data analysis (TDA) models, specifically persistent Laplacians (PL).
  • This approach extracts topological and geometric features from protein-protein interaction (PPI) complex structures to predict changes in binding free energy (BFE) and deep mutational scanning (DMS) upon viral mutations.
  • The method was validated using four 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 degradation compared to using experimental structures (average 1.1% decrease in Pearson correlation coefficients (PCC) and 9.3% increase in root mean square errors (RMSE)).
  • The model achieved a PCC of 0.81 when tested on a SARS-CoV-2 HK.3 variant DMS dataset, indicating accurate prediction of BFE changes.
  • The strategy proved adaptable to new experimental data, confirming its potential for real-time application.

Conclusions:

  • The AF3-assisted MT-TopLap strategy offers an efficient and accurate computational tool for predicting the effects of viral mutations on PPIs and BFE.
  • This approach can accelerate the response to emerging infectious viruses by improving viral tracking, diagnostics, and the design of therapeutics and vaccines.
  • The method's ability to adapt to new data highlights its value in addressing the challenges posed by fast-evolving pathogens.