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Benchmarking the accuracy of structure-based binding affinity predictors on Spike-ACE2 deep mutational interaction

Burcu Ozden1,2, Eda Şamiloğlu1,2, Atakan Özsan1

  • 1Izmir Biomedicine and Genome Center, Dokuz Eylul University Health Campus, Izmir, Turkey.

Proteins
|November 22, 2023
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Summary

Six computational tools for predicting SARS-CoV-2 Spike-ACE2 binding affinity were benchmarked. None accurately predicted experimental data, highlighting the need for improved binding affinity predictors for host-pathogen systems.

Keywords:
ACE2RBDSARS-CoV-2binding affinity predictiondeep mutagenesis

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Area of Science:

  • Computational biology
  • Structural biology
  • Virology

Background:

  • Understanding SARS-CoV-2 Spike-ACE2 interactions is crucial for pandemic response.
  • Deep mutational scanning studies have generated extensive data on mutations affecting Spike and ACE2 binding.

Purpose of the Study:

  • To benchmark the performance of commonly used structure-based binding affinity predictors.
  • To evaluate predictors using experimental data from deep mutational scanning studies of Spike-ACE2 interface mutations.

Main Methods:

  • Six structure-based binding affinity predictors (FoldX, EvoEF1, MutaBind2, SSIPe, HADDOCK, UEP) were selected and tested.
  • Predictors were benchmarked against experimental binding data derived from deep mutational scanning.
  • Performance was assessed using correlation coefficients and binary classification accuracy.

Main Results:

  • No tested predictor showed a meaningful correlation with experimental binding affinity data.
  • FoldX achieved the best correlation (R = -0.51) and binary classification accuracy (64%).
  • Simple energetic scoring functions outperformed evolutionary-based methods, and AI approaches showed comparable performance to force field-based techniques.

Conclusions:

  • Current structure-based binding affinity predictors require significant improvement for predicting variant-induced binding changes in host-pathogen systems.
  • The study provides valuable benchmarking data and visualized mutant models to facilitate future predictor development.