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September 10, 2025Open Access

Structure-based prediction of SARS-CoV-2 variant properties using machine learning on mutational neighborhoods

MBMax van den BoomMayo Clinic ESErik SchultesPartners In Health THThomas HankemeierLeiden University

Key Points

The dataset links structural descriptors to ACE2 binding and expression data, enhancing understanding of sars-cov-2 variants.
It includes 3,705 single-point Wuhan-Hu-1 variants and 100 higher-order Omicron variants, aiding variant modeling efforts.
Using machine learning approaches, the research supports structure-function analysis in virology and structural biology.
The integration of empirical biophysical measurements provides a robust foundation for computational protein science exploration.

Abstract

This dataset presents a structure-enriched resource of theoretical and empirical SARS-CoV-2 spike receptor-binding domain (RBD) variants, developed under the STAYAHEAD project for pandemic preparedness. It integrates large-scale in silico structure predictions with empirical biophysical measurements. The dataset includes 3,705 single-point Wuhan-Hu-1 RBD variants and 100 higher-order Omicron BA.1/BA.2 variants, annotated with AlphaFold2 and ESMFold metrics and Bio2Byte sequence-based predictors. Structural descriptors—RMSD, TM-score, plDDT, solvent accessibility, hydrophobicity, aggregation propensity—are linked to ACE2 binding and expression data from deep mutational scanning. Provided as a FAIR 2 Data Package, it supports structure–function analysis, variant modeling, and responsible reuse in virology, structural biology, and computational protein science. This collaboration was co-funded by the PPP Allowance from Health ∼ Holland, Top Sector Life Sciences and Health, to stimulate public–private partnerships.

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