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

Frontiers in Bioinformatics, vol. 5, pp. 1634111

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 FAIR2 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.

Authors 3

1. Max van den Boom Aachen Metabolics and Analytics Centre Leiden Academic Centre for Drug Research

   Leiden University · Vrije Universiteit Amsterdam

   Affiliation as printed

   Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

   Metabolics and Analytics Centre, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, Netherlands

2. Erik Schultes corresponding Aachen Metabolics and Analytics Centre Leiden Academic Centre for Drug Research

   Leiden University · GO FAIR Foundation

   Affiliation as printed

   GO FAIR Foundation, Leiden, Netherlands

   Metabolics and Analytics Centre, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, Netherlands

3. Thomas Hankemeier Aachen Metabolics and Analytics Centre Leiden Academic Centre for Drug Research

   Leiden University

   Affiliation as printed

   Metabolics and Analytics Centre, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, Netherlands