Structure-based prediction of SARS-CoV-2 variant properties using machine learning on mutational neighborhoods
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资源简介：
This dataset provides a comprehensive analysis of protein mutations, detailing structural and energetic metrics such as RMSD, TM-score, SASA, and binding energies. It includes sequence identifiers, mutation sites, and various quantitative measures critical for understanding protein stability, folding, and interactions, particularly with the ACE-2 receptor. The data is structured to support FAIR principles, promoting interoperability and machine actionability.
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SENSCIENCE创建时间：
2025-09-09
