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Article Abstract
While predicting structure-function relationships from sequence data is fundamental in biophysical chemistry, identifying prospective single-point and collective mutation sites in proteins can help us stay ahead in understanding their potential effects on protein structure and function. Addressing the challenges of large sequence-space analysis, we present EVOLVE, a web tool enabling researchers to explore prospective mutation sites and their collective behavior. EVOLVE integrates a statistical mechanics-guided machine learning algorithms to predict probable mutational sites, with statistical mechanics calculating mutational entropy to accurately identify mutational hotspots. Validation against a number of viral protein sequences confirms its ability to predict mutations and their functional consequences. By leveraging statistical mechanics of phase transition concept, EVOLVE also quantifies mutational entropy fluctuations, offering a quantitative foundation for identifying Variants of Concern (VOC) or Variants under Monitoring (VUM) as per World Health Organization (WHO) guidelines. EVOLVE streamlines data upload and analysis with a user-friendly interface and comprehensive tutorials. Access EVOLVE free at https://evolve-iiserkol.com.
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