Mass Balance Approximation of Unfolding Improves Potential-Like Methods for Protein Stability Predictions

• URL: http://arxiv.org/abs/2504.06806v1
• Date: Wed, 09 Apr 2025 11:53:02 GMT
• Title: Mass Balance Approximation of Unfolding Improves Potential-Like Methods for Protein Stability Predictions
• Authors: Ivan Rossi, Guido Barducci, Tiziana Sanavia, Paola Turina, Emidio Capriotti, Piero Fariselli,
• Abstract summary: Deep-learning strategies have pushed the field forward, but their use in standard methods remains limited due to resource demands.<n>This study shows that incorporating a mass-balance correction (MBC) to account for the unfolded state significantly enhances these methods.<n>While many machine learning models partially model this balance, our analysis suggests that a refined representation of the unfolded state may improve the predictive performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The prediction of protein stability changes following single-point mutations plays a pivotal role in computational biology, particularly in areas like drug discovery, enzyme reengineering, and genetic disease analysis. Although deep-learning strategies have pushed the field forward, their use in standard workflows remains limited due to resource demands. Conversely, potential-like methods are fast, intuitive, and efficient. Yet, these typically estimate Gibbs free energy shifts without considering the free-energy variations in the unfolded protein state, an omission that may breach mass balance and diminish accuracy. This study shows that incorporating a mass-balance correction (MBC) to account for the unfolded state significantly enhances these methods. While many machine learning models partially model this balance, our analysis suggests that a refined representation of the unfolded state may improve the predictive performance.

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