Mass Balance Approximation of Unfolding Improves Potential-Like Methods for Protein Stability Predictions
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915266434170880 |
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| author | Rossi, Ivan Barducci, Guido Sanavia, Tiziana Turina, Paola Capriotti, Emidio Fariselli, Piero |
| author_facet | Rossi, Ivan Barducci, Guido Sanavia, Tiziana Turina, Paola Capriotti, Emidio Fariselli, Piero |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06806 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mass Balance Approximation of Unfolding Improves Potential-Like Methods for Protein Stability Predictions Rossi, Ivan Barducci, Guido Sanavia, Tiziana Turina, Paola Capriotti, Emidio Fariselli, Piero Quantitative Methods Machine Learning Biological Physics 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. |
| title | Mass Balance Approximation of Unfolding Improves Potential-Like Methods for Protein Stability Predictions |
| topic | Quantitative Methods Machine Learning Biological Physics |
| url | https://arxiv.org/abs/2504.06806 |