Prediction of aggregation in monoclonal antibodies from molecular surface curvature
Abstract Protein aggregation is one of the key challenges in the biopharmaceutical industry as its control is crucial in achieving long-term stability and efficacy of biopharmaceuticals. Attempts have been made to develop regression models for predicting the aggregation of monoclonal antibodies in s...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-13527-w |
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| author | Benjamin Knez Lara Erzin Žiga Kos Drago Kuzman Miha Ravnik |
| author_facet | Benjamin Knez Lara Erzin Žiga Kos Drago Kuzman Miha Ravnik |
| author_sort | Benjamin Knez |
| collection | DOAJ |
| description | Abstract Protein aggregation is one of the key challenges in the biopharmaceutical industry as its control is crucial in achieving long-term stability and efficacy of biopharmaceuticals. Attempts have been made to develop regression models for predicting the aggregation of monoclonal antibodies in solution using machine learning methods. These efforts have yielded varying levels of success, with current state-of-the-art AI approaches achieving good prediction accuracies ( $$r=0.86$$ ). Here, we demonstrate the prediction of aggregation rate in monoclonal antibodies with beyond state-of-the-art reliability using a coupled AI-MD-Molecular surface curvature modelling platform. The scientific novelty of this approach lies in using local geometrical surface curvature of proteins as the core element for protein stability analysis. By combining local surface curvature and hydrophobicity, as derived from time-dependent MD simulations, we are able to construct aggregation predictive features that, when coupled with linear regression machine learning techniques, give a high prediction accuracy ( $$r=0.91$$ ) on a dataset of 20 molecules. More generally, this approach shows significant potential for quantitative in silico screening and prediction of protein aggregation, which is of great scientific and industrial relevance, particularly in biopharmaceutics. |
| format | Article |
| id | doaj-art-e14f5c846dca4c928041c3533e981efa |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e14f5c846dca4c928041c3533e981efa2025-08-20T03:04:38ZengNature PortfolioScientific Reports2045-23222025-08-0115111110.1038/s41598-025-13527-wPrediction of aggregation in monoclonal antibodies from molecular surface curvatureBenjamin Knez0Lara Erzin1Žiga Kos2Drago Kuzman3Miha Ravnik4Novartis LLCFaculty of Mathematics and Physics, University of LjubljanaFaculty of Mathematics and Physics, University of LjubljanaNovartis LLCFaculty of Mathematics and Physics, University of LjubljanaAbstract Protein aggregation is one of the key challenges in the biopharmaceutical industry as its control is crucial in achieving long-term stability and efficacy of biopharmaceuticals. Attempts have been made to develop regression models for predicting the aggregation of monoclonal antibodies in solution using machine learning methods. These efforts have yielded varying levels of success, with current state-of-the-art AI approaches achieving good prediction accuracies ( $$r=0.86$$ ). Here, we demonstrate the prediction of aggregation rate in monoclonal antibodies with beyond state-of-the-art reliability using a coupled AI-MD-Molecular surface curvature modelling platform. The scientific novelty of this approach lies in using local geometrical surface curvature of proteins as the core element for protein stability analysis. By combining local surface curvature and hydrophobicity, as derived from time-dependent MD simulations, we are able to construct aggregation predictive features that, when coupled with linear regression machine learning techniques, give a high prediction accuracy ( $$r=0.91$$ ) on a dataset of 20 molecules. More generally, this approach shows significant potential for quantitative in silico screening and prediction of protein aggregation, which is of great scientific and industrial relevance, particularly in biopharmaceutics.https://doi.org/10.1038/s41598-025-13527-w |
| spellingShingle | Benjamin Knez Lara Erzin Žiga Kos Drago Kuzman Miha Ravnik Prediction of aggregation in monoclonal antibodies from molecular surface curvature Scientific Reports |
| title | Prediction of aggregation in monoclonal antibodies from molecular surface curvature |
| title_full | Prediction of aggregation in monoclonal antibodies from molecular surface curvature |
| title_fullStr | Prediction of aggregation in monoclonal antibodies from molecular surface curvature |
| title_full_unstemmed | Prediction of aggregation in monoclonal antibodies from molecular surface curvature |
| title_short | Prediction of aggregation in monoclonal antibodies from molecular surface curvature |
| title_sort | prediction of aggregation in monoclonal antibodies from molecular surface curvature |
| url | https://doi.org/10.1038/s41598-025-13527-w |
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