20.500.12556/RUL-174676 Prediction of aggregation in monoclonal antibodies from molecular surface curvature 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. biophysics biopharmaceutics proteins monoclonal antibidies aggregation biofizika proteini monoklonska protitelesa agregacija true false true Angleški jezik Slovenski jezik Članek v reviji 2025-10-08 15:50:52 2025-10-08 15:50:53 2025-11-27 03:34:30 0000-00-00 00:00:00 2025 0 0 11 str. art. no. 28266 Vol. 15 Aug. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 577.322 2045-2322 10.1038/s41598-025-13527-w 252359939 s41598-025-13527-w.pdf s41598-025-13527-w.pdf 1 1425015CCF2765930640710816F52824 2d260c5f33d5a554eefa9a618d245f2e126acb20849fbb1cb136dd81e402b37a f7f7e611-a44e-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=219546 https://www.nature.com/articles/s41598-025-13527-w 0 12230957-a44f-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=219547 Fakulteta za matematiko in fiziko 0 0 0