Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants
Niccolò Alberto Elia Venanzia, Andrea Basciub, Attilio Vittorio Vargiub, Alexandros Kiparissidesa c, Paul Anthony Dalbya, Duygu Dikicioǧlua✉
- aDepartment of Biochemical Engineering, University College London, Gower Street, WC1E 6BT London, U.K
- bDepartment of Physics, University of Cagliari, Cittadella Universitaria, I-09042 Monserrato, Cagliari, Italy
- cDepartment of Chemical Engineering, Aristotle University of Thessaloniki, 54 124 Thessaloniki, Greece
- ✉Corresponding author
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Abstract
Despite recent advances in computational protein science, the dynamic behavior of proteins, which directly governs their biological activity, cannot be gleaned from sequence information alone. To overcome this challenge, we propose a framework that integrates the peptide sequence, protein structure, and protein dynamics descriptors into machine learning algorithms to enhance their predictive capabilities and achieve improved prediction of the protein variant function. The resulting machine learning pipeline integrates traditional sequence and structure information with molecular dynamics simulation data to predict the effects of multiple point mutations on the fold improvement of the activity of bovine enterokinase variants. This study highlights how the combination of structural and dynamic data can provide predictive insights into protein functionality and address protein engineering challenges in industrial contexts.
TL;DRA framework that integrates the peptide sequence, protein structure, and protein dynamics descriptors into machine learning algorithms to enhance their predictive capabilities and achieve improved prediction of the protein variant function is proposed. (Semantic Scholar)