Version 2 2024-04-08, 16:39Version 2 2024-04-08, 16:39
Version 1 2024-02-22, 17:07Version 1 2024-02-22, 17:07
journal contribution
posted on 2024-04-08, 16:39authored byNiccolo
Alberto Elia Venanzi, Andrea Basciu, Attilio Vittorio Vargiu, Alexandros Kiparissides, Paul A. Dalby, Duygu Dikicioglu
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.