Leveraging Protein Dynamics to Identify Functional Phosphorylation Sites using Deep Learning Models

Accurate prediction of post-translational modifications
(PTMs)
is of great significance in understanding cellular processes, by modulating
protein structure and dynamics. Nowadays, with the rapid growth of
protein data at different “omics” levels, machine learning
models largely enriched the prediction of PTMs. However, most machine
learning models only rely on protein sequence and little structural
information. The lack of the systematic dynamics analysis underlying
PTMs largely limits the PTM functional predictions. In this research,
we present two dynamics-centric deep learning models, namely, cDL-PAU
and cDL-FuncPhos, by incorporating sequence, structure, and dynamics-based
features to elucidate the molecular basis and underlying functional
landscape of PTMs. cDL-PAU achieved satisfactory area under the curve
(AUC) scores of 0.804–0.888 for predicting phosphorylation,
acetylation, and ubiquitination (PAU) sites, while cDL-FuncPhos achieved
an AUC value of 0.771 for predicting functional phosphorylation (FuncPhos)
sites, displaying reliable improvements. Through a feature selection,
the dynamics-based coupling and commute ability show large contributions
in discovering PAU sites and FuncPhos sites, suggesting the allosteric
propensity for important PTMs. The application of cDL-FuncPhos in
three oncoproteins not only corroborates its strong performance in
FuncPhos prioritization but also gains insight into the physical basis
for the functions. The source code and data set of cDL-PAU and cDL-FuncPhos
are available at https://github.com/ComputeSuda/PTM_ML.