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.

精确预测翻译后修饰（PTMs）对于理解细胞过程具有重要意义，因其可调节蛋白质的结构与动态。随着不同“组学”层面蛋白质数据的迅速增长，机器学习模型在很大程度上丰富了PTMs的预测。然而，大多数机器学习模型仅依赖于蛋白质序列，而忽略了少量结构信息。PTMs系统性动态分析的缺失在很大程度上限制了PTMs功能预测的准确性。在本研究中，我们提出了两种以动态为核心深度学习模型，即cDL-PAU和cDL-FuncPhos，通过整合序列、结构和基于动态的特征，以阐明PTMs的分子基础及其潜在的功能景观。cDL-PAU在预测磷酸化、乙酰化和泛素化（PAU）位点方面实现了令人满意的曲线下面积（AUC）分数，介于0.804至0.888之间，而cDL-FuncPhos在预测功能磷酸化（FuncPhos）位点方面实现了0.771的AUC值，显示出可靠的改进。通过特征选择，基于动态的耦合和交换能力在发现PAU位点和FuncPhos位点中贡献显著，暗示了重要PTMs的变构倾向。cDL-FuncPhos在三个肿瘤蛋白中的应用不仅证实了其在FuncPhos优先级排序中的强大性能，还揭示了其功能的物理基础。cDL-PAU和cDL-FuncPhos的源代码和数据集可在https://github.com/ComputeSuda/PTM_ML获取。