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DTL-DephosSite: Deep Transfer Learning Based Approach to Predict Dephosphorylation Sites

脱磷 磷酸丝氨酸 磷酸化 激酶 蛋白质磷酸化 磷酸酶 细胞生物学 生物 生物化学 蛋白质酪氨酸磷酸酶 计算生物学
作者
Meenal Chaudhari,Niraj Thapa,Hamid D. Ismail,Sandhya Chopade,Doina Caragea,Maja Köhn,Robert H. Newman,Dukka B. Kc
出处
期刊:Frontiers in Cell and Developmental Biology [Frontiers Media SA]
卷期号:9 被引量:11
标识
DOI:10.3389/fcell.2021.662983
摘要

Phosphorylation, which is mediated by protein kinases and opposed by protein phosphatases, is an important post-translational modification that regulates many cellular processes, including cellular metabolism, cell migration, and cell division. Due to its essential role in cellular physiology, a great deal of attention has been devoted to identifying sites of phosphorylation on cellular proteins and understanding how modification of these sites affects their cellular functions. This has led to the development of several computational methods designed to predict sites of phosphorylation based on a protein’s primary amino acid sequence. In contrast, much less attention has been paid to dephosphorylation and its role in regulating the phosphorylation status of proteins inside cells. Indeed, to date, dephosphorylation site prediction tools have been restricted to a few tyrosine phosphatases. To fill this knowledge gap, we have employed a transfer learning strategy to develop a deep learning-based model to predict sites that are likely to be dephosphorylated. Based on independent test results, our model, which we termed DTL-DephosSite, achieved efficiency scores for phosphoserine/phosphothreonine residues of 84%, 84% and 0.68 with respect to sensitivity (SN), specificity (SP) and Matthew’s correlation coefficient (MCC). Similarly, DTL-DephosSite exhibited efficiency scores of 75%, 88% and 0.64 for phosphotyrosine residues with respect to SN, SP, and MCC.
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