title: SProtP: a web server to recognize those short-lived proteins based on sequence-derived features in human cells.
abstract: Protein turnover metabolism plays important roles in cell cycle progression, signal transduction, and differentiation. Those proteins with short half-lives are involved in various regulatory processes. To better understand the regulation of cell process, it is important to study the key sequence-derived factors affecting short-lived protein degradation. Until now, most of protein half-lives are still unknown due to the difficulties of traditional experimental methods in measuring protein half-lives in human cells. To investigate the molecular determinants that affect short-lived proteins, a computational method was proposed in this work to recognize short-lived proteins based on sequence-derived features in human cells. In this study, we have systematically analyzed many features that perhaps correlated with short-lived protein degradation. It is found that a large fraction of proteins with signal peptides and transmembrane regions in human cells are of short half-lives. We have constructed an SVM-based classifier to recognize short-lived proteins, due to the fact that short-lived proteins play pivotal roles in the control of various cellular processes. By employing the SVM model on human dataset, we achieved 80.8% average sensitivity and 79.8% average specificity, respectively, on ten testing dataset (TE1-TE10). We also obtained 89.9%, 99% and 83.9% of average accuracy on an independent validation datasets iTE1, iTE2 and iTE3 respectively. The approach proposed in this paper provides a valuable alternative for recognizing the short-lived proteins in human cells, and is more accurate than the traditional N-end rule. Furthermore, the web server SProtP (http://reprod.njmu.edu.cn/sprotp) has been developed and is freely available for users.
authors: Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
authors: Xiaofeng
authors: X
authors: Song
authors: Tao
authors: T
authors: Zhou
authors: Hao
authors: H
authors: Jia
authors: Xuejiang
authors: X
authors: Guo
authors: Xiaobai
authors: X
authors: Zhang
authors: Ping
authors: P
authors: Han
authors: Jiahao
authors: J
authors: Sha
mesh_terms: D030562:Databases, Protein; D006801:Humans; D020407:Internet; D011506:Proteins / Q000378:metabolism*; D059748:Proteolysis; D020539:Sequence Analysis, Protein; D012984:Software
chemical_list: D011506:Proteins