sprotp a web server to recognize those short-lived proteins based on sequence-derived features in human cellssprotp web服务器来识别那些短暂的蛋白质根据sequence-derived特性在人类细胞.pdf

SProtP: A Web Server to Recognize Those Short-Lived Proteins Based on Sequence-Derived Features in Human Cells 1 2 1 2 1 3 2 Xiaofeng Song , Tao Zhou , Hao Jia , Xuejiang Guo *, Xiaobai Zhang , Ping Han *, Jiahao Sha 1 Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China, 2 State Key Laboratory of Reproductive Medicine, Department of Histology and Embryology, Nanjing Medical University, Nanjing, China, 3 Department of Gynecology and Obstetrics, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China 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