Computational prediction of MoRFs based on protein sequences and minimax probability machine
基于蛋白质序列和minimax概率机的MoRFs计算预测
作者: Hao HeJiaxiang ZhaoGuiling Sun
作者单位: 1College of Electronic Information and Optical Engineering, Nankai University, Tianjin, China
刊名: BMC Bioinformatics, 2019, Vol.20 (13), pp.1182-1189
来源数据库: Springer Nature Journal
DOI: 10.1186/s12859-019-3111-z
关键词: Molecular recognition featuresIntrinsically disordered proteinsMinimax probability machine
英文摘要: Abstract(#br)Background(#br)Molecular recognition features (MoRFs) are one important type of disordered segments that can promote specific protein-protein interactions. They are located within longer intrinsically disordered regions (IDRs), and undergo disorder-to-order transitions upon binding to their interaction partners. The functional importance of MoRFs and the limitation of experimental identification make it necessary to predict MoRFs accurately with computational methods. Results(#br)In this study, a new sequence-based method, named as MoRFMPM, is proposed for predicting MoRFs. MoRFMPM uses minimax probability machine (MPM) to predict MoRFs based on 16 features and 3 different windows, which neither relying on other predictors nor calculating the properties...
全文获取路径: Springer Nature  (合作)
分享到:
来源刊物:
影响因子:3.024 (2012)

×