MoRFPred-plus: Computational Identification of MoRFs in Protein Sequences using Physicochemical Properties and HMM profiles
作者: Ronesh SharmaMaitsetseg BayarjargalTatsuhiko TsunodaAshwini PatilAlok Sharma
作者单位: 1Department of Electronics Engineering, Fiji National University, Suva, Fiji
2Department of Engineering and Physics, the University of the South Pacific, Suva, Fiji
3Department of Health Science, Fiji National University, Fiji
4CREST, JST, Yokohama 230-0045, Japan
5RIKEN Center for Integrative Medical Science, Yokohama 230-0045, Japan
6Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
7Griffith University, Australia
刊名: Journal of Theoretical Biology, 2018, Vol.437 , pp.9-16
来源数据库: Elsevier Journal
DOI: 10.1016/j.jtbi.2017.10.015
关键词: Intrinsically disordered proteinsMolecular recognition featureHidden Markov modelSupport vector machine
原始语种摘要: Abstract(#br)Motivation. Intrinsically Disordered Proteins (IDPs) lack stable tertiary structure and they actively participate in performing various biological functions. These IDPs expose short binding regions called Molecular Recognition Features (MoRFs) that permit interaction with structured protein regions. Upon interaction they undergo a disorder-to-order transition as a result of which their functionality arises. Predicting these MoRFs in disordered protein sequences is a challenging task.(#br)Method. In this study, we present MoRFpred-plus, an improved predictor over our previous proposed predictor to identify MoRFs in disordered protein sequences. Two separate independent propensity scores are computed via incorporating physicochemical properties and HMM profiles, these scores...
全文获取路径: Elsevier  (合作)
影响因子:2.351 (2012)

  • query 查询
  • recognition 识别
  • feature 结构元件
  • Recognition 识别
  • computational 计算的
  • averaging 求平均数
  • machine 机器
  • residue 余渣
  • propensity 晶癖
  • predictor 预示