Computational Prediction of MoRFs, Short Disorder-to-order Transitioning Protein Binding Regions
作者: Akila KatuwawalaZhenling PengJianyi YangLukasz Kurgan
作者单位: 1Department of Computer Science, Virginia Commonwealth University, USA
2Center for Applied Mathematics, Tianjin University, Tianjin, China
3School of Mathematical Sciences, Nankai University, Tianjin, China
刊名: Computational and Structural Biotechnology Journal, 2019, Vol.17 , pp.454-462
来源数据库: Elsevier Journal
DOI: 10.1016/j.csbj.2019.03.013
关键词: Intrinsic disorderIntrinsically disordered regionsMolecular recognition featuresDisordered protein bindingShort linear motifsSemi-disorderProtein-protein interactions
原始语种摘要: Abstract(#br)Molecular recognition features (MoRFs) are short protein-binding regions that undergo disorder-to-order transitions (induced folding) upon binding protein partners. These regions are abundant in nature and can be predicted from protein sequences based on their distinctive sequence signatures. This first-of-its-kind survey covers 14 MoRF predictors and six related methods for the prediction of short protein-binding linear motifs, disordered protein-binding regions and semi-disordered regions. We show that the development of MoRF predictors has accelerated in the recent years. These predictors depend on machine learning-derived models that were generated using training datasets where MoRFs are annotated using putative disorder. Our analysis reveals that they generate accurate...
全文获取路径: Elsevier  (合作)

  • disorder 无序
  • protein 蛋白质
  • recognition 识别
  • features 特征
  • binding 装订
  • linear 线形的