title: DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach.
abstract: The precise prediction of protein intrinsically disordered regions, which play a crucial role in biological procedures, is a necessary prerequisite to further the understanding of the principles and mechanisms of protein function. Here, we propose a novel predictor, DisoMCS, which is a more accurate predictor of protein intrinsically disordered regions. The DisoMCS bases on an original multi-class conservative score (MCS) obtained by sequence-order/disorder alignment. Initially, near-disorder regions are defined on fragments located at both the terminus of an ordered region connecting a disordered region. Then the multi-class conservative score is generated by sequence alignment against a known structure database and represented as order, near-disorder and disorder conservative scores. The MCS of each amino acid has three elements: order, near-disorder and disorder profiles. Finally, the MCS is exploited as features to identify disordered regions in sequences. DisoMCS utilizes a non-redundant data set as the training set, MCS and predicted secondary structure as features, and a conditional random field as the classification algorithm. In predicted near-disorder regions a residue is determined as an order or a disorder according to the optimized decision threshold. DisoMCS was evaluated by cross-validation, large-scale prediction, independent tests and CASP (Critical Assessment of Techniques for Protein Structure Prediction) tests. All results confirmed that DisoMCS was very competitive in terms of accuracy of prediction when compared with well-established publicly available disordered region predictors. It also indicated our approach was more accurate when a query has higher homologous with the knowledge database.
  
  
  BACKGROUND
  The DisoMCS is available at http://cal.tongji.edu.cn/disorder/.
authors.affiliation: Department of Chemistry, Tongji University, Shanghai, China.
authors.forename: Zhiheng
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Department of Chemistry, Tongji University, Shanghai, China.
authors.forename: Qianqian
authors.initials: Q
authors.lastname: Yang
authors.affiliation: Department of Chemistry, Tongji University, Shanghai, China.
authors.forename: Tonghua
authors.initials: T
authors.lastname: Li
authors.affiliation: Department of Chemistry, Tongji University, Shanghai, China.
authors.forename: Peisheng
authors.initials: P
authors.lastname: Cong
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D064267:Intrinsically Disordered Proteins / Q000737:chemistry*; D012372:ROC Curve; D015203:Reproducibility of Results; D012984:Software; D064878:Web Browser
chemical_list: D064267:Intrinsically Disordered Proteins