title: Development of an accurate classification system of proteins into structured and unstructured regions that uncovers novel structural domains: its application to human transcription factors.
abstract: BACKGROUND
  In addition to structural domains, most eukaryotic proteins possess intrinsically disordered (ID) regions. Although ID regions often play important functional roles, their accurate identification is difficult. As human transcription factors (TFs) constitute a typical group of proteins with long ID regions, we regarded them as a model of all proteins and attempted to accurately classify TFs into structural domains and ID regions. Although an extremely high fraction of ID regions besides DNA binding and/or other domains was detected in human TFs in our previous investigation, 20% of the residues were left unassigned. In this report, we exploit the generally higher sequence divergence in ID regions than in structural regions to completely divide proteins into structural domains and ID regions.
  
  
  RESULTS
  The new dichotomic system first identifies domains of known structures, followed by assignment of structural domains and ID regions with a combination of pre-existing tools and a newly developed program based on sequence divergence, taking un-aligned regions into consideration. The system was found to be highly accurate: its application to a set of proteins with experimentally verified ID regions had an error rate as low as 2%. Application of this system to human TFs (401 proteins) showed that 38% of the residues were in structural domains, while 62% were in ID regions. The preponderance of ID regions makes a sharp contrast to TFs of Escherichia coli (229 proteins), in which only 5% fell in ID regions. The method also revealed that 4.0% and 11.8% of the total length in human and E. coli TFs, respectively, are comprised of structural domains whose structures have not been determined.
  
  
  CONCLUSIONS
  The present system verifies that sequence divergence including information of unaligned regions is a good indicator of ID regions. The system for the first time estimates the complete fractioning of structured/un-structured regions in human TFs, also revealing structural domains without homology to known structures. These predicted novel structural domains are good targets of structural genomics. When applied to other proteins, the system is expected to uncover more novel structural domains.
authors.affiliation: Center for Information Biology & DNA Data Bank of Japan, National Institute of Genetics, Mishima, Shizuoka, Japan. sfukuchi@genes.nig.ac.jp
authors.forename: Satoshi
authors.initials: S
authors.lastname: Fukuchi
authors.forename: Keiichi
authors.initials: K
authors.lastname: Homma
authors.forename: Yoshiaki
authors.initials: Y
authors.lastname: Minezaki
authors.forename: Takashi
authors.initials: T
authors.lastname: Gojobori
authors.forename: Ken
authors.initials: K
authors.lastname: Nishikawa
mesh_terms: D001185:Artificial Intelligence; D001426:Bacterial Proteins; D019295:Computational Biology; D030562:Databases, Protein / Q000145:classification*; D006801:Humans; D010363:Pattern Recognition, Automated; D011485:Protein Binding; D011487:Protein Conformation; D017510:Protein Folding; D017434:Protein Structure, Tertiary; D020539:Sequence Analysis, Protein; D012984:Software; D013329:Structure-Activity Relationship; D014157:Transcription Factors / Q000737:chemistry* / Q000235:genetics
chemical_list: D001426:Bacterial Proteins; D014157:Transcription Factors