title: Simplified method for predicting a functional class of proteins in transcription factor complexes.
abstract: BACKGROUND
  Initiation of transcription is essential for most of the cellular responses to environmental conditions and for cell and tissue specificity. This process is regulated through numerous proteins, their ligands and mutual interactions, as well as interactions with DNA. The key such regulatory proteins are transcription factors (TFs) and transcription co-factors (TcoFs). TcoFs are important since they modulate the transcription initiation process through interaction with TFs. In eukaryotes, transcription requires that TFs form different protein complexes with various nuclear proteins. To better understand transcription regulation, it is important to know the functional class of proteins interacting with TFs during transcription initiation. Such information is not fully available, since not all proteins that act as TFs or TcoFs are yet annotated as such, due to generally partial functional annotation of proteins. In this study we have developed a method to predict, using only sequence composition of the interacting proteins, the functional class of human TF binding partners to be (i) TF, (ii) TcoF, or (iii) other nuclear protein. This allows for complementing the annotation of the currently known pool of nuclear proteins. Since only the knowledge of protein sequences is required in addition to protein interaction, the method should be easily applicable to many species.
  
  
  RESULTS
  Based on experimentally validated interactions between human TFs with different TFs, TcoFs and other nuclear proteins, our two classification systems (implemented as a web-based application) achieve high accuracies in distinguishing TFs and TcoFs from other nuclear proteins, and TFs from TcoFs respectively.
  
  
  CONCLUSIONS
  As demonstrated, given the fact that two proteins are capable of forming direct physical interactions and using only information about their sequence composition, we have developed a completely new method for predicting a functional class of TF interacting protein partners with high precision and accuracy.
authors.affiliation: King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center, Thuwal, Kingdom of Saudi Arabia.
authors.forename: Marek J
authors.initials: MJ
authors.lastname: Piatek
authors.forename: Michael C
authors.initials: MC
authors.lastname: Schramm
authors.forename: Dharani D
authors.initials: DD
authors.lastname: Burra
authors.forename: Abdulaziz
authors.initials: A
authors.lastname: Binshbreen
authors.forename: Boris R
authors.initials: BR
authors.lastname: Jankovic
authors.forename: Rajesh
authors.initials: R
authors.lastname: Chowdhary
authors.forename: John A C
authors.initials: JA
authors.lastname: Archer
authors.forename: Vladimir B
authors.initials: VB
authors.lastname: Bajic
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D046912:Multiprotein Complexes / Q000378:metabolism*; D011485:Protein Binding; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D046912:Multiprotein Complexes; D014157:Transcription Factors