Increasing Coverage of Transcription Factor Position Weight Matrices through Domain-level Homology

Transcription factor-DNA interactions, central to cellular regulation and control, are commonly described by position weight matrices (PWMs). These matrices are frequently used to predict transcription factor binding sites in regulatory regions of DNA to complement and guide further experimental investigation. The DNA sequence preferences of transcription factors, encoded in PWMs, are dictated primarily by select residues within the DNA binding domain(s) that interact directly with DNA. Therefore, the DNA binding properties of homologous transcription factors with identical DNA binding domains may be characterized by PWMs derived from different species. Accordingly, we have implemented a fully automated domain-level homology searching method for identical DNA binding sequences. By applying the domain-level homology search to transcription factors with existing PWMs in the JASPAR and TRANSFAC databases, we were able to significantly increase coverage in terms of the total number of PWMs associated with a given species, assign PWMs to transcription factors that did not previously have any associations, and increase the number of represented species with PWMs over an order of magnitude. Additionally, using protein binding microarray (PBM) data, we have validated the domain-level method by demonstrating that transcription factor pairs with matching DNA binding domains exhibit comparable DNA binding specificity predictions to transcription factor pairs with completely identical sequences. The increased coverage achieved herein demonstrates the potential for more thorough species-associated investigation of protein-DNA interactions using existing resources. The PWM scanning results highlight the challenging nature of transcription factors that contain multiple DNA binding domains, as well as the impact of motif discovery on the ability to predict DNA binding properties. The method is additionally suitable for identifying domain-level homology mappings to enable utilization of additional information sources in the study of transcription factors. The domain-level homology search method, resulting PWM mappings, web-based user interface, and web API are publicly available at http://dodoma.systemsbiology.netdodoma.systemsbiology.net.

[1]  H. Lähdesmäki,et al.  A Linear Model for Transcription Factor Binding Affinity Prediction in Protein Binding Microarrays , 2011, PloS one.

[2]  L. Holm,et al.  The Pfam protein families database , 2005, Nucleic Acids Res..

[3]  David J. Arenillas,et al.  JASPAR 2010: the greatly expanded open-access database of transcription factor binding profiles , 2009, Nucleic Acids Res..

[4]  Amos Bairoch,et al.  PROSITE, a protein domain database for functional characterization and annotation , 2009, Nucleic Acids Res..

[5]  Baris E. Suzek,et al.  The Universal Protein Resource (UniProt) in 2010 , 2009, Nucleic Acids Res..

[6]  María Martín,et al.  The Universal Protein Resource (UniProt) in 2010 , 2010 .

[7]  Juan M. Vaquerizas,et al.  A census of human transcription factors: function, expression and evolution , 2009, Nature Reviews Genetics.

[8]  Anthony A. Philippakis,et al.  Predicting the binding preference of transcription factors to individual DNA k-mers , 2009, Bioinform..

[9]  Peer Bork,et al.  SMART 6: recent updates and new developments , 2008, Nucleic Acids Res..

[10]  Robert D. Finn,et al.  InterPro: the integrative protein signature database , 2008, Nucleic Acids Res..

[11]  Martha L. Bulyk,et al.  UniPROBE: an online database of protein binding microarray data on protein–DNA interactions , 2008, Nucleic Acids Res..

[12]  E. Birney,et al.  Pfam: the protein families database , 2013, Nucleic Acids Res..

[13]  H. Lähdesmäki,et al.  Probabilistic Inference of Transcription Factor Binding from Multiple Data Sources , 2008, PloS one.

[14]  Xiaoyu Chen,et al.  RankMotif++: a motif-search algorithm that accounts for relative ranks of K-mers in binding transcription factors , 2007, ISMB/ECCB.

[15]  A. Philippakis,et al.  Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities , 2006, Nature Biotechnology.

[16]  Alexandre V. Morozov,et al.  Statistical mechanical modeling of genome-wide transcription factor occupancy data by MatrixREDUCE , 2006, ISMB.

[17]  Alexander E. Kel,et al.  TRANSFAC® and its module TRANSCompel®: transcriptional gene regulation in eukaryotes , 2005, Nucleic Acids Res..

[18]  G. Crooks,et al.  WebLogo: a sequence logo generator. , 2004, Genome research.

[19]  A. Sandelin,et al.  Applied bioinformatics for the identification of regulatory elements , 2004, Nature Reviews Genetics.

[20]  Kathleen Marchal,et al.  INCLUSive: INtegrated Clustering, Upstream sequence retrieval and motif Sampling , 2002, Bioinform..

[21]  G. Church,et al.  Identifying regulatory networks by combinatorial analysis of promoter elements , 2001, Nature Genetics.

[22]  Xin Chen,et al.  The TRANSFAC system on gene expression regulation , 2001, Nucleic Acids Res..

[23]  Gary D. Stormo,et al.  DNA binding sites: representation and discovery , 2000, Bioinform..

[24]  Andreas Wagner,et al.  Genes regulated cooperatively by one or more transcription factors and their identification in whole eukaryotic genomes , 1999, Bioinform..

[25]  J Schultz,et al.  SMART, a simple modular architecture research tool: identification of signaling domains. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[26]  Thomas L. Madden,et al.  Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. , 1997, Nucleic acids research.

[27]  P. V. von Hippel,et al.  Selection of DNA binding sites by regulatory proteins. Statistical-mechanical theory and application to operators and promoters. , 1987, Journal of molecular biology.