title: An affinity-structure database of helix-turn-helix: DNA complexes with a universal coordinate system.
abstract: BACKGROUND
  Molecular interactions between proteins and DNA molecules underlie many cellular processes, including transcriptional regulation, chromosome replication, and nucleosome positioning. Computational analyses of protein-DNA interactions rely on experimental data characterizing known protein-DNA interactions structurally and biochemically. While many databases exist that contain either structural or biochemical data, few integrate these two data sources in a unified fashion. Such integration is becoming increasingly critical with the rapid growth of structural and biochemical data, and the emergence of algorithms that rely on the synthesis of multiple data types to derive computational models of molecular interactions.
  
  
  METHODS
  We have developed an integrated affinity-structure database in which the experimental and quantitative DNA binding affinities of helix-turn-helix proteins are mapped onto the crystal structures of the corresponding protein-DNA complexes. This database provides access to: (i) protein-DNA structures, (ii) quantitative summaries of protein-DNA binding affinities using position weight matrices, and (iii) raw experimental data of protein-DNA binding instances. Critically, this database establishes a correspondence between experimental structural data and quantitative binding affinity data at the single basepair level. Furthermore, we present a novel alignment algorithm that structurally aligns the protein-DNA complexes in the database and creates a unified residue-level coordinate system for comparing the physico-chemical environments at the interface between complexes. Using this unified coordinate system, we compute the statistics of atomic interactions at the protein-DNA interface of helix-turn-helix proteins. We provide an interactive website for visualization, querying, and analyzing this database, and a downloadable version to facilitate programmatic analysis.
  
  
  CONCLUSIONS
  This database will facilitate the analysis of protein-DNA interactions and the development of programmatic computational methods that capitalize on integration of structural and biochemical datasets. The database can be accessed at http://ProteinDNA.hms.harvard.edu.
authors.affiliation: Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA. alquraishi@hms.harvard.edu.|HMS Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Avenue, Boston, MA, 02115, USA. alquraishi@hms.harvard.edu.
authors.forename: Mohammed
authors.initials: M
authors.lastname: AlQuraishi
authors.affiliation: Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA.|HMS Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Avenue, Boston, MA, 02115, USA.
authors.forename: Shengdong
authors.initials: S
authors.lastname: Tang
authors.affiliation: Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA.|HMS Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Avenue, Boston, MA, 02115, USA.
authors.forename: Xide
authors.initials: X
authors.lastname: Xia
mesh_terms: D000465:Algorithms; D004247:DNA / Q000235:genetics*; D004268:DNA-Binding Proteins / Q000235:genetics* / Q000378:metabolism; D016208:Databases, Factual; D014157:Transcription Factors
chemical_list: D004268:DNA-Binding Proteins; D014157:Transcription Factors; D004247:DNA