Insights in Protein–DNA Communications through Composition Network Examination R. Sathyapriya. ¤, Meters. S. Vijayabaskar., Saraswathi Vishveshwara* Molecular Biophysics Unit, Of india Institute of Science, Bangalore, India
Protein–DNA relationships are crucial for several cellular procedures. Now with the increased availability of structures of protein–DNA complexes, gaining further insights into the nature of protein–DNA relationships has become possible. Earlier, brought on have characterized the software properties by considering pairwise interactions. Nevertheless , the information communicated along the cadre is almost never a pairwise phenomenon, and we feel that a worldwide picture can be obtained by looking at a protein–DNA complex as a network of noncovalently bonding systems. Furthermore, most of the before investigations have been completely carried out from the protein standpoint (protein-centric), as well as the present network approach should combine the two protein-centric as well as the DNA-centric parts of view. Section of the study entails the development of strategy to investigate protein–DNA graphs/networks together with the development of key parameters. A network manifestation provides a all natural view with the interacting surface and has become reported here for the first time. The second part of the research involves the analyses of these graphs with regards to clusters of interacting elements and the identification of very connected elements (hubs) along the protein–DNA interface. A predominance of deoxyribose–amino acid groupings in b-sheet proteins, variation of the program clusters in helix–turn–helix, plus the zipper-type healthy proteins would not have already been possible simply by conventional pairwise interaction evaluation. Additionally , we all propose a potential classification scheme for a set of protein–DNA processes on the basis of the protein–DNA user interface clusters. This provides a general idea of how the healthy proteins interact with the various components of DNA in different processes. Thus, we expect that the present graph-based method provides a deeper insight into the analysis in the protein–DNA recognition mechanisms simply by throwing even more light around the nature as well as the specificity of the interactions. Citation: Sathyapriya L, Vijayabaskar MS, Vishveshwara T (2008) Observations into Protein–DNA Interactions through Structure Network Analysis. PLoS Comput Biol 4(9): e1000170. doi: 15. 1371/journal. pcbi. 1000170 Manager: Ruth Nussinov, National Malignancy Institute, Usa and Tel Aviv University, Israel Received January 10, 2008; Recognized July 29, 2008; Published September a few, 2008 Copyright: ß 2008 Sathyapriya et al. This is certainly an open-access article allocated under the the Creative Commons Attribution Permit, which permits unrestricted work with, distribution, and reproduction in different medium, provided the original creator and origin are a certain amount. Funding: Department of Biotechnology, Government of India, support for Basic Biological Study Competing Passions: The experts have declared that zero competing passions exist. 2. E-mail: [email protected] iisc. ernet. in ¤ Current treat: Max Planck Institute for Molecular Genetics, Berlin, Indonesia. These authors contributed equally to this operate.
A network of interactions among the macromolecules drives the cell. The protein–DNA interactions orchestrate the excessive fidelity procedures like GENETICS recombination, DNA replication, and transcription. With all the increasing volume of high-resolution structures of macromolecular complexes, it is now possible to get insights into the atomic details of interactions regulating their structural and practical integrity. In our study, all of us focus on protein–DNA interactions, that may either end up being specific or nonspecific with regards to the functional need. Insights into the mechanism of protein–DNA capturing and identification have come from extensive examination of protein–DNA interfaces [1– 14]. Some of these brought on have been performed at the level of...
References: 1 ) Luscombe NM, Thornton JM (2002) Protein–DNA interactions: protein conservation plus the effects of mutations on holding specificity. T Mol Biol 320: 991–1009. 2 . Lustig B, Jernigan RL (1995) Consistencies of individual DNA base–amino chemical p interactions in structures and sequences. Nucleic Acids Vaca 23: 4707–4711. 3. Prabakaran P, Siebers JG, Ahmad S, Gromiha MM, Singarayan MG, et al. (2006) Classification of protein-DNA processes based on structural descriptors. Composition 14: 1355–1367. 4. Sathyapriya R, Brinda KV, Vishveshwara S (2006) Correlation from the side-chain hubs with the efficient residues in DNA holding protein structures. J Chem Inf Model 46: 123–129. 5. Luscombe NM, Laskowski RA, Thornton JM (2001) Amino acid–base interactions: a three-dimensional research of protein–DNA interactions at an atomic level. Nucleic Acids Res up to 29: 2860–2874. 6th. Jones S, van Heyningen P, Berman HM, Thornton JM (1999) Protein-DNA communications: a strength analysis. J Mol Biol 287: 877–896. 7. Siggers TW, Silkov A, Honig B (2005) Structural positioning of protein–DNA interfaces: observations into the determinants of holding specificity. J Mol Biol 345: 1027–1045. 8. Ahmad S, Kono H, Arauzo-Bravo MJ, Sarai A (2006) ReadOut: structure-based calculation of direct and indirect monitor energies and specificities pertaining to protein– GENETICS recognition. Nucleic Acids Vaca 34: W124–W127. 9. Baker CM, Give GH (2007) Role of aromatic proteins in protein-nucleic acid acknowledgement. Biopolymers 85: 456–470. twelve. Coulocheri SOCIAL FEAR, Pigis DG, Papavassiliou KA, Papavassiliou AG (2007) Hydrogen bonds in protein–DNA complexes: where geometry meets plasticity. Biochimie 89: 1291–1303. 14. Lejeune G, Delsaux D, Charloteaux B, Thomas A, Brasseur L (2005) Proteinnucleic acid reputation: statistical examination of atomic interactions and influence of DNA structure. Proteins sixty one: 258–271. 12. Gromiha LOGISTIK, Siebers JG, Selvaraj H, Kono H, Sarai A (2005) Role of inter and intramolecular interactions in protein–DNA identification. Gene 364: 108–113. 13. Kono L, Sarai A (1999) Structure-based prediction of DNA focus on sites by regulatory healthy proteins. Proteins thirty five: 114–131. 13. Gromiha MM, Siebers JG, Selvaraj H, Kono H, Sarai A (2004) Intermolecular and intramolecular readout components in protein–DNA recognition. T Mol Biol 337: 285–294. 15. Kannan N, Vishveshwara S (1999) Identification of side-chain clusters in protein structures with a graph unreal method. J Mol Biol 292: 441–464. 16. Patra SM, Vishveshwara S (2000) Backbone bunch identification in proteins with a graph theoretical method. Biophys Chem 84: 13–25. 17. Vendruscolo M, Paci At the, Dobson CENTIMETER, Karplus Meters (2001) Three key elements form a crucial contact network in a necessary protein folding move state. Nature 409: 641–645. 18. Greene LH, Higman VA (2003) Uncovering network systems within just protein set ups. J Mol Biol 334: 781–791. 19. Atilgan AREAL, Turgut Deb, Atilgan C (2007) Screened non-bonded connections in local proteins shape optimal pathways for powerful residue communication. Biophys J 92: 3052–3062. 20. Alter S, Jiao X, Li CH, Gong XQ, Chen WZ, ou al. (2008) Amino acid network and its credit scoring application in protein–protein docking. Biophys Chem 134: 111–118. 21. de Sol A, O'Meara G (2005) Small-world network method to identify important residues in protein-protein discussion. Proteins fifty eight: 672–682. twenty-two. Brinda KV, Kannan In, Vishveshwara S (2002) Analysis of homodimeric protein cadre by graph-spectral methods. Necessary protein Eng 12-15: 265–277. 3. Brinda KAVIAR, Vishveshwara H (2005) A network manifestation of necessary protein structures: effects for proteins stability. Biophys J fifth 89: 4159–4170. twenty-four. Sen TANGZHOU, Kloczkowski A, Jernigan RL (2006) A DNA-centric check out proteinDNA complexes. Structure 16: 1341–1342. 25. Luscombe NM, Austin SE, Berman HM, Thornton JM (2000) A review of the buildings of protein-DNA complexes. Genome Biol you: REVIEWS001. 26. Juo ZS, Chiu TK, Leiberman PM, Baikalov I, Berk AJ, et approach. (1996) Just how proteins identify the STRUKTUR box. L Mol Biol 261: 239–254. 27. Prelado N, Casta?o L, Weinstein H (1997) Does TATA matter? A structural hunt for the selectivity determinants in its complexes with TATA boxbinding protein. Biophys J 73: 640–652. twenty-eight. Zhao Back button, Herr Watts (2002) A regulated two-step mechanism of TBP binding to DNA: a solvent-exposed surface of TBP inhibits TATA package recognition. Cellular 108: 615–627. 29. Wintjens R, Rooman M (1996) Structural classification of HTH DNA-binding domains and protein–DNA interaction settings. J Mol Biol 262: 294–313. 30. Risse G, Jooss K, Neuberg M, Bruller HJ, Muller L (1989) Asymmetrical recognition with the palindromic AP1 binding web page (TRE) by simply Fos necessary protein complexes. EMBO J eight: 3825–3832. 31. Leonard WEIL, Rajaram And, Kerppola TK (1997) Structural basis of GENETICS bending and oriented heterodimer binding by the basic leucine zipper websites of Fos and Jun. Proc Natl Acad Sci U T A 94: 4913–4918. thirty-two. Ellenberger TE, Brandl CJ, Struhl K, Harrison SOUTH CAROLINA (1992) The GCN4 standard region leucine zipper binds DNA being a dimer of uninterrupted a helices: ravenscroft structure of the protein-DNA intricate. Cell 71: 1223–1237. thirty-three. Grant PENNSYLVANIA, Sterner PARA, Duggan LJ, Workman JL, Berger SL (1998) The SAGA originates: convergence of transcription regulators in chromatin-modifying complexes. Developments Cell Biol 8: 193–197. 34. Luger K, Mader AW, Richmond RK, Sargent DF, Richmond TJ (1997) Crystal ˚ structure with the nucleosome key particle for 2 . 8 A resolution. Mother nature 389: 251–260. 35. Luger K, Richmond TJ (1998) DNA binding within the nucleosome core. Curr Opin Struct Biol almost 8: 33–40.
PLoS Computational Biology | www.ploscompbiol.org
September 2008 | Volume some | Issue 9 | e1000170
Protein-DNA Structure Networks
36. Woodcock CL (2006) Chromatin structure. Curr Opin Struct Biol 16: 213–220. 37. Edayathumangalam RS, Weyermann P, Gottesfeld JM, Dervan PB, Luger K (2004) Molecular reputation of the nucleosomal ‘‘supergroove''. Proc Natl Acad Sci U S A 101: 6864–6869. 38. Cavazza B, Brizzolara G, Lazzarini G, Patrone E, Piccardo M, et al. (1991) Thermodynamics of condensation of nuclear chromatin. A gear scanning calorimetry study from the salt-dependent structural transitions. Biochemistry and biology 30: 9060–9072.
39. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, ain al. (2000) The Necessary protein Data Bank. Nucleic Stomach acids Res twenty-eight: 235–242. 45. Kannan In, Vishveshwara S i9000 (1999) Identity of side-chain clusters in protein constructions by a chart spectral method. J Mol Biol 292: 441–464. forty one. Jones T, Shanahan HORSEPOWER, Berman HM, Thornton JM (2003) Using electrostatic potentials to predict DNA-binding sites on DNA-binding proteins. Nucleic Acids Ers 31: 7189–7198. 42. Cormen TH, Leiserson CE, Rivest RL, Stein C (2001) Introduction to Methods. 2nd model. New York: McGraw-Hill.
PLoS Computational Biology | www.ploscompbiol.org
September 08 | Volume 4 | Issue on the lookout for | e1000170