Macromolecular structure and interaction studies of SigF and Usfx in Mycobacterium tuberculosis

References

• Goldberg DE, Siliciano RF, Jacobs WR Jr. Outwitting evolution: fighting drug-resistant TB, Malaria, and HIV. Cell 2012;148:1271–83
   Google Scholar
• Lee JH, Ammerman NC, Nolan S, et al. Isoniazid resistance without a loss of fitness in Mycobacterium tuberculosis. Nat Commun 2012;3:753
   PubMedGoogle Scholar
• Jain A, Mondal R. Extensively drug-resistant tuberculosis: current challenges and threats. FEMS Immunol Med Microbiol 2008;53:145–50
   PubMed Web of Science ®Google Scholar
• Sachdeva P, Misra R, Tyagi AK, Singh Y. The sigma factors of Mycobacterium tuberculosis: regulation of the regulators. FEBS J 2010;277:605–26
   PubMed Web of Science ®Google Scholar
• Cole ST, Brosch R, Parkhill J, et al. Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature 1998;393:537–44
   PubMed Web of Science ®Google Scholar
• Lamrabet O, Drancourt M. Genetic engineering of Mycobacterium tuberculosis: a review. Tuberculosis (Edinb) 2012;92:365–76
   Google Scholar
• Rodrigue S, Provvedi R, Jacques PE, et al. The sigma factors of Mycobacterium tuberculosis. FEMS Microbiol Rev 2006;30:926–41
   PubMed Web of Science ®Google Scholar
• Reynolds RC, Ananthan S, Faaleolea E, et al. High throughput screening of a library based on kinase inhibitor scaffolds against Mycobacterium tuberculosis H37Rv. Tuberculosis (Edinb) 2012;92:72–83
   Google Scholar
• Malkhed V, Gudlur B, Kondagari B, et al. Study of interactions between Mycobacterium tuberculosis proteins: SigK and anti-SigK. J Mol Model 2011;17:1109–19
   PubMed Web of Science ®Google Scholar
• Arcus VL, Lott JS, Johnston JM, Baker EN. The potential impact of structural genomics on tuberculosis drug discovery. Drug Discov Today 2006;11:28–34
   Google Scholar
• Augustynowicz-Kopec E, Jagielski T, Zwolska Z. Genetic diversity of isoniazid-resistant Mycobacterium tuberculosis isolates collected in Poland and assessed by spoligotyping. J Clin Microbiol 2008;46:4041–4
   Google Scholar
• Brooks BE, Buchanan SK. Signaling mechanisms for activation of extracytoplasmic function (ECF) sigma factors. Biochim Biophys Acta 2008;1778:1930–45
   Google Scholar
• Bashyam MD, Hasnain SE. The extracytoplasmic function sigma factors: role in bacterial pathogenesis. Infect Genet Evol 2004;4:301–8
   PubMed Web of Science ®Google Scholar
• Thakur KG, Praveena T, Gopal B. Structural and biochemical bases for the redox sensitivity of Mycobacterium tuberculosis RslA. J Mol Biol 2010;397:1199–208
   Google Scholar
• Beaucher J, Rodrigue S, Jacques PE, et al. Novel Mycobacterium tuberculosis anti-sigma factor antagonists control sigmaF activity by distinct mechanisms. Mol Microbiol 2002;45:1527–40
   Google Scholar
• Geiman DE, Kaushal D, Ko C, et al. Attenuation of late-stage disease in mice infected by the Mycobacterium tuberculosis mutant lacking the SigF alternate sigma factor and identification of SigF-dependent genes by microarray analysis. Infect Immun 2004;72:1733–45
   PubMed Web of Science ®Google Scholar
• Williams EP, Lee JH, Bishai WR, et al. Mycobacterium tuberculosis SigF regulates genes encoding cell wall-associated proteins and directly regulates the transcriptional regulatory gene phoY1. J Bacteriol 2007;189:4234–42
   Google Scholar
• Lee J-H, Karakousis PC, Bishai WR. Roles of SigB and SigF in the Mycobacterium tuberculosis Sigma Factor Network. J Bacteriol 2008;190:699–707
   Google Scholar
• DeMaio J, Zhang Y, Ko C, et al. A stationary-phase stress-response sigma factor from Mycobacterium tuberculosis. Proc Natl Acad Sci 1996;93:2790–4
   PubMed Web of Science ®Google Scholar
• Singh A, Singh B. Conservation of Sigma F in mycobacteria and its expression in Mycobacterium smegmatis. Curr Microbiol 2008;56:574–80
   Google Scholar
• Michele TM, Ko C, Bishai WR. Exposure to antibiotics induces expression of the Mycobacterium tuberculosis sigF gene: implications for chemotherapy against mycobacterial persistors. Antimicrob Agents Chemother 1999;43:218–25
   Google Scholar
• Sachdeva P, Narayan A, Misra R, et al. Loss of kinase activity in Mycobacterium tuberculosis multidomain protein Rv1364c. FEBS J 2008;275:6295–308
   Google Scholar
• Malik SS, Luthra A, Ramachandran R. Interactions of the M. tuberculosis UsfX with the cognate sigma factor SigF and the anti-anti sigma factor RsfA. Biochim Biophys Acta 2009;1794:541–53
   Google Scholar
• Malik SS, Luthra A, Srivastava SK, Ramachandran R. Mycobacterium tuberculosis UsfX (Rv3287c) exhibits novel nucleotide binding and hydrolysis properties. Biochem Biophys Res Commun 2008;375:465–70
   Google Scholar
• Becker OM, Marantz Y, Shacham S, et al. G protein-coupled receptors: in silico drug discovery in 3D. Proc Natl Acad Sci USA 2004;101:11304–9
   PubMed Web of Science ®Google Scholar
• Rajender PS, Vasavi M, Vuruputuri U. Identification of novel selective antagonists for cyclin C by homology modeling and virtual screening. Int J Biol Macromol 2011;48:292–300
   PubMed Web of Science ®Google Scholar
• Bhargavi K, Kalyan Chaitanya P, Ramasree D, et al. Homology modeling and docking studies of human Bcl-2L10 protein. J Biomol Struct Dyn 2010;28:379–91
   PubMed Web of Science ®Google Scholar
• Cavasotto CN, Phatak SS. Homology modeling in drug discovery: current trends and applications. Drug Discov Today 2009;14:676–83
   PubMed Web of Science ®Google Scholar
• Mustyala KK, Chitturi AR, Naikal James PS, Vuruputuri U. Pharmacophore mapping and in silico screening to identify new potent leads for A(2 A) adenosine receptor as antagonists. J Recept Signal Transduct Res 2012;32:102–13
   PubMed Web of Science ®Google Scholar
• Navaneetha N, Mustyala KK, Malked V, et al. Angiogenesis: an insilico approach to angiogenic phenotype. J Pharm Res 2012;5:583–8
   Google Scholar
• Sarita Rajender P, Mustyala KK, Malked V, Vuruputuri U. Novel inhibitors targeting cell signaling receptor – cyclin D2 by virtual screening. J Pharm Res 2012;5:572–9
   Google Scholar
• Kondagari B, Dulapalli R, Krishna Murthy D, Vuruputuri U. Towards the virtual screening of BIK inhibitors with the homology-modeled protein structure. Med Chem Res 2013;22:1184–96
   Web of Science ®Google Scholar
• UniProt Consortium. Ongoing and future developments at the Universal Protein Resource. Nucleic Acids Res 2011;39:D214–19
   PubMed Web of Science ®Google Scholar
• Berman H, Henrick K, Nakamura H, Markley JL. The worldwide Protein Data Bank (wwPDB): ensuring a single, uniform archive of PDB data. Nucleic Acids Res 2007;35:D301–3
   PubMed Web of Science ®Google Scholar
• Mount DW. Using the Basic Local Alignment Search Tool (BLAST). CSH Protoc 2007;2007:pdb top17
   PubMedGoogle Scholar
• Altschul SF, Gish W, Miller W, et al. Basic local alignment search tool. J Mol Biol 1990;215:403–10
   PubMed Web of Science ®Google Scholar
• Kelley LA, Sternberg MJ. Protein structure prediction on the Web: a case study using the Phyre server. Nat Protoc 2009;4:363–71
   PubMed Web of Science ®Google Scholar
• Cole C, Barber JD, Barton GJ. The Jpred 3 secondary structure prediction server. Nucleic Acids Res 2008;36:W197–201
   PubMed Web of Science ®Google Scholar
• Larkin MA, Blackshields G, Brown NP, et al. Clustal W and Clustal X version 2.0. Bioinformatics 2007;23:2947–8
   PubMed Web of Science ®Google Scholar
• Gonnet GH, Cohen MA, Benner SA. Exhaustive matching of the entire protein sequence database. Science 1992;256:1443–5
   PubMed Web of Science ®Google Scholar
• Fiser A, Sali A. Modeller: generation and refinement of homology-based protein structure models. Methods Enzymol 2003;374:461–91
   PubMed Web of Science ®Google Scholar
• Eswar N, Webb B, Marti-Renom MA, et al. Comparative protein structure modeling using MODELLER. Curr Protoc Protein Sci 2007; Chapter 2:Unit 2 9
   PubMedGoogle Scholar
• Eswar N, Eramian D, Webb B, et al. Protein structure modeling with MODELLER. Methods Mol Biol 2008;426:145–59
   PubMedGoogle Scholar
• Guex N, Peitsch MC. SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling. Electrophoresis 1997;18:2714–23
   PubMed Web of Science ®Google Scholar
• Jorgensen WL, Maxwell DS, Tirado-Rives J. Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc 1996;118:11225–36
   Web of Science ®Google Scholar
• Jorgensen WL, Tirado-Rives J. The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. J Amn Chem Soc 1988;110:1657–66
   PubMed Web of Science ®Google Scholar
• Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 1993;26:283–91
   Web of Science ®Google Scholar
• Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 2007;35( Suppl. 2):W407–10
   PubMed Web of Science ®Google Scholar
• Sippl MJ. Recognition of errors in three-dimensional structures of proteins. Proteins 1993;17:355–62
   PubMed Web of Science ®Google Scholar
• Liang J, Edelsbrunner H, Woodward C. Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design. Protein Sci 1998;7:1884–97
   PubMed Web of Science ®Google Scholar
• Dundas J, Ouyang Z, Tseng J, et al. CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Res 2006;34:W116–18
   PubMed Web of Science ®Google Scholar
• Laurie AT, Jackson RM. Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites. Bioinformatics 2005;21:1908–16
   PubMed Web of Science ®Google Scholar
• Halgren T. New method for fast and accurate binding-site identification and analysis. Chem Biol Drug Des 2007;69:146–8
   PubMed Web of Science ®Google Scholar
• Halgren TA. Identifying and characterizing binding sites and assessing druggability. J Chem Inf Model 2009;49:377–89
   PubMed Web of Science ®Google Scholar
• Sorenson MK, Ray SS, Darst SA. Crystal structure of the flagellar sigma/anti-sigma complex sigma(28)/FlgM reveals an intact sigma factor in an inactive conformation. Mol Cell 2004;14:127–38
   Google Scholar
• Lee JH, Im YJ, Bae J, et al. Crystal structure of Thermus caldophilus phosphoglycerate kinase in the open conformation. Biochem Biophys Res Commun 2006;350:1044–9
   Google Scholar
• Pierce BG, Hourai Y, Weng Z. Accelerating protein docking in ZDOCK using an advanced 3D Convolution Library. PLoS ONE 2011;6:e24657
   PubMed Web of Science ®Google Scholar
• Wiehe K, Pierce B, Tong WW, et al. The performance of ZDOCK and ZRANK in rounds 6–11 of CAPRI. Proteins 2007;69:719–25
   Google Scholar
• Mintseris J, Pierce B, Wiehe K, et al. Integrating statistical pair potentials into protein complex prediction. Proteins 2007;69:511–20
   PubMed Web of Science ®Google Scholar
• Lill MA, Danielson ML. Computer-aided drug design platform using PyMOL. J Comput Aided Mol Des 2011;25:13–19
   PubMed Web of Science ®Google Scholar
• Malhotra A, Severinova E, Darst SA. Crystal structure of a sigma 70 subunit fragment from E. coli RNA polymerase. Cell 1996;87:127–36
   Google Scholar
• Campbell EA, Muzzin O, Chlenov M, et al. Structure of the bacterial RNA polymerase promoter specificity sigma subunit. Mol Cell 2002;9:527–39
   PubMed Web of Science ®Google Scholar
• Marchler-Bauer A, Zheng C, Chitsaz F, et al. CDD: conserved domains and protein three-dimensional structure. Nucleic Acids Res 2013;41:D348–52
   PubMed Web of Science ®Google Scholar
• Koonin EV, Galperin MY. Sequence -- evolution -- Function: Computational Approaches in Comparative Genomics. Boston: Kluwer Academic; 2003
   Google Scholar
• Malkhed V, Mustyala K, Potlapally S, Vuruputuri U. Modeling of alternate RNA polymerase Sigma D factor and identification of novel inhibitors by virtual screening. Cel Mol Bioeng 2012;5:363–74
   Google Scholar
• Sali A, Blundell TL. Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 1993;234:779–815
   PubMed Web of Science ®Google Scholar
• Yadav PK, Singh G, Gautam B, et al. Molecular modeling, dynamics studies and virtual screening of Fructose 1, 6 biphosphate aldolase-II in community acquired-methicillin resistant Staphylococcus aureus (CA-MRSA). Bioinformation 2013;9:158–64
   Google Scholar
• Motto I, Bordogna A, Soshilov AA, et al. New aryl hydrocarbon receptor homology model targeted to improve docking reliability. J Chem Inform Model 2011;51:2868–81
   PubMed Web of Science ®Google Scholar