Voir la notice de l'article provenant de la source Math-Net.Ru
@article{MBB_2015_10_2_a3, author = {I. A. Kashyn and A. V. Tuzikov and A. M. Andrianov}, title = {Identification of novel potential inhibitors of the {HIV-1} gp41 protein by virtual screening and molecular modeling methods}, journal = {Matemati\v{c}eska\^a biologi\^a i bioinformatika}, pages = {325--343}, publisher = {mathdoc}, volume = {10}, number = {2}, year = {2015}, language = {ru}, url = {http://geodesic.mathdoc.fr/item/MBB_2015_10_2_a3/} }
TY - JOUR AU - I. A. Kashyn AU - A. V. Tuzikov AU - A. M. Andrianov TI - Identification of novel potential inhibitors of the HIV-1 gp41 protein by virtual screening and molecular modeling methods JO - Matematičeskaâ biologiâ i bioinformatika PY - 2015 SP - 325 EP - 343 VL - 10 IS - 2 PB - mathdoc UR - http://geodesic.mathdoc.fr/item/MBB_2015_10_2_a3/ LA - ru ID - MBB_2015_10_2_a3 ER -
%0 Journal Article %A I. A. Kashyn %A A. V. Tuzikov %A A. M. Andrianov %T Identification of novel potential inhibitors of the HIV-1 gp41 protein by virtual screening and molecular modeling methods %J Matematičeskaâ biologiâ i bioinformatika %D 2015 %P 325-343 %V 10 %N 2 %I mathdoc %U http://geodesic.mathdoc.fr/item/MBB_2015_10_2_a3/ %G ru %F MBB_2015_10_2_a3
I. A. Kashyn; A. V. Tuzikov; A. M. Andrianov. Identification of novel potential inhibitors of the HIV-1 gp41 protein by virtual screening and molecular modeling methods. Matematičeskaâ biologiâ i bioinformatika, Tome 10 (2015) no. 2, pp. 325-343. http://geodesic.mathdoc.fr/item/MBB_2015_10_2_a3/
[1] De Clercq E., “New approaches toward anti-HIV chemotherapy”, J. Med. Chem., 48 (2005), 1297–1313 | DOI
[2] Este J. A., Telenti A., “HIV entry inhibitors”, Lancet, 370 (2007), 81–88 | DOI
[3] Rusconi S., Scozzafava A., Mastrolorenzo A., Supuran C. T., “An update in the development of HIV entry inhibitors”, Curr. Topics in Med. Chem., 7 (2007), 1273–1289 | DOI
[4] Ryser H. J. P., Fluckiger R., “Progress in targeting HIV-1 entry”, Drug Discov. Today, 10 (2005), 1085–1094 | DOI
[5] Adamson C. S., Freed E. O., “Novel approaches to inhibiting HIV-1 replication”, Antiviral. Res., 85 (2010), 119–141 | DOI
[6] Tilton J. C., Doms R. W., “Entry inhibitors in the treatment of HIV-1 infection”, Antiviral Res., 85 (2010), 91–100 | DOI
[7] Arts E. J., Hazuda D. J., “HIV-1 antiretroviral drug therapy”, Cold Spring Harb. Perspect. Med., 2:4 (2012) | DOI | MR | Zbl
[8] Orsega S., “Treatment of adult HIV infection: antiretroviral update and overview”, JNP, 10 (2007), 612–624
[9] Hartley O., Klasse P. J., Sattentau Q. J., Moore J. P., “V3: HIV's switch-hitter”, AIDS Res Hum Retroviruses, 21 (2005), 171–189 | DOI
[10] Sirois S., Sing T., Chou K. C., “HIV-1 gp120 V3 loop for structure-based drug design”, Curr. Protein Pept. Sci., 6 (2005), 413–422 | DOI
[11] Andrianov A. M., “HIV-1 gp120 V3 loop for anti-AIDS drug discovery: computer-aided approaches to the problem solving”, Expert Opin. Drug Discov., 6 (2011), 419–435 | DOI
[12] Hoxie J. A., “Toward an antibody-based HIV-1 vaccine”, Annu. Rev. Med., 61 (2010), 135–152 | DOI
[13] Walker L. M., Burton D. R., “Rational antibody-based HIV-1 vaccine design: current approaches and future directions”, Curr. Opin. Immunol., 22 (2010), 358–366 | DOI
[14] Kwong P. D., Mascola J. R., Nabel G. J., “Rational design of vaccines to elicit broadly neutralizing antibodies to HIV-1”, Cold Spring Harb. Perspect. Med., 1:1 (2011) | DOI
[15] McCoy L. E., Weiss R. A., “Neutralizing antibodies to HIV-1 induced by immunization”, J. Exp. Med., 210 (2013), 209–223 | DOI
[16] Huang J., Ofek G., Laub L., Louder M. K., Doria-Rose N. A., Longo N. S., Imamichi H., Bailer R. T., Chakrabarti B., Sharma S. K., Munir Alam S., Wang T., Yang Y., Zhang B., Migueles S. A., Wyatt R., Haynes B. F., Kwong P. D., Mascola J. R., Connors M., “Broad and potent neutralization of HIV-1 by a gp41-specific human antibody”, Nature, 491 (2012), 406–414 | DOI
[17] Andrianov A. M., Kashin I. A., Tuzikov A. V., “Kompyuternyi poisk novykh anti-VICh-1 agentov — peptidomimetikov neitralizuyuschikh antitel — i otsenka ikh potentsialnoi ingibitornoi aktivnosti metodami molekulyarnogo modelirovaniya”, Matematicheskaya biologiya i bioinformatika, 8:1 (2013), 119–134 | DOI
[18] Kashin I. A., Tuzikov A. V., Andrianov A. M., “Virtualnyi skrining novykh ingibitorov proniknoveniya VICh-1, blokiruyuschikh CD4-svyazyvayuschii uchastok belka gp120 obolochki virusa”, Matematicheskaya biologiya i bioinformatika, 9:2 (2014), 359–372 | DOI
[19] Floris M., Masciocchi J., Fanton M., Moro S., “Swimming into peptidomimetic chemical space using pepMMsMIMIC”, Nucl. Acids Res., 39 (2011), 261–269 | DOI
[20] Bernstein F. C., Koetzle T. F., Williams G. J. B., Meyer E. F., Brice M. D., Rodgers J. R., Kennard O., Shimanouchi T., Tasumi M., “The protein data bank. A computer-based archival file for macromolecular structures”, J. Mol. Biol., 112 (1977), 535–542 | DOI
[21] Berman H. M., Westbrook J., Feng Z., Gilliland G., Bhat T. N., Weissig H., Shindyalov I. N., Bourne P. E., “The Protein Data Bank”, Nucl. Acids Res., 28 (2000), 235–242 | DOI
[22] Case D. A., Darden T. A., Cheatham T. E., Simmerling C. L., Wang J., Duke R. E., Luo R., Crowley M., Walker R. C., Zhang W., Merz K. M., Wang B., Hayik S., Roitberg A., Seabra G., Kolossváry I., Wong K. F., Paesani F., Vanicek J., Wu X., Brozell S. R., Steinbrecher T., Gohlke H., Yang L., Tan C., Mongan J., Hornak V., Cui G., Mathews D. H., Seetin M. G., Sagui C., Babin V., Kollman P. A., AMBER 11. Users' Manual, University of California, San Francisco, 2010, 302 pp.
[23] Jorgensen W. L., Chandrasekhar J., Madura J. D., Impey R. W., Klein M. L., “Comparison of simple potential functions for simulating liquid water”, J. Chem. Phys., 79 (1983), 926–935 | DOI
[24] Ryckaert J. P., Ciccotti G., Berendsen H. J. C., “Numerical integration of the Cartesian equations of motion of a system with constraints: molecular dynamics of $n$-alkanes”, J. Comput. Phys., 23 (1977), 327–341 | DOI
[25] Massova I., Kollman P. A., “Computational alanine scanning to probe protein-protein interactions: a novel approach to evaluate binding free energies”, J. Am. Chem. Soc., 121 (1999), 8133–8143 | DOI
[26] Masciocchi J., Frau G., Fanton M., Sturlese M., Floris M., Pireddu L., Palla P., Cedrati F., Rodriguez-Tome P., Moro S., “MMsINC: a large-scale chemoinformatics database”, Nucl. Acids Res., 37 (2009), D284–D290 | DOI
[27] Ballester P. J., Richards W. G., “Ultrafast shape recognition to search compound databases for similar molecular shapes”, J. Comput. Chem., 28 (2007), 1711–1723 | DOI
[28] Mason J. S., Morize I., Menard P. R., Cheney D. L., Hulme C., Labaudiniere R. F., “New 4-point pharmacophore method for molecular similarity and diversity applications: overview of the method and applications, including a novel approach to the design of combinatorial libraries containing privileged substructures”, J. Med. Chem., 42 (1999), 3251–3264 | DOI
[29] Karnachi P., Kulkarni A., “Application of pharmacophore fingerprints to structure-based design and data mining”, Pharmacophores and Pharmacophore Searches, eds. Langer T., Hoffmannn R. D., Wiley-VCH, Weinheim, 2006, 193–206 | DOI
[30] Lipinski C. A., Lombardo F., Dominy B. W., Feeney P. J., “Lead- and drug-like compounds: the rule-of-five revolution”, Adv. Drug Delivery Rev., 46 (2001), 3–26 | DOI
[31] Drug Likeness Tool (DruLiTo) HomePage, (data obrascheniya: 21.07.2015) http://www.niper.gov.in/pi_dev_tools/DruLiToWeb/DruLiTo_index.html
[32] Trott O., Olson A. J., “AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading”, J. Comput. Chem., 31 (2010), 455–461
[33] Morris G. M., Huey R., Lindstrom W., Sanner M. F., Belew R. K., Goodsell D. S., Olson A. J., “AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility”, J. Comput. Chem., 30 (2009), 2785–2791 | DOI
[34] Durrant J. D., McCammon J. A., “NNScore: A neural-network-based scoring function for the characterization of protein-ligand complexes”, J. Chem. Inf. Model., 50 (2010), 1865–1871 | DOI
[35] Durrant J. D., McCammon J. A., “NNScore 2.0: a neural-network receptor-ligand scoring function”, J. Chem. Inf. Model., 51 (2011), 2897–2903 | DOI
[36] Ballester P. J., Mitchell J. B. O., “A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking”, Bioinformatics, 26 (2010), 1169–1175 | DOI | MR
[37] Cao Y., Li L., “Improved protein-ligand binding affinity prediction by using a curvature-dependent surface-area model”, Bioinformatics, 30 (2014), 1674–1680 | DOI
[38] Wang J., Wolf R. M., Caldwell J. W., Kollman P. A., Case D. A., “Development and testing of a general amber force field”, J. Comput. Chem., 25 (2004), 1157–1174 | DOI
[39] Durrant J. D., McCammon J. A., “BINANA: A novel algorithm for ligand-binding characterization”, J. Mol. Graph. Model., 29 (2011), 888–893 | DOI
[40] McDonald I. K., Thornton J. M., “Satisfying hydrogen bonding potential in proteins”, J. Mol. Biol., 238 (1994), 777–793 | DOI
[41] Munoz-Barroso I., Salzwedel K., Hunter E., Blumenthal R., “Role of the membraneproximal domain in the initial stages of human immunodeficiency virus type 1 envelope glycoprotein-mediated membrane fusion”, J. Virol., 73 (1999), 6089–6092
[42] Salzwedel K., West J. T., Hunter E., “A conserved tryptophan-rich motif in the membrane-proximal region of the human immunodeficiency virus type 1 gp41 ectodomain is important for Env-mediated fusion and virus infectivity”, J. Virol., 73 (1999), 2469–2480
[43] Cheng Y., Elicitation of antibody responses against the HIV-1 gp41 Membrane Proximal External Region (MPER), Doctoral dissertation, Harvard University, 2014 (data obrascheniya: 21.07.2015)
[44] Sun Z. Y. J., Cheng Y., Kim M., Song L., Choi J., Kudahl U. J., Brusic V., Chowdhury B., Yu L., Seaman M. S., Bellot G., Shih W. M., Wagner G., Reinherz E. L., “Disruption of helix-capping residues 671 and 674 reveals a role in HIV-1 entry for a specialized hinge segment of the membrane proximal external region of gp41”, J. Mol. Biol., 426 (2014), 1095–1108 | DOI