205
Views
8
CrossRef citations to date
0
Altmetric
Special Issue: 9th International Symposium on Computational Methods in Toxicology and Pharmacology Integrating Internet Resources (CMTPI-2017) - Part 2. Guest Editors: A.K. Saxena and M. Saxena

Molecular property diagnostic suite (MPDS): Development of disease-specific open source web portals for drug discoveryFootnote$

, , , , , , ORCID Icon, ORCID Icon & ORCID Icon show all
Pages 913-926 | Received 03 Nov 2017, Accepted 06 Nov 2017, Published online: 05 Dec 2017

References

  • D.B. Searls, Data integration: Challenges for drug discovery, Nat. Rev. Drug Discov. 4 (2005), pp. 45–58.
  • J. Mullen, S.J. Cockell, P. Woollard, and A. Wlpat, An integrated data driven approach to drug repositioning using gene-disease associations, PLoS ONE 11 (2016), p. e0155811.
  • J. Luo, M. Wu, D. Gopukumar, and Y. Zhao, Big data application in biomedical research and health care: A literature review, Biomed. Inform. Insights 8 (2016), pp. 1–10.
  • A. Belle, R. Thiagarajan, S.M.R. Soroushmehr, F. Navidi, D.A. Beard, and K. Najarian, Big data analytics in healthcare, Biomed. Res. Int. 2015 (2015), pp. 1–16.
  • A.S. Gaur, A. Bhardwaj, A. Sharma, L. John, M.R. Vivek, N. Tripathi, P.V. Bharatam, R. Kumar, S. Janardhan, A. Mori, A. Banerji, A.M. Lynn, A.J. Hemrom, A. Passi, A. Singh, A. Kumar, C. Muvva, C. Madhuri, C. Choudhury, A.D. Kumar, D. Pandit, D.R. Bharti, D. Kumar, A. Er, G.P.S. Singam, H. Raghava, H. Sailaja, K. Jangra, K. Raithatha, K. Tanneeru, M. Chaudhary, M. Karthikeyan, N. Prasanthi, N. Kumar, N.K. Yedukondalu, P.S. Rajput, P. Saranya, P. Narang, R.V. Dutta, R. Krishnan, R.Srinithi Sharma, R. Mishra S. Hemasri, S. Singh, S. Venkatesan, S. Kumar, U.C.A. Jaleel, V. Khedkar, Y. Joshi, and G.N. Sastry, Assessing therapeutic potential of molecules: Molecular Property Diagnostic Suite for Tuberculosis (MPDSTB), J. Chem. Sci. 129 (2017), pp. 515–531.
  • S. Janardhan and G.N. Sastry, Dipeptidyl peptidase IV inhibitors: A new paradigm in type 2 diabetes treatment, Curr. Drug Targets 15 (2014), pp. 600–621.
  • D.E. Scott, A.G. Coyne, S.A. Hudson, and C. Abell, Fragment-based approaches in drug discovery and chemical biology, Biochemistry 51 (2012), pp. 4990–5003.
  • D. Blankenberg, G. Von Kuster, N. Coraor, G. Ananda, R. Lazarus, M. Mangan, A. Nekrutenko, and J. Taylor, Galaxy: A web-based genome analysis tool for experimentalists, Curr. Protoc. Mol. Biol. Chapter 19 Unit 19.10 (2010), pp. 1–21.
  • E. Afgan, D. Baker, M.V.D. Beek, D. Blankenberg, D. Bouvier, M. Cech, J. Chilton, D. Clements, N. Coraor, C. Eberhard, B. Gruning, A. Guerler, J.H. Jackson, G.V. Kuster, E. Rasche, N. Soranzo, N. Turaga, J. Taylor, A. Nekrutenko, and J. Goecks, The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update, Nucleic Acids Res. 44 (2016), pp. W3–W10.
  • S. Janardhan, L. John, M. Prasanthi, V. Poroikov, and G.N. Sastry, A QSAR and molecular modelling study towards new lead finding: Polypharmacological approach to Mycobacterium tuberculosis, SAR QSAR Environ. Res. 2017 doi:10.1080/1062936X.2017.1398782.
  • P.J.A. Goodford, Computational procedure for determining energetically favorable binding sites on biologically important macromolecules, J. Med. Chem. 28 (1985), pp. 849–857.
  • A. Miranker and M. Karplus, Functionality maps of binding sites: A multiple copy simultaneous search method, Proteins 11 (1991), pp. 29–34.
  • A. Caflisch, A. Miranker, and M. Karplus, Multiple copy simultaneous search and construction of ligands in binding sites: Application to inhibitors of HIV-1 aspartic proteinase, J. Med. Chem. 36 (1993), pp. 2142–2167.
  • U. Lessel, B. Wellenzohn, M. Lilienthal, and H. Claussen, Searching fragment spaces with feature trees, J. Chem. Inf. Model. 49 (2009), pp. 270–279.
  • R.F. Ludlow, M.L. Verdonk, H.K. Saini, I.J. Tickle, and H. Jhoti, Detection of secondary binding sites in proteins using fragment screening, Proc. Natl. Acad. Sci. 112 (2015), pp. 15910–15915.
  • M. Congreve, R. Carr, C. Murray, and H.A. Jhoti, “Rule of three” for fragment-based lead discovery?, Drug. Discov. Today 8 (2003), pp. 876–877.
  • H. Koster, T. Craan, S. Brass, C. Herhaus, M. Zentgraf, L. Neumann, A. Heine, and G. Klebe, A small non rule of 3 compatible fragment library provides high hit rate of endothiapepsin crystal structures with various fragment chemotypes, J. Med. Chem. 54 (2011), pp. 7784–7796.
  • P. Badrinarayan and G.N. Sastry, Exploiting DFG-out conformation as a strategy to design new type II p38 MAP Kinase leads: Sequence, strategy and active site analysis, J. Chem. Inf. Model. 51 (2011), pp. 115–129.
  • P. Badrinarayan and G.N. Sastry, Virtual screening filters for the design of type II p38 MAP kinase inhibitors: A fragment-based library generation approach, J. Mol. Graph. Model. 34 (2012), pp. 89–100.
  • N.M. O’Boyle, M. Banck, C.A. James, C. Morley, T. Vandermeersch, and G.R. Hutchison, Open Babel: An open chemical toolbox, J. Cheminform. 3 (2011), p,. 33.
  • A.S. Reddy, S.P. Pati, P.P. Kumar, H.N. Pradeep, and G.N. Sastry, Virtual screening in drug discovery – a computational perspective, Curr. Protein. Pept. Sci. 4 (2007), pp. 329–351.
  • P. Badrinarayan and G.N. Sastry, Virtual high throughput screening in new lead identification, Comb. Chem. High Throughput Screen. 14 (2011), pp. 840–860.
  • A. Lagunin, A. Stepanchikova, D. Filimonov, and V. Poroikov, PASS: Prediction of activity spectra for biologically active substances, Bioinformatics 16 (2000), pp. 747–748.
  • D.A. Filimonov, A.A. Lagunin, T.A. Gloriozova, A.V. Rudik, D.S. Druzhilovskiy, P.V. Pogodin, and V.V. Poroikov, Prediction of the biological activity spectra of organic compounds using the PASS online web resource, Chem. Heterocycl. Compnd. 50 (2014), pp. 444–457.
  • T. Rolland, M. Taşan, B. Charloteaux, S.J. Pevzner, Q. Zhong, N. Sahni, S. Yi, I. Lemmens, C. Fontanillo, R. Mosca, A. Kamburov, S.D. Ghiassian, X. Yang, L. Ghamsari, D. Balcha, B.E. Begg, P. Braun, M. Brehme, M.P. Broly, A.R. Carvunis, D. Convery-Zupan, R. Corominas, J. Coulombe-Huntington, E. Dann, M. Dreze, A. Dricot, C. Fan, E. Franzosa, F. Gebreab, B.J. Gutierrez, M.F. Hardy, M. Jin, S. Kang, R. Kiros, G.N. Lin, K. Luck, A. MacWilliams, J. Menche, R.R. Murray, A. Palagi, M.M. Poulin, X. Rambout, J. Rasla, P. Reichert, V. Romero, E. Ruyssinck, J.M. Sahalie, A. Scholz, A.A. Shah, A. Sharma, Y. Shen, K. Spirohn, S. Tam, A.O. Tejeda, S.A. Trigg, J.C. Twizere, K. Vega, J. Walsh, M.E. Cusick, Y. Xia, A.L. Barabási, L.M. Iakoucheva, P. Aloy, J. De Las Rivas, J. Tavernier, M.A. Calderwood, D.E. Hill, T. Hao, F.P. Roth, and M. Vida, A proteome-scale map of the human interactome network, Cell 159 (2014), pp. 1212–1226.
  • L. Hakes, J.W. Pinney, D.L. Robertson, and S.C. Lovell, Protein-protein interaction networks and biology – What’s the connection? Nat. Biotechnol. 26 (2008), pp. 69–72.
  • N. Zmora, S. Bashiardes, M. Levy, and E. Elinav, The role of the immune system in metabolic health and disease, Cell. Metab. 25 (2017), pp. 506–521.
  • B. Chen and A.J. Butte, Leveraging big data to transform target selection and drug discovery, Clin. Pharmacol. Ther. 99 (2017), pp. 285–297.
  • W. Raghupathi and V. Raghupathi, Big data analytics in healthcare: Promise and potential, Health. Inf Sci Syst. 2 (2014), pp. 1–10.
  • S.A. Neymotin and W.W. Lytton, Multiscale modeling for drug discovery in brain disease, Drug Discov Today: Disease 19 (2016), pp. 1–3.
  • V. Matys, E. Fricke, R. Geffers, E. Gössling, M. Haubrock, R. Hehl, K. Hornischer, D. Karas, A.E. Kel, O.V. Kel-Margoulis, D.U. Kloos, S. Land, B. Lewicki-Potapov, H. Michael, R. Münch, I. Reuter, S. Rotert, H. Saxel, M. Scheer, S. Thiele, and E. Wingender, TRANSFAC: Transcriptional regulation, from patterns to profiles, Nucleic Acids Res. 31 (2003), pp. 374–378.
  • J. Menche, A. Sharma, M. Kitsak, S.D. Ghiassian, M. Vidal, J. Loscalzo, and A-L. Barabasi, Uncovering disease-disease relationships through the incomplete interactome, Science 347 (2015), pp. 1257601–1–1257601-8.
  • B. Aranda, P. Achuthan, Y. Alam-Faruque, I. Armean, A. Bridge, C. Derow, M. Feuermann, A.T. Ghanbarian, S. Kerrien, J. Khadake, J. Kerssemakers, C. Leroy, M. Menden, M. Michaut, L. Montecchi-Palazzi, S.N. Neuhauser, S. Orchard, V. Perreau, B. Roechert, K. van Eijk, and H. Hermjakob, The IntAct molecular interaction database in 2010, Nucleic Acids Res. 38 (2010), pp. D525–D531.
  • A. Ceol, A. Chatr Aryamontri, L. Licata, D. Peluso, L. Briganti, L. Perfetto, L. Castagnoli, and G. Cesareni, MINT, the molecular interaction database: 2009 update, Nucleic Acids Res. 38 (2010), pp. D532–D539.
  • C. Stark, B.J. Breitkreutz, A. Chatr-Aryamontri, L. Boucher, R. Oughtred, M.S. Livstone, J. Nixon, K. Van Auken, X. Wang, X. Shi, T. Reguly, J.M. Rust, A. Winter, K. Dolinski, M. Tyers, The biogrid interaction database: 2011 update, Nucleic Acids Res. 39 (2011), pp. D698–D704.
  • T.S. Keshava Prasad, R. Goel, K. Kandasamy, S. Keerthikumar, S. Kumar, S. Mathivanan, D. Telikicherla, R. Raju, B. Shafreen, A. Venugopal, L. Balakrishnan, A. Marimuthu, S. Banerjee, D.S. Somanathan, A. Sebastian, S. Rani, S. Ray, C.J. Harrys Kishore, S. Kanth, M. Ahmed, M.K. Kashyap, R. Mohmood, Y.L. Ramachandra, V. Krishna, B.A. Rahiman, S. Mohan, P. Ranganathan, S. Ramabadran, R. Chaerkady, and A. Pandey, Human protein reference database – 2009 update, Nucleic Acids Res. 37 (2009), pp. D767–D772.
  • I. Lee, U.M. Blom, P.I. Wang, J.E. Shim, and E.M. Marcotte, Prioritizing candidate disease genes by network-based boosting of genome-wide association data, Genome Res. 21 (2011), pp. 1109–1121.
  • A. Ruepp, B. Waegele, M. Lechner, B. Brauner, I. Dunger-Kaltenbach, G. Fobo, G. Frishman, C. Montrone, and H.W. Mewes, CORUM: The comprehensive resource of mammalian protein complexes—2009, Nucleic Acids Res. 38 (2010), pp. D497–D501.
  • P.V. Hornbeck, J.M. Kornhauser, S. Tkachev, B. Zhang, E. Skrzypek, B. Murray, V. Latham, and M. Sullivan, PhosphoSitePlus: A comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse, Nucleic Acids Res. 40 (2012), pp. D261–D270.
  • A. Vinayagam, U. Stelzl, R. Foulle, S. Plassmann, M. Zenkner, J. Timm, H.E. Assmus, M. A. Andrade-Navarro, and E.E. Wanker, A directed protein interaction network for investigating intracellular signal transduction, Sci. Signal. 4 (2011), pp. rs8.
  • P.E. Scherer, Adipose tissue: From lipid storage compartment to endocrine organ, Diabetes. 55 (2006), pp. 1537–1545.
  • J. Hirosumi, G. Tuncman, L. Chang, C.Z. Gorgun, K.T. Uysal, K. Maeda, M. Karin, and G.S. Hotamisligil, A central role for JNK in obesity and insulin resistance, Nature 420 (2002), pp. 333–336.
  • N. Ouchi, A. Higuchi, K. Ohashi, Y. Oshima, N. Gokce, R. Shibata, Y. Akasaki, A. Shimono, and K. Walsh, Sfrp5 is an anti-inflammatory adipokine that modulates metabolic dysfunction in obesity, Science 329 (2010), pp. 454–457.
  • H.J. Kim, T. Higashimori, S.Y. Park, H. Choi, J. Dong, Y.J. Kim, H.L. Noh, Y.R. Cho, G. Cline, Y.B. Kim, J. K. Kim, Differential effects of interleukin-6 and -10 on skeletal muscle and liver insulin action in vivo, Diabetes 53 (2004), pp. 1060–1067.
  • A. Gaffo, K.G. Saag, and J.R. Curtis, Treatment of rheumatoid arthritis, Am. J. Health Syst. Pharm. 63 (2006), pp. 2451–2465.
  • L. Bossaller and A. Rothe, Monoclonal antibody treatments for rheumatoid arthritis, Expert Opin. Biol. Ther. 13 (2013), pp. 1257–1272.
  • A. Gokhale, T.K. Weldeghiorghis, V. Taneja, and S.D. Satyanarayanajois, Conformationally constrained peptides from CD2 to modulate protein-protein interactions between CD2 and CD58, J. Med. Chem. 54 (2011), pp. 5307–5319.
  • M. Tsai, M. Grimbaldeston, and S.J. Galli, Mast cells and immunoregulation/immunomodulation, Adv. Exp. Med. Biol. 716 (2011), pp. 186–211.
  • O. Bloom, K.F. Cheng, M. He, A. Papatheodorou, B.T. Volpe, B. Diamond, and Y. Al-Abed, Generation of a unique small molecule peptidomimetic that neutralizes lupus autoantibody activity, Proc. Natl Acad. Sci. USA 108 (2011), pp. 10255–10259.
  • P.T. Kaumaya and K.C. Foy, Peptide vaccines and targeting HER and VEGF proteins may offer a potentially new paradigm in cancer immunotherapy, Future Oncol. 8 (2012), pp. 961–987.
  • P.T. Kaumaya, Bridging oncology and immunology: Expanding horizons with innovative peptide vaccines and peptidomimetics, Immunotherapy 5 (2013), pp. 1159–1163.
  • M.J. Welters, G.G. Kenter, S.J. Piersma, A.P. Vloon, M.J. Löwik, D.M. Berends-van der Meer, J.W. Drijfhout, A.R. Valentijn, A.R. Wafelman, J. Oostendorp, G.J. Fleuren, R. Offringa, C.J. Melief, and S.H. van der Burg, Induction of tumor specific CD4+ and CD8+ T-cell immunity in cervical cancer patients by a human papilloma virus type 16 E6 and E7 long peptides vaccine, Clin. Cancer Res. 14 (2008), pp. 178–187.
  • Z. Hayouka, M. Hurevich, A. Levin, H. Benyamini, A. Iosub, M. Maes, D.E. Shalev, A. Loyter, C. Gilon, and A. Friedler, Cyclic peptide inhibitors of HIV-1 integrase derived from the LEDGF/p75 protein, Bioorg. Med. Chem. 18 (2010), pp. 8388–8395.
  • L. De Luca, S. Ferro, F. Morreale, and A. Chimirri, Inhibition of the interaction between HIV-1 integrase and its cofactor LEDGF/p75: A promising approach in anti-retroviral therapy, Mini Rev. Med. Chem. 11 (2011), pp. 714–727.
  • A.S. Gokhale and S. Satyanarayanajois, Peptides and peptidomimetics as immunomodulators, Immunotherapy 6 (2014), pp. 755–774.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.