AN INSIGHT TO VIRTUAL LIGAND SCREENING METHODS FOR STRUCTURE-BASED DRUG DESIGN AND METHODS TO PREDICT PROTEIN STRUCTURE AND FUNCTION IN LUNG CANCER: APPROACHES AND PROGRESS
Lung cancer is a complex disease that involves multiple types of biological interactions across diverse, physical, temporal, and biological scales. This complexity presents substantial challenges for the characterization of lung cancer, and motivates the study of cancer in the context of molecular, cellular, and physiological systems. Computational models of Lung cancer are being developed to aid both biological discovery and clinical medicine. The development of these in silico models is facilitated by rapidly advancing experimental and analytical tools that generate information-rich, high-throughput biological data. Protein structure prediction by using bioinformatics can involve sequence similarity searches, multiple sequence alignments, identification and characterization of domains, secondary structure prediction, solvent accessibility prediction, automatic protein fold recognition, constructing three-dimensional models to atomic detail, and model validation. Till today technologies like combinatorial chemistry and high-throughput screening (HTS) authorize biological assays of a large number of small molecules against the therapeutically relevant targets. However, the escalating costs highlight the need of developing novel approaches while still allowing one to explore larger chemical diversity. In this respect, Virtual ligand screening (VLS) is established as an attractive approach to handle large sets of compounds and to improve the â€œhit-rateâ€ of drug discovery programs. Here, we review the main Ligand Screening techniques applied for structure-based drug design and we focus on key concepts in the molecular dockingâ€“scoring methodology in lung cancer. These methods, if used appropriately, can provide valuable indicators of protein structure and function for the different type of cancers.Keywords: Lung cancer, Molecular modeling, Sequence similarity searches, Multiple sequence alignment, Secondary structure prediction, Virtual screening, Structure-based drug design, Polo like kinase 1, Thrombomodulin, Review.
2. Land H, Parada LF, Weinberg RA. Tumorigenic conversion of primary embryo fibroblasts requires at least two cooperating oncogenes. Nature 1983;304(5927):596â€“602.
3. Lloyd AC, Obermuller F, Staddon S, Barth CF, McMahon M, Land H. converge to regulate cyclin/cdk complexes. Genes Dev 1997;11(5):663â€“77.
4. Fanidi A, Harrington EA, Evan GI. Cooperative interaction between c-myc and bcl-2 protooncogenes. Nature 1992;359(6395):554â€“6.
5. Lowe SW, Cepero E, Evan G. Intrinsic tumour suppression. Nature 2004;432(7015):307â€“15.
6. McMurray HR, Sampson ER, Compitello G, Kinsey C, Newman L, Smith B, et al. Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype. Nature 2008;453(7198):1112.
7. Chuang HY, Lee E, Liu YT, Lee D, Ideker T. Network-based classification of breast cancer metastasis. Mol Syst Biol 2007;3:140.
8. Liu ET, Lemberger T. Higher order structure in the cancer transcriptome and systems medicine. Mol Syst Biol 2007;3:94.
9. Auffray C. Protein subnetwork markers improve prediction of cancer outcome. Mol Syst Biol 2007;3:141.
10. Neely KE, Workman JL. The complexity of chromatin remodeling and its links to cancer. Biochim Biophys Acta 2002;1603(1):19â€“29.
11. Seligson DB, Horvath S, Shi T, Yu H, Tze S, Grunstein M, et al. Global histone modification patterns predict risk of prostate cancer recurrence. Nature 2005;435(7046):1262.
12. Esteller M. Cancer epigenomics: DNA methylomes and histone-modification maps. Nature 2007;8:286-98.
13. Jones PA. DNA methylation and cancer. Oncogene 2002;21:5358â€“60.
14. Esteller M, Fraga MF, Paz MF, Campo E, Colomer D, Novo FJ, et al. Cancer epigenetics and methylation. Sci 2002;297(5588):1807â€“8.
15. Laird PW. Cancer epigenetics. Hum Mol Genet 2005;14(90001):65â€“76.
16. Cummings MD, RL DesJarlais, AC Gibbs, V Mohan, EP Jaeger. Comparison of automated docking programs as virtual screening tools. J Med Chem 2005;48:962-76.
17. Schneidman-Duhovny D, Nussinov HJ. Wolfson predicting molecular interactions in silico: ii. protein-protein and protein-drug docking. Curr Med Chem 2004;11:91-107.
18. Sperandio O, MA Miteva, F Delfaud BO. Villoutreix receptor-based computational screening of compound databases: the main docking-scoring engines. Curr Protein Pept Sci 2006;7:369-93.
19. Zhou Z, AK Felts, RA Friesner, RM Levy. Comparative performance of several flexible docking programs and scoring functions: enrichment studies for a diverse set of pharmaceutically relevant targets. J Chem Inf Model. 2007;47:1599-608.
20. Evers A, G Klebe. Successful virtual screening for a submicromolar antagonist of the neurokinin-1 receptor based on a ligand-supported homology model. J Med Chem 2004;47:5381-92.
21. Kairys V, MX Fernandes, MK Gilson. Screening Drug-Like compounds by docking to homology models: a systematic study. J Chem Inf Model 2006;46:365-79.
22. Mohan V, AC Gibbs, MD Cummings, EP Jaeger, RL DesJarlais. Docking: successes and challenges. Curr Pharm Des. 2005;11:323-33.
23. Rockey WM, AH Elcock. Progress toward virtual screening for drug side effects. Proteins 2002;48:664-71.
24. Lyne PD. Structure-based virtual screening: an overview. Drug Discov Today 2002;7:1047-55.
25. Iskar M, Zeller G, Zhao XM, Van Noort V, Bork P. Drug discovery in the age of systems biology: the rise of computational approaches for data integration. Curr Opin Biotechnol 2012;23:609â€“16.
26. Whittaker P. What is the relevance of bioinformatics to pharmacology? Trend Pharmacol Sci 2003;24:434â€“9.
27. Ortega SS, Cara LC, Salvador MK. In silico pharmacology for a multidisciplinary drug discovery process. Drug Metabol Drug Interact 2012;27:199â€“207.
28. Song CM, Lim SJ, Tong JC. Recent advances in computer aided drug design. Brief Bioinform 2009;10:579â€“91.
29. Speck-Planche A, Cordeiro MN, Guilarte-Montero L, Yera-Bueno R. Current computational approaches towards the rational design of new insecticidal agents. Curr Comput Aided Drug Des 2011;7(4):304â€“14.
30. Chen YP, Chen F. Identifying targets for drug discovery using bioinformatics. Expert Opin Ther Targ 2008;12:383â€“38.
31. Katara P, Grover A, Kuntal H, Sharma V. In silico prediction of drug targets in Vibrio cholerae. Protoplasma 2011;248:799â€“804.
32. Yamanishi Y, Kotera M, Kanehisa M, Goto S. Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics 2010;26:246â€“54.
33. Loh M, Soong R. Challenges and pitfalls in the introduction of pharmacogenetics for cancer. Ann Acad Med Singap 2011;40:369â€“74.
34. Bajorath J, Stenkamp R, Aruffo A. Knowledge-based model building of proteins: concepts and examples. Protein Sci 1994;2:1798-810.
35. Blundell TL, Sibanda BL, Sternberg MJE, Thornton JM. Knowledge-based prediction of protein structures and the design of novel molecules. Nature 1994;326:347â€“52.
36. Johnson MS, Srinivasan N, Sowdhamini R, Blundell TL. Knowledge-based protein modelling. CRC Crit Rev Biochem Mol Biol 1994;29:1-68.
37. Sali A. Modeling mutations and homologous proteins. Curr Opin Biotech 1995;6:437-51.
38. Sanchez R, Sali A. Advances in comparative protein-structure modeling. Curr Opin Struct Biol 1994;7:206-14.
39. Chothia C, Lesk AM. The relation between the divergence of sequence and structure in proteins. EMBO J 1986;5:823â€“26.
40. Koehl P, Levitt M. A brighter future for protein structure prediction. Nature Struct Biol 1999;6:108â€“11.
41. Lesk AM, Chothia C. How different amino acid sequences determine similar protein structures: the structure and evolutionary dynamics of the globins. J Mol Biol 1980;130:225-70.
42. Fischer D, Eisenberg D. Assigning folds to the proteins encoded by the genome of Mycoplasma genitalium. Proc Natl Acad Sci USA 1997;94:11929â€“34.
43. Huynen M, Doerks T, Eisenhaber F, Orengo C, Sunyaev S, Yuan Y, et al. Homology-based fold predictions for Mycoplasma genitalium proteins. J Mol Biol 1998;280:323â€“26.
44. Jones DT. Gen Threader: an efficient and reliable protein fold recognition method for genomic sequences. J Mol Biol 1999;287:797â€“815.
45. Rychlewski L, Zhang B, Godzik A. Fold and function predictions for Mycoplasma genitalium proteins. Folding Design 1998;3:229â€“38.
46. Sanchez R, Sali A. Large-scale protein structure modeling of the Saccharomyces cerevisiae genome. Proc Natl Acad Sci USA 1998;95:13597â€“602.
47. Bairoch A, Apweiler R. The swissprot protein sequence databank and its supplement TrEMBL in. Nucleic Acids Res 1999;27:49-54.
48. Abola EE, Bernstein FC, Bryant SH, Koetzle TF, Weng J. Allen FH, et al. Protein data bank. In Crystallographic Databasesâ€”Information, Content, Software Systems, Scientific Applications. Bonn/Cambridge/Chester. Data Commission Int. Union of Crystallography; 1987. p. 107â€“32.
49. Berman HM, West brook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The protein data bank. Nucleic Acids Res 2000;28:235-42.
50. Zhang ZT. Relations of the numbers of protein sequences families and folds. Protein Eng 1997;10:757-61.
51. Holm L, Sander C. Mapping the protein universe. Sci 1996;273:595-602.
52. Brooijmans N. Kuntz ID. Molecular recognition and docking algorithms. Annu Rev Biophys Biolmol Struct 2003;32:335â€“73.
53. Gohlke H, Klebe G. Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors. Angew Chem Int Ed 2002;41:2644â€“76.
54. Halperin I, Ma B, Wolfson H. Nussinov R. Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins 2002;47:409â€“43.
55. Burnett RM, Taylor JS. Darwin: a program for docking flexible molecules. Proteins 2000;41:173â€“91.
56. Norel R, Lin SL, Wolfson H, Nussinov R. Shape complementarity at proteinâ€“protein interfaces. Biopolymers 1994;34:933â€“40.
57. Norel R, Petrey D, Wolfson H, Nussinov R. Examination of shape complementarity in docking of unbound proteins. Proteins 1999;35:403â€“19.
58. Connolly ML. Analytical molecular surface calculation. J Appl Cryst 1983;16:548â€“58.
59. Connolly M. Solvent-accessible surface of proteins and nucleic acids. Sci 1983;221:709â€“13.
60. Norel R, Wolfson H, Nussinov R. Small molecular recognition: solid angles surface representation and shape complementarity. Comb Chem High Throughput Screen 1999;2:177â€“91.
61. Goodford PJ. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 1985;28:849â€“57.
62. Sayle RA, Milner-White EJ. Rasmol-Biomolecular graphics for all. Trends Biochem Sci 1995;20:374â€“6.
63. Sutcliffe MJ, Haneef I, Carney D, Blundell TL. Knowledge based modeling of homologous proteins, Part I: Three-dimensional frameworks derived from the simultaneous superposition of multiple structures. Protein Eng 1987;1:377â€“84.
64. Sutcliffe MJ, Hayes FR, Blundell TL. Knowledge based modeling of homologous proteins, Part II: Rules for the conformations of substituted side chains. Protein Eng 1987;1:385â€“92.
65. Sanchez R, Sali A. Comparative protein structure modeling. Introduction and practical examples with modeller. Methods Mol Biol 2000;143:97â€“129.
66. Vriend G. WhatIf: a molecular modeling and drug design program. J Mol Graph 1990;8:52â€“6.
67. Guex N, Diemand A, Peitsch MC. Protein modeling for all. Trends Biochem Sci 1999;24:364â€“7.
68. Brocklehurst SM, Perham RN. Prediction of the three-dimensional structures of the biotinylated domain from yeast pyruvate carboxylase and of the lipoylated H-protein from the pea leaf glycine cleavage system: a new automated method for the prediction of protein tertiary structure. Protein Sci 1993;4:626â€“39.
69. Levi F. Cancer prevention: epidemiology and perspectives. Eur J Cancer 1999;35(14):1912-24.
70. A Lee J, Rodriguez D, Dosemeci M, Albanes D, Hoover R, Blair A. Leisure-time physical activity and lung cancer: a meta-analysis. Cancer Causes Control 2005;16(4):389-97.
71. Rodriguez V, Tardon A, Kogevinas M, Prieto S, Cueto A, Garcia M, et al. Lung cancer risk in iron and steel foundry workers: a nested case control study in Asturias, Spain. Am J Ind Med 2000;38(6):644-50.
72. Philip Bonomi. Matrix metalloproteinases and matrix metalloproteinase inhibitors in lung cancer. Seminars in Oncology 2009;29(1):78-86.
73. Bhagavathi S. Analysis of Lung Cancer Micro array data identifies new potential genes targets for Inhibitor design. Anil Prakash Int J Adv Biotechnol Res 2012;3(4):824-34.
74. Bhagavathi S. In silico modeling and validation of differential expressed proteins in Lung Cancer. Anil Prakash Asian Pacific J Tropical Disease; 2012. p. S524-9.
75. Takai N, Hamanaka R, Yoshimatsu J, Miyakawa I. Polo-like kinases (Plks) and cancer. Oncogene 2005;24(2):287-91.
76. Sanga S, Frieboes H, Zheng X, Gatenby R, Bearer E, Cristini V. Predictive oncology: multidisciplinary, multi-scale in-silicomodeling linking phenotype, morphology and growth. Neuroimage 2007;37:120-34.
77. Kim B, Cheng H, Grishin N, Hor A. Web server to infer homology between proteins using sequence and structural similarity. Nucleic Acids Res 2009;37:532-38.
78. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: Architecture and applications. BMC Bioinformatics 2009;10:421.
79. Friedrich A, Ripp R, Garnier N, Bettler E, DelÃ©age G, Poch O, et al. Blast sampling for structural and functional analyses. BMC Bioinformatics 2007;8:8-62.
80. Greer J. Comparative model-building of the mammalian serine proteases. J Mol Biol 1981;153:1027â€“42.
81. Altschul SF, Madden TL, Schaffer AA, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997;25:3389â€“402.
82. Piedra D, Lois S, Cruz X. Preservation of protein clefts in comparative models. BMC Struct Biol 2008;8:2.
83. Bairoch A, Boeckmann B, Ferro S, Gasteiger E. Swiss-Prot Brief Bioinform 2004;5:39-55.
84. The Uni Prot Consortium The Universal Protein Resource (UniProt). Nucleic Acids Res 2007;36:190-5.
85. Boeckmann B, Blatter C, Famiglietti L, Hinz U, Lane L, Roechert B, et al. Comptes Rendus Biologies 2005;328:882-99.
86. Dowlathabad M, Anuraj N, Mukesh Y, Showmy S, Disha P. Comparative modeling of methylentetrahydrofolate reductase (MTHFR) enzyme and its mutational assessment: in silico approach. Int J Bioinformatics Res 2010;2(1):05-09.
87. Laskowski RA, Watson JD, Thornton JM. Pro Func: a server for predicting protein function from 3D structure. Nucleic Acids Res 2005;33:89â€“93.
88. Laskowski RA, Rullmann JA, MacArthur MW, Kaptein R, Thornton JM. AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. J Biomol NMR 1996;8:477â€“86.
89. Wiederstein M, Sippl MJ. Pro SA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 2007;35:407â€“10.
90. LuÂ¨thy R, Bowie JU, Eisenberg D. Assessment of protein models with three-dimensional profiles. Nature 1992;356:83â€“5.
91. Burge C, Karlin S. Prediction of complete gene structures in genomic DNA. J Mol Biol 1997;268:78-94.
92. Darryl L, Scott M. In Silico technologies in drug target identification and validation. Taylor Francis Group, LLC; 2006.
93. Bleicher KH, Bohm Hj, muller K, Alanine AI. Hit and Lead generation: beyond high-throughput screening. Nat Rev Drug Discov 2003;2(5):369-78.
94. Robert AG. Hit and lead identification: Integrated technology based approaches. Drug Discovery Today 2006;3(4):367-75.
95. Bhagavathi S, Anil Prakash. Molecular modeling and drug discovery of potential inhibitors for anti cancer target gene PLK-Polo like Kinase1. Int J Pharm Bio Sci 2014;5(1):(B)342â€“52.
96. Bhagavathi S, Anil Prakash. Molecular docking of Lung cancer proteins against specific drug targets. World J Pharm Res 3(3);4248-62.
97. H Godden JW, Stahura FL, Bajorath J. Statistical analysis of computational docking of large compound databases to distinct protein binding sites. J Comput Chem 1999;20:1634â€“43.