Computational analysis and predictive modeling of small molecule modulators of microRNA
MicroRNAs (miRNA) are small endogenously transcribed regulatory RNA which modulates gene expression at a post transcriptional level. These small RNAs have now been shown to be critical regulators in a number of biological processes in the cell including pathophysiology of diseases like cancers. The increasingly evident roles of microRNA in disease processes have also motivated attempts to target them therapeutically. Recently there has been immense interest in understanding small molecule mediated regulation of RNA, including microRNA.
We have used publicly available datasets of high throughput screens on small molecules with potential to inhibit microRNA. We employed computational methods based on chemical descriptors and machine learning to create predictive computational models for biological activity of small molecules. We further used a substructure based approach to understand common substructures potentially contributing to the activity.
We generated computational models based on Naïve Bayes and Random Forest towards mining small RNA binding molecules from large molecular datasets. We complement this with substructure based approach to identify and understand potentially enriched substructures in the active dataset. We use this approach to identify miRNA binding potential of a set of approved drugs, suggesting a probable novel mechanism of off-target activity of these drugs. To the best of our knowledge, this is the first and most comprehensive computational analysis towards understanding RNA binding activities of small molecules and predictive modeling of these activities.
- Ambros V: microRNAs: tiny regulators with great potential. Cell 2001, 107:823–826. CrossRef
- Bartel DP: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004, 116:281–297. CrossRef
- Yoon S, De MG: Computational identification of microRNAs and their targets. Birth Defects Res C Embryo Today 2006, 78:118–128. CrossRef
- Chaudhuri K, Chatterjee R: MicroRNA detection and target prediction: integration of computational and experimental approaches. DNA Cell Biol 2007, 26:321–337. CrossRef
- Mendes ND, Freitas AT, Sagot MF: Current tools for the identification of miRNA genes and their targets. Nucleic Acids Res 2009, 37:2419–2433. CrossRef
- Filipowicz W, Bhattacharyya SN, Sonenberg N: Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight? Nat Rev Genet 2008, 9:102–114. CrossRef
- Chekulaeva M, Filipowicz W: Mechanisms of miRNA-mediated post-transcriptional regulation in animal cells. Curr Opin Cell Biol 2009, 21:452–460. CrossRef
- Cho WC: OncomiRs: the discovery and progress of microRNAs in cancers. Mol Cancer 2007, 6:60. CrossRef
- Scaria V, Hariharan M, Brahmachari SK, Maiti S, Pillai B: microRNA: an emerging therapeutic. ChemMedChem 2007, 2:789–792. CrossRef
- Liu Z, Sall A, Yang D: MicroRNA: An emerging therapeutic target and intervention tool. Int J Mol Sci 2008, 9:978–999. CrossRef
- Roshan R, Ghosh T, Scaria V, Pillai B: MicroRNAs: novel therapeutic targets in neurodegenerative diseases. Drug Discov Today 2009, 14:1123–1129. CrossRef
- Mishra PK, Tyagi N, Kumar M, Tyagi SC: MicroRNAs as a therapeutic target for cardiovascular diseases. J Cell Mol Med 2009, 13:778–789. CrossRef
- Gumireddy K, Young DD, Xiong X, Hogenesch JB, Huang Q, Deiters A: Small-molecule inhibitors of microrna miR-21 function. Angew Chem Int Ed Engl 2008, 47:7482–7484. CrossRef
- Melo S, Villanueva A, Moutinho C, Davalos V, Spizzo R, Ivan C, et al.: Small molecule enoxacin is a cancer-specific growth inhibitor that acts by enhancing TAR RNA-binding protein 2-mediated microRNA processing. Proc Natl Acad Sci U S A 2011, 108:4394–4399. CrossRef
- Shan G, Li Y, Zhang J, Li W, Szulwach KE, Duan R, et al.: A small molecule enhances RNA interference and promotes microRNA processing. Nat Biotechnol 2008, 26:933–940. CrossRef
- Tan GS, Chiu CH, Garchow BG, Metzler D, Diamond SL, Kiriakidou M: Small molecule inhibition of RISC loading. ACS Chem Biol 2012, 7:403–410. CrossRef
- Schierz AC: Virtual screening of bioassay data. J Cheminform 2009, 1:21. CrossRef
- Melville JL, Burke EK, Hirst JD: Machine Learning in Virtual Screening. Comb Chem High Throughput Screen 2009, 12:332–343. CrossRef
- Periwal V, Rajappan JK, Jaleel AU, Scaria V: Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets. BMC Res Notes 2011, 4:504. CrossRef
- Periwal V, Kishtapuram S, Scaria V: Computational models for in-vitro anti-tubercular activity of molecules based on high-throughput chemical biology screening datasets. BMC Pharmacol 2012, 12:1. CrossRef
- Cao Y, Jiang T, Girke T: A maximum common substructure-based algorithm for searching and predicting drug-like compounds. Bioinformatics 2008, 24:i366-i374. CrossRef
- Stahl M, Mauser H: Database clustering with a combination of fingerprint and maximum common substructure methods. J Chem Inf Model 2005, 45:542–548. CrossRef
- Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, et al.: DrugBank 3.0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Res 2011, 39:D1035-D1041. CrossRef
- Bernstein FC, Koetzle TF, Williams GJ, Meyer EF, Brice MD, Rodgers JR, et al.: The Protein Data Bank: a computer-based archival file for macromolecular structures. J Mol Biol 1977, 112:535–542. CrossRef
- Collie GW, Sparapani S, Parkinson GN, Neidle S: Structural basis of telomeric RNA quadruplex–acridine ligand recognition. J Am Chem Soc 2011, 133:2721–2728. CrossRef
- Young DD, Connelly CM, Grohmann C, Deiters A: Small molecule modifiers of microRNA miR-122 function for the treatment of hepatitis C virus infection and hepatocellular carcinoma. J Am Chem Soc 2010, 132:7976–7981. CrossRef
- Watashi K, Yeung ML, Starost MF, Hosmane RS, Jeang KT: Identification of small molecules that suppress microRNA function and reverse tumorigenesis. J Biol Chem 2010, 285:24707–24716. CrossRef
- Liu PT, Wheelwright M, Teles R, Komisopoulou E, Edfeldt K, Ferguson B, et al.: MicroRNA-21 targets the vitamin D-dependent antimicrobial pathway in leprosy. Nat Med 2012, 18:267–273. CrossRef
- Xu G, Zhang Y, Jia H, Li J, Liu X, Engelhardt JF, et al.: Cloning and identification of microRNAs in bovine alveolar macrophages. Mol Cell Biochem 2009, 332:9–16. CrossRef
- Wang C, Yang S, Sun G, Tang X, Lu S, Neyrolles O, et al.: Comparative miRNA expression profiles in individuals with latent and active tuberculosis. PLoS One 2011, 6:e25832. CrossRef
- Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH: PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 2009, 37:W623-W633. CrossRef
- Thompson JF, Hayes LS, Lloyd DB: Modulation of firefly luciferase stability and impact on studies of gene regulation. Gene 1991, 103:171–177. CrossRef
- Auld DS, Thorne N, Nguyen DT, Inglese J: A specific mechanism for nonspecific activation in reporter-gene assays. ACS Chem Biol 2008, 3:463–470. CrossRef
- Liu K, Feng J, Young SS: PowerMV: a software environment for molecular viewing, descriptor generation, data analysis and hit evaluation. J Chem Inf Model 2005, 45:515–522. CrossRef
- Sud M: MayaChemTools. 2010. http://www.mayachemtools.org/
- Blagus R, Lusa L: Class prediction for high-dimensional class-imbalanced data. BMC Bioinforma 2010, 11:523. CrossRef
- Elkan C: The Foundations of Cost-Sensitive Learning. , 973–978.
- Bouckaert RR, Frank E, Hall MA, Holmes G, Pfahringer B, Reutemann P, et al.: Weka -Experiences with a Java Open-Source Project. J Mach Learn Res 2010,:2533–2541.
- Friedman N, Geiger D, GoldSzmidt M: Bayesian Network Classifiers. Mach Learn 1997, 29:131–163. CrossRef
- Breiman L: Random Forests. Mach Learn 2001, 45:5–32. CrossRef
- Chemaxon: Budapest H. Library MCS, version 0.7. 2008.
- Chemaxon: Budapest H. Jcsearch version 5.8.2. .
- vROCS: release 3.1.2. OpenEye Scientific Software, Inc, Santa Fe, NM, USA; 2010. www.eyesopen.com
- VIDA: version 4.1.1. OpenEye Scientific Software, Inc, Santa Fe, NM, USA; 2010. www.eyesopen.com
- OpenEye Scientific Software, Inc: Santa Fe, NM, USA. 2010. www.eyesopen.com
- Computational analysis and predictive modeling of small molecule modulators of microRNA
- Open Access
- Available under Open Access This content is freely available online to anyone, anywhere at any time.
Journal of Cheminformatics
- Online Date
- August 2012
- Online ISSN
- Chemistry Central
- Additional Links
- Machine learning
- Maximum common substructure (MCS)
- Author Affiliations
- 1. Open Source Drug Discovery Unit, Council of Scientific and Industrial Research (CSIR), Anusandhan Bhavan, 2 Rafi Marg, New Delhi, 110001, India
- 2. GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi, 110007, India