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 (2001) microRNAs: tiny regulators with great potential. Cell 107: pp. 823-826 CrossRef
- Bartel, DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116: pp. 281-297 CrossRef
- Yoon, S, De, MG (2006) Computational identification of microRNAs and their targets. Birth Defects Res C Embryo Today 78: pp. 118-128 CrossRef
- Chaudhuri, K, Chatterjee, R (2007) MicroRNA detection and target prediction: integration of computational and experimental approaches. DNA Cell Biol 26: pp. 321-337 CrossRef
- Mendes, ND, Freitas, AT, Sagot, MF (2009) Current tools for the identification of miRNA genes and their targets. Nucleic Acids Res 37: pp. 2419-2433 CrossRef
- Filipowicz, W, Bhattacharyya, SN, Sonenberg, N (2008) Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight?. Nat Rev Genet 9: pp. 102-114 CrossRef
- Chekulaeva, M, Filipowicz, W (2009) Mechanisms of miRNA-mediated post-transcriptional regulation in animal cells. Curr Opin Cell Biol 21: pp. 452-460 CrossRef
- Cho, WC (2007) OncomiRs: the discovery and progress of microRNAs in cancers. Mol Cancer 6: pp. 60 CrossRef
- Scaria, V, Hariharan, M, Brahmachari, SK, Maiti, S, Pillai, B (2007) microRNA: an emerging therapeutic. ChemMedChem 2: pp. 789-792 CrossRef
- Liu, Z, Sall, A, Yang, D (2008) MicroRNA: An emerging therapeutic target and intervention tool. Int J Mol Sci 9: pp. 978-999 CrossRef
- Roshan, R, Ghosh, T, Scaria, V, Pillai, B (2009) MicroRNAs: novel therapeutic targets in neurodegenerative diseases. Drug Discov Today 14: pp. 1123-1129 CrossRef
- Mishra, PK, Tyagi, N, Kumar, M, Tyagi, SC (2009) MicroRNAs as a therapeutic target for cardiovascular diseases. J Cell Mol Med 13: pp. 778-789 CrossRef
- Gumireddy, K, Young, DD, Xiong, X, Hogenesch, JB, Huang, Q, Deiters, A (2008) Small-molecule inhibitors of microrna miR-21 function. Angew Chem Int Ed Engl 47: pp. 7482-7484 CrossRef
- Melo, S, Villanueva, A, Moutinho, C, Davalos, V, Spizzo, R, Ivan, C (2011) 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 108: pp. 4394-4399 CrossRef
- Shan, G, Li, Y, Zhang, J, Li, W, Szulwach, KE, Duan, R (2008) A small molecule enhances RNA interference and promotes microRNA processing. Nat Biotechnol 26: pp. 933-940 CrossRef
- Tan, GS, Chiu, CH, Garchow, BG, Metzler, D, Diamond, SL, Kiriakidou, M (2012) Small molecule inhibition of RISC loading. ACS Chem Biol 7: pp. 403-410 CrossRef
- Schierz, AC (2009) Virtual screening of bioassay data. J Cheminform 1: pp. 21 CrossRef
- Melville, JL, Burke, EK, Hirst, JD (2009) Machine Learning in Virtual Screening. Comb Chem High Throughput Screen 12: pp. 332-343 CrossRef
- Periwal, V, Rajappan, JK, Jaleel, AU, Scaria, V (2011) Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets. BMC Res Notes 4: pp. 504 CrossRef
- Periwal, V, Kishtapuram, S, Scaria, V (2012) Computational models for in-vitro anti-tubercular activity of molecules based on high-throughput chemical biology screening datasets. BMC Pharmacol 12: pp. 1 CrossRef
- Cao, Y, Jiang, T, Girke, T (2008) A maximum common substructure-based algorithm for searching and predicting drug-like compounds. Bioinformatics 24: pp. i366-i374 CrossRef
- Stahl, M, Mauser, H (2005) Database clustering with a combination of fingerprint and maximum common substructure methods. J Chem Inf Model 45: pp. 542-548 CrossRef
- Knox, C, Law, V, Jewison, T, Liu, P, Ly, S, Frolkis, A (2011) DrugBank 3.0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Res 39: pp. D1035-D1041 CrossRef
- Bernstein, FC, Koetzle, TF, Williams, GJ, Meyer, EF, Brice, MD, Rodgers, JR (1977) The Protein Data Bank: a computer-based archival file for macromolecular structures. J Mol Biol 112: pp. 535-542 CrossRef
- Collie, GW, Sparapani, S, Parkinson, GN, Neidle, S (2011) Structural basis of telomeric RNA quadruplex–acridine ligand recognition. J Am Chem Soc 133: pp. 2721-2728 CrossRef
- Young, DD, Connelly, CM, Grohmann, C, Deiters, A (2010) Small molecule modifiers of microRNA miR-122 function for the treatment of hepatitis C virus infection and hepatocellular carcinoma. J Am Chem Soc 132: pp. 7976-7981 CrossRef
- Watashi, K, Yeung, ML, Starost, MF, Hosmane, RS, Jeang, KT (2010) Identification of small molecules that suppress microRNA function and reverse tumorigenesis. J Biol Chem 285: pp. 24707-24716 CrossRef
- Liu, PT, Wheelwright, M, Teles, R, Komisopoulou, E, Edfeldt, K, Ferguson, B (2012) MicroRNA-21 targets the vitamin D-dependent antimicrobial pathway in leprosy. Nat Med 18: pp. 267-273 CrossRef
- Xu, G, Zhang, Y, Jia, H, Li, J, Liu, X, Engelhardt, JF (2009) Cloning and identification of microRNAs in bovine alveolar macrophages. Mol Cell Biochem 332: pp. 9-16 CrossRef
- Wang, C, Yang, S, Sun, G, Tang, X, Lu, S, Neyrolles, O (2011) Comparative miRNA expression profiles in individuals with latent and active tuberculosis. PLoS One 6: pp. e25832 CrossRef
- Wang, Y, Xiao, J, Suzek, TO, Zhang, J, Wang, J, Bryant, SH (2009) PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 37: pp. W623-W633 CrossRef
- Thompson, JF, Hayes, LS, Lloyd, DB (1991) Modulation of firefly luciferase stability and impact on studies of gene regulation. Gene 103: pp. 171-177 CrossRef
- Auld, DS, Thorne, N, Nguyen, DT, Inglese, J (2008) A specific mechanism for nonspecific activation in reporter-gene assays. ACS Chem Biol 3: pp. 463-470 CrossRef
- Liu, K, Feng, J, Young, SS (2005) PowerMV: a software environment for molecular viewing, descriptor generation, data analysis and hit evaluation. J Chem Inf Model 45: pp. 515-522 CrossRef
- Sud, M (2010) MayaChemTools.
- Blagus, R, Lusa, L (2010) Class prediction for high-dimensional class-imbalanced data. BMC Bioinforma 11: pp. 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 (1997) Bayesian Network Classifiers. Mach Learn 29: pp. 131-163 CrossRef
- Breiman, L (2001) Random Forests. Mach Learn 45: pp. 5-32 CrossRef
- Budapest H. Library MCS, version 0.7.
- Chemaxon: Budapest H. Jcsearch version 5.8.2. .
- release 3.1.2. OpenEye Scientific Software, Inc, Santa Fe, NM, USA
- version 4.1.1. OpenEye Scientific Software, Inc, Santa Fe, NM, USA
- Santa Fe, NM, USA.
- 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