research-article

Molecular property diagnostic suite for diabetes mellitus (MPDS^DM): : An integrated web portal for drug discovery and drug repurposing

Authors:

Anamika Singh Gaur,

Selvaraman Nagamani,

Karunakar Tanneeru,

Dmitry Druzhilovskiy,

Anastassia Rudik,

Vladimir Poroikov,

G. Narahari SastryAuthors Info & Claims

Volume 85, Issue C

Pages 114 - 125

https://doi.org/10.1016/j.jbi.2018.08.003

Published: 01 September 2018 Publication History

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Highlights

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Developed an open source diabetes-specific web portal along with drug discovery tools.

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Developed a diabetes gene database.

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Drug repurposing for type 1 and 2 diabetes mellitus.

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Developed gene library for Homo sapiens with corresponding gene ID and card.

Abstract

Molecular Property Diagnostic Suite - Diabetes Mellitus (MPDS^DM) is a Galaxy-based, open source disease-specific web portal for diabetes. It consists of three modules namely (i) data library (ii) data processing and (iii) data analysis tools. The data library (target library and literature) module provide extensive and curated information about the genes involved in type 1 and type 2 diabetes onset and progression stage (available at http://www.mpds-diabetes.in). The database also contains information on drug targets, biomarkers, therapeutics and associated genes specific to type 1, and type 2 diabetes. A unique MPDS identification number has been assigned for each gene involved in diabetes mellitus and the corresponding card contains chromosomal data, gene information, protein UniProt ID, functional domains, druggability and related pathway information. One of the objectives of the web portal is to have an open source data repository that contains all information on diabetes and use this information for developing therapeutics to cure diabetes. We also make an attempt for computational drug repurposing for the validated diabetes targets. We performed virtual screening of 1455 FDA approved drugs on selected 20 type 1 and type 2 diabetes proteins using docking protocol and their biological activity was predicted using “PASS Online” server (http://www.way2drug.com/passonline) towards anti-diabetic activity, resulted in the identification of 41 drug molecules. Five drug molecules (which are earlier known for anti-malarial/microbial, anti-viral, anti-cancer, anti-pulmonary activities) were proposed to have a better repurposing potential for type 2 anti-diabetic activity and good binding affinity towards type 2 diabetes target proteins.

References

[1]

Diabetes programme - World Health Organization. <www.who.int/diabetes/en/> (accessed 20th April 2018).

[2]

W. Rodger, Insulin-dependent (type 1) diabetes mellitus, CMAJ 145 (1991) 1227–1237.

[3]

P.Z. Zimmet, The pathogenesis and prevention of diabetes in adults: genes, autoimmunity, and demography, Diab. Care 18 (1995) 1050–1064.

[4]

T. Kzuya, A. Mastsuda, Classification of diabetes on the basis of etiologies versus degree of insulin deficiency, Diab. Care 20 (1997) 219–220.

[5]

J.E. Gerich, The genetic basis of type 2 diabetes mellitus: impaired insulin secretion versus impaired insulin sensitivity, Endocr. Rev. 19 (1998) 491–503.

[6]

S. Janardhan, G.N. Sastry, Dipeptidyl peptidase IV inhibitors: a new paradigm in type 2 diabetes treatment, Curr. Drug Targ. 15 (2004) 600–621.

[7]

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. Singam, G.P.S. Raghava, H. Sailaja, H. Jangra, K. Raithatha, K. Tanneeru, K. Chaudhary, M. Karthikeyan, M. Prasanthi, N. Kumar, N. Yedukondalu, N.K. Rajput, P.S. Saranya, P. Narang, P. Dutta, R.V. Krishnan, R. Sharma, R. Srinithi, R. Mishra, S. Hemasri, S. Singh, S. Venkatesan, S. Kumar, U.C.A. Jaleel, V. Khedkar, Y. Joshi, G.N. Sastry, Assessing therapeutic potential of molecules: molecular property diagnostic suite for tuberculosis (MPDSTB), J. Chem. Sci. 129 (2017) 515–531.

[8]

S. Nagamani, A.S. Gaur, K. Tanneeru, G. Muneeswaran, S.S. Madugula, MPDS Consortium, D. Druzhilovskiy, V.V. Poroikov, G.N. Sastry, Molecular property diagnostic suite (MPDS): Development of disease-specific open source web portals for drug discovery, SAR QSAR Environ. Res. 28 (2017) 913–926.

[9]

D. Blankenberg, G. Von Kuster, N. Coraor, G. Ananda, R. Lazarus, M. Mangan, A. Nekrutenko, J. Taylor, Galaxy: a web-based genome analysis tool for experimentalists, Current Protocol in Molecular Biology, Unit 19.10 (2010) 1–21 (Chapter 19).

[10]

E. Afgan, D. Bake, 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, J. Goecks, The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update, Nucl. Acids Res. 44 (2016) (2016) W3–W10.

[11]

D. Blankenberg, G.V. Kuster, E. Bouvier, D. Baker, E. Afgan, N. Stoler, J. Galaxy Team, A. Nekrutenko Taylor, Dissemination of scientific software with galaxy toolshed, Geno. Biol. 15 (2014) 403.

[12]

K. Gopinath, R. Jayakumararaj, M. Karthikeyan, DAPD: a knowledgebase for diabetes associated proteins, IEEE/ACM. Trans. Comput. Biol. Bioinform. 12 (2015) 604–610.

[13]

G. Jin, S.T. Wong, Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines, Drug Discov. Today 19 (2014) 637–644.

[14]

S. Ekins, J. Mestres, B. Testa, In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling, Br. J. Pharmacol. 152 (2007) 9–20.

[15]

P. Kolb, R.S. Ferreira, J.J. Irwin, B.K. Shoichet, Docking and chemoinformatic screens for new ligands and targets, Curr. Opin. Biotechnol. 20 (2009) 429–436.

[16]

S.J. Swamidass, Mining small-molecule screens to repurpose drugs, Brief Bioinform. 12 (2011) 327–335.

[17]

S. Alaimo, A. Pulvirenti, R. Giugno, A. Ferro, Drug–target interaction prediction through domain-tuned network-based inference, Bioinformatics 29 (2013) 2004–2008.

[18]

L. Yang, P. Agarwal, Systematic drug repositioning based on clinical side-effects, PLoS One 6 (2011) e28025.

[19]

Y. Chen, R. Xu, Drug repurposing for glioblastoma based on molecular subtypes, J. Biomed. Inform. 64 (2016) 131–138.

[20]

H. Bisgin, Z. Liu, R. Kelly, H. Fang, X. Xu, W. Tong, Investigating drug repositioning opportunities in FDA drug labels through topic modeling, BMC Bioinform. 13 (2012) S6.

[21]

J. Yang, Z. Li, X. Fan, Y. Cheng, Drug-disease association and drug-repositioning predictions in complex diseases using causal inference-probabilistic matrix factorization, J. Chem. Inform. Model. 54 (2014) 2562–2569.

[22]

S. Henry, B.T. McInnes, Literature based discovery: models, methods, and trends, J. Biomed. Inform. 74 (2017) 20–32.

Digital Library

[23]

E.A. Oral, S.M. Reilly, A.V. Gomez, R. Meral, L. Butz, N. Ajluni, T.L. Chenevert, E. Korytnaya, A.H. Neidert, R. Hench, D.D. Rus, J.F. Horowitz, B. Poirier, P. Zhao, K. Lehmann, M. Jain, R. Yu, C. Liddle, M. Ahmadian, M. Downes, R.M. Evans, A.R. Saltiel, Inhibition of IKKɛ and TBK1 improves glucose control in a subset of patients with type 2 diabetes, Cell. Metab. 26 (2017) 157–170.e7.

[24]

A. Hamosh, A.F. Scott, J. Amberger, C. Bocchini, D. Valle, V.A. McKusick, Online Mendelian Inheritance in Man (OMIM) a knowledgebase of human genes and genetic disorders, Nucl. Acids Res. 30 (2002) 52–55.

[25]

R.D. Finn, P. Coggill, R.Y. Eberhardt, S.R. Eddy, J. Mistry, A.L. Mitchell, S.C. Potter, M. Punta, M. Qureshi, A. Sangrador-Vegas, G.A. Salazar, J. Tate, A. Bateman, The Pfam protein families database: towards a more sustainable future, Nucl. Acids Res. 44 (2016) D279–285.

[26]

J. Schultz, F. Milpetz, P. Bork, C.P. Ponting, SMART a simple modular architecture research tool: identification of signaling domains, Proc. Natl. Acad. Sci. USA 95 (1998) 5857–5864.

[27]

D. Szklarczyk, A. Franceschini, S. Wyder, K. Forslund, D. Heller, J. Huerta-Cepas, M. Simonovic, A. Roth, A. Santos, K.P. Tsafou, M. Kuhn, P. Bork, L.J. Jensen, C. von Mering, STRING v10: protein-protein interaction networks integrated over the tree of life, Nucl. Acids Res. 43 (2015) D447–452.

[28]

M. Kanehisa, S. Goto, M. Hattori, K.F. Aoki-Kinoshita, M. Itoh, S. Kawashima, T. Katayama, M. Araki, M. Hirakawa, From genomics to chemical genomics: new developments in KEGG, Nucl. Acids Res. 34 (2006) D354–D357.

[29]

P. Badrinarayan, G.N. Sastry, Virtual high-throughput screening in new lead identification, Comb. Chem. High Throug. Screen. 14 (2011) 840–860.

[30]

A.S. Reddy, S. Priyadarshini Pati, P. Praveen Kumar, H.N. Pradeep, G.N. Sastry, Virtual screening in drug discovery - a computational perspective, Curr. Protein Pept. Sci. 8 (2007) 329–351.

[31]

M.F. Sanner, Python: a programming language for software integration and development, J. Mol. Graph. 17 (1999) 57–61.

[32]

O. Trott, A.J. Olson, AutoDockVina: improving the speed and accuracy of docking with a new scoring function efficient optimization and multi-threading, J. Comp. Chem. 31 (2010) 455–461.

[33]

A. Lagunin, A. Stepanchikova, D. Filimonov, V.V. Poroikov, PASS: prediction of activity spectra for biologically active substances, Bioinformatics 16 (2000) 747–748.

[34]

D.A. Filimonov, A.A. Lagunin, T.A. Gloriozova, A.V. Rudik, D.S. Druzhilovskiy, P.V. Pogodin, V.V. Poroikov, Prediction of the biological activity spectra of organic compounds using the PASS online web resource, Chem. Heterocycl. Compd. 50 (2014) 444–457.

[35]

D.A. Filimonov, D.S. Druzhilovskiy, A.A. Lagunin, T.A. Gloriozova, A.V. Rudik, A.V. Dmitriev, P.V. Pogodin, V.V. Poroikov, Computer-aided prediction of biological activity spectra for chemical compounds: opportunities and limitations, Biomed. Chem. Res. Methods 1 (2018) e00004.

[36]

V.M. Bezhentsev, D.S. Druzhilovskiy, S.M. Ivanov, D.A. Filimonov, G.N. Sastry, V.V. Poroikov, Web resources for discovery and development of new medicines, Pharm. Chem. 51 (2017) 91–99.

[37]

K.A. Murtazalieva, D.S. Druzhilovskiy, R.K. Goel, G.N. Sastry, V.V. Poroikov, How good are publicly available web services that predict bioactivity profiles for drug repurposing?, SAR QSAR Environ. Res. 28 (2017) 843–862.

[38]

B. Hess, C. Kutzner, D. van der Spoel, E. Lindahl, GROMACS 4: algorithms for highly efficient load-balanced and scalable molecular simulation, J. Chem. Theory Comput. 4 (2008) 435–447.

[39]

D. van der Spoel, E. Lindahl, B. Hess, G. Groenhof, A.E. Mark, H.J. Berendsen, GROMACS: fast flexible and free, J. Comp. Chem. 26 (2005) 1701–1718.

[40]

C. Oostenbrink, A. Villa, A.E. Mark, W.F. van Gunsteren, A biomolecular force field based on the free enthalpy of hydration and solvation: the GROMOS force-field parameter sets 53A5 and 53A6, J. Comp. Chem. 25 (2004) 1656–1676.

[41]

A.W. Schüttelkopf, D.M. van Aalten, PRODRG - a tool for high-throughput crystallography of protein-ligand complexes, Acta Crystallogr. D. Struct. Biol. 60 (2004) 1355–1363.

[42]

T. Darden, D. York, L. Pedersen, Particle mesh Ewald: an Nlog (N) method for Ewald sums in large systems, J. Chem. Phys. 98 (1993) 10089–10092.

[43]

U. Essmann, L. Perera, M.L. Berkowitz, T. Darden, H. Lee, G. Lee, A smooth particle meshes Ewald method, J. Chem. Phys. 103 (1995) 8577–8593.

[44]

G. Bussi, D. Donadio, M. Parrinello, Canonical sampling through velocity rescaling, J. Chem. Phys. 126 (2007) 014101.

[45]

M. Parrinello, A. Rahman, Polymorphic transitions in single crystals: a new molecular dynamics method, J. Appl. Phys. 52 (1981) 7182–7190.

[46]

B. Hess, H. Bekker, H.J.C. Berendsen, J.G.E.M. Fraaije, LINCS: a linear constraint solver for molecular simulations, J. Comp. Chem. 18 (1997) 1463–1472.

[47]

R. Kumari, R. Kumar, Open Source Drug Discovery Consortium Lynn A, g_mmpbsa-a GROMACS tool for high-throughput MM-PBSA calculations, J. Chem. Inform. Model. 54 (2014) 1951–1962.

[48]

N.A. Baker, D. Sept, S. Joseph, M.J. Holst, J.A. McCammon, Electrostatics of nanosystems: application to microtubules and the ribosome, Proc. Natl. Acad. Sci. USA 98 (2001) 10037–10041.

[49]

H.K. Srivastava, G.N. Sastry, Efficient estimation of MMGBSA based binding energies for DNA and aromatic furan amidino derivatives, J. Biomol. Struct. Dyn. 31 (2013) 522–537.

[50]

H.K. Srivastava, G.N. Sastry, A molecular dynamics investigation on a series of HIV protease inhibitors: assessing the performance of MM-PBSA and MM-GBSA approaches, J. Chem. Inform. Model. 52 (2012) 3088–3098.

[51]

N. Tiwari, A.K. Thakur, V. Kumar, A. Dey, V. Kumar, Therapeutic targets for diabetes mellitus: an update, Clin. Pharmacol. Biopharm. 3 (2014) 117.

Cited By

Mazumdar BDeva Sarma PMahanta HSastry G(2023)Machine learning based dynamic consensus model for predicting blood-brain barrier permeabilityComputers in Biology and Medicine10.1016/j.compbiomed.2023.106984160:COnline publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.compbiomed.2023.106984
Madugula SJohn LNagamani SGaur APoroikov VSastry G(2021)Molecular descriptor analysis of approved drugs using unsupervised learning for drug repurposingComputers in Biology and Medicine10.1016/j.compbiomed.2021.104856138:COnline publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1016/j.compbiomed.2021.104856

Index Terms

Molecular property diagnostic suite for diabetes mellitus (MPDS^DM): An integrated web portal for drug discovery and drug repurposing
1. Applied computing
  1. Life and medical sciences
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Journal of Biomedical Informatics

Journal of Biomedical Informatics Volume 85, Issue C

Sep 2018

208 pages

ISSN:1532-0464

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Elsevier Inc.

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Elsevier Science

San Diego, CA, United States

Publication History

Published: 01 September 2018

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Cited By

Mazumdar BDeva Sarma PMahanta HSastry G(2023)Machine learning based dynamic consensus model for predicting blood-brain barrier permeabilityComputers in Biology and Medicine10.1016/j.compbiomed.2023.106984160:COnline publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.compbiomed.2023.106984
Madugula SJohn LNagamani SGaur APoroikov VSastry G(2021)Molecular descriptor analysis of approved drugs using unsupervised learning for drug repurposingComputers in Biology and Medicine10.1016/j.compbiomed.2021.104856138:COnline publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1016/j.compbiomed.2021.104856

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