Skip to main content
Top

2019 | OriginalPaper | Chapter

Turbo Analytics: Applications of Big Data and HPC in Drug Discovery

Authors : Rajendra R. Joshi, Uddhavesh Sonavane, Vinod Jani, Amit Saxena, Shruti Koulgi, Mallikarjunachari Uppuladinne, Neeru Sharma, Sandeep Malviya, E. P. Ramakrishnan, Vivek Gavane, Avinash Bayaskar, Rashmi Mahajan, Sudhir Pandey

Published in: Structural Bioinformatics: Applications in Preclinical Drug Discovery Process

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In this current age of data-driven science, perceptive research is being carried out in the areas of genomics, network and metabolic biology, human, animal, organ and tissue models of drug toxicity, witnessing or capturing key biological events or interactions for drug discovery. Drug designing and repurposing involves understanding of ligand orientations for proper binding to the target molecules. The crucial requirement of finding right pose of small molecule in ligand–protein complex is done using drug docking and simulation methods. The domains of biology like genomics, biomolecular structure dynamics, and drug discovery are capable of generating vast molecular data in range of terabytes to petabytes. The analysis and visualization of this data pose a great challenge to the researchers and needs to be addressed in an accelerated and efficient way. So there is continuous need to have advanced analytics platform and algorithms which can perform analysis of this data in a faster way. Big data technologies may help to provide solutions for these problems of molecular docking and simulations.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Schmidt B, Hildebrandt A (2017) Next-generation sequencing: big data meets high performance computing. Drug Discov Today 22:712–717PubMedCrossRef Schmidt B, Hildebrandt A (2017) Next-generation sequencing: big data meets high performance computing. Drug Discov Today 22:712–717PubMedCrossRef
2.
go back to reference Tripathi R et al (2016) Next-generation sequencing revolution through big data analytics. Front Life Sci 9(2):119–149CrossRef Tripathi R et al (2016) Next-generation sequencing revolution through big data analytics. Front Life Sci 9(2):119–149CrossRef
3.
go back to reference Taglang G, Jackson DB (2016) Use of “big data” in drug discovery and clinical trials. Gynecol Oncol 141(1):17–23PubMedCrossRef Taglang G, Jackson DB (2016) Use of “big data” in drug discovery and clinical trials. Gynecol Oncol 141(1):17–23PubMedCrossRef
4.
go back to reference Leyens Lada et al (2017) Use of big data for drug development and for public and personal health and care. Genet Epidemiol 41(1):51–60PubMedCrossRef Leyens Lada et al (2017) Use of big data for drug development and for public and personal health and care. Genet Epidemiol 41(1):51–60PubMedCrossRef
7.
go back to reference Zhao S et al (2017) Cloud computing for next-generation sequencing data analysis. In: Cloud computing-architecture and applications. InTech, London Zhao S et al (2017) Cloud computing for next-generation sequencing data analysis. In: Cloud computing-architecture and applications. InTech, London
8.
go back to reference Bhuvaneshwar K et al (2015) A case study for cloud based high throughput analysis of NGS data using the globus genomics system. Comput Struct Biotechnol J 13:64–74PubMedCrossRef Bhuvaneshwar K et al (2015) A case study for cloud based high throughput analysis of NGS data using the globus genomics system. Comput Struct Biotechnol J 13:64–74PubMedCrossRef
9.
go back to reference da Fonseca RR et al (2016) Next-generation biology: sequencing and data analysis approaches for non-model organisms. Mar Genomics 30:3–13PubMedCrossRef da Fonseca RR et al (2016) Next-generation biology: sequencing and data analysis approaches for non-model organisms. Mar Genomics 30:3–13PubMedCrossRef
11.
go back to reference Shaw DE et al (2008) Anton, a special-purpose machine for molecular dynamics simulation. Commun ACM 51(7):91–97CrossRef Shaw DE et al (2008) Anton, a special-purpose machine for molecular dynamics simulation. Commun ACM 51(7):91–97CrossRef
12.
go back to reference Bernardi RC, Melo MCR, Schulten K (2015) Enhanced sampling techniques in molecular dynamics simulations of biological systems. Biochimica et Biophysica Acta (BBA) 1850(5):872–877CrossRef Bernardi RC, Melo MCR, Schulten K (2015) Enhanced sampling techniques in molecular dynamics simulations of biological systems. Biochimica et Biophysica Acta (BBA) 1850(5):872–877CrossRef
13.
go back to reference Sugita Y, Okamoto Y (1999) Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 314.1:141–151.APA Sugita Y, Okamoto Y (1999) Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 314.1:141–151.APA
14.
go back to reference Swinney DC, Anthony J (2011) How were new medicines discovered? Nat Rev Drug Discov 10(7):507–519PubMedCrossRef Swinney DC, Anthony J (2011) How were new medicines discovered? Nat Rev Drug Discov 10(7):507–519PubMedCrossRef
15.
go back to reference Borhani DW, Shaw DE (2012) The future of molecular dynamics simulations in drug discovery. J Comput Aided Mol Des 26(1):15–26PubMedCrossRef Borhani DW, Shaw DE (2012) The future of molecular dynamics simulations in drug discovery. J Comput Aided Mol Des 26(1):15–26PubMedCrossRef
17.
19.
go back to reference Wishart DS et al (2006) DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 34.suppl_1:D668–D672CrossRef Wishart DS et al (2006) DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 34.suppl_1:D668–D672CrossRef
20.
21.
go back to reference Lengauer T, Rarey M (1996) Computational methods for biomolecular docking. Curr Opin Struct Biol 6(3):402–406PubMedCrossRef Lengauer T, Rarey M (1996) Computational methods for biomolecular docking. Curr Opin Struct Biol 6(3):402–406PubMedCrossRef
22.
go back to reference Sleigh Sara H, Barton Cheryl L (2010) Repurposing strategies for therapeutics. Pharm Med 24(3):151–159CrossRef Sleigh Sara H, Barton Cheryl L (2010) Repurposing strategies for therapeutics. Pharm Med 24(3):151–159CrossRef
24.
go back to reference Sagiroglu, Seref, and Duygu Sinanc (2013) Big data: a review. In: International conference on collaboration technologies and systems (CTS). IEEE Sagiroglu, Seref, and Duygu Sinanc (2013) Big data: a review. In: International conference on collaboration technologies and systems (CTS). IEEE
25.
go back to reference Nayak A, Poriya A, Poojary D (2013) Type of NOSQL databases and its comparison with relational databases. Int J Appl Inf Syst 5(4):16–19 Nayak A, Poriya A, Poojary D (2013) Type of NOSQL databases and its comparison with relational databases. Int J Appl Inf Syst 5(4):16–19
27.
go back to reference Zaharia M et al (2010) Spark: cluster computing with working sets. HotCloud 10(10-10):95 Zaharia M et al (2010) Spark: cluster computing with working sets. HotCloud 10(10-10):95
28.
go back to reference Allen WJ et al (2015) DOCK 6: impact of new features and current docking performance. J Comp Chem 36(15):1132–1156CrossRef Allen WJ et al (2015) DOCK 6: impact of new features and current docking performance. J Comp Chem 36(15):1132–1156CrossRef
29.
go back to reference Jones G et al (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267(3):727–748PubMedCrossRef Jones G et al (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267(3):727–748PubMedCrossRef
30.
go back to reference Trott Oleg, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem 31(2):455–461PubMedPubMedCentral Trott Oleg, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem 31(2):455–461PubMedPubMedCentral
31.
go back to reference Case DA et al (2005) The Amber biomolecular simulation programs. J Comput Chem 26.16:1668–1688CrossRef Case DA et al (2005) The Amber biomolecular simulation programs. J Comput Chem 26.16:1668–1688CrossRef
32.
go back to reference Brooks BR et al (2009) CHARMM: the biomolecular simulation program. J Comput Chem 30.10:1545–1614CrossRef Brooks BR et al (2009) CHARMM: the biomolecular simulation program. J Comput Chem 30.10:1545–1614CrossRef
33.
go back to reference Van Der Spoel D et al (2005) GROMACS: fast, flexible, and free. J Comput Chem 26(16):1701–1718CrossRef Van Der Spoel D et al (2005) GROMACS: fast, flexible, and free. J Comput Chem 26(16):1701–1718CrossRef
35.
go back to reference Rysavy SJ, Bromley D, Daggett V (2014) DIVE: a graph-based visual-analytics framework for big data. IEEE Comput Graphics Appl 34(2):26–37CrossRef Rysavy SJ, Bromley D, Daggett V (2014) DIVE: a graph-based visual-analytics framework for big data. IEEE Comput Graphics Appl 34(2):26–37CrossRef
36.
go back to reference Doerr S et al (2016) HTMD: high-throughput molecular dynamics for molecular discovery. J Chem Theory Comput 12(4):1845–1852PubMedCrossRef Doerr S et al (2016) HTMD: high-throughput molecular dynamics for molecular discovery. J Chem Theory Comput 12(4):1845–1852PubMedCrossRef
37.
go back to reference Tu T et al (2008) A scalable parallel framework for analyzing terascale molecular dynamics simulation trajectories. In: International conference for high performance computing, networking, storage and analysis. SC 2008. IEEE Tu T et al (2008) A scalable parallel framework for analyzing terascale molecular dynamics simulation trajectories. In: International conference for high performance computing, networking, storage and analysis. SC 2008. IEEE
38.
go back to reference Roe DR, Cheatham TE III (2013) PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput 9(7):3084–3095PubMedCrossRef Roe DR, Cheatham TE III (2013) PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput 9(7):3084–3095PubMedCrossRef
39.
go back to reference Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph Model 14(1):33–38CrossRef Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph Model 14(1):33–38CrossRef
40.
go back to reference Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemometr Intell Lab Syst 2(1–3):37–52CrossRef Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemometr Intell Lab Syst 2(1–3):37–52CrossRef
42.
go back to reference Privalov PL, Crane-Robinson C (2017) Role of water in the formation of macromolecular structures. Eur Biophys J 46(3):203–224PubMedCrossRef Privalov PL, Crane-Robinson C (2017) Role of water in the formation of macromolecular structures. Eur Biophys J 46(3):203–224PubMedCrossRef
43.
go back to reference Pace CN, Fu H, Lee Fryar K, Landua J, Trevino SR, Schell D, Thurlkill RL, Imura S, Scholtz JM, Gajiwala K, Sevcik J (2014) Contribution of hydrogen bonds to protein stability. Protein Sci 23(5):652–661PubMedPubMedCentralCrossRef Pace CN, Fu H, Lee Fryar K, Landua J, Trevino SR, Schell D, Thurlkill RL, Imura S, Scholtz JM, Gajiwala K, Sevcik J (2014) Contribution of hydrogen bonds to protein stability. Protein Sci 23(5):652–661PubMedPubMedCentralCrossRef
44.
45.
go back to reference Yuriev E, Chalmers D, Capuano B (2009) Conformational analysis of drug molecules: a practical exercise in the medicinal chemistry course. J Chem Educ 86(4):477CrossRef Yuriev E, Chalmers D, Capuano B (2009) Conformational analysis of drug molecules: a practical exercise in the medicinal chemistry course. J Chem Educ 86(4):477CrossRef
46.
go back to reference Li J, Ehlers T, Sutter J, Varma-O’Brien S, Kirchmair J (2007) CAESAR: a new conformer generation algorithm based on recursive buildup and local rotational symmetry consideration. J Chem Inf Model 47(5):1923–1932PubMedCrossRef Li J, Ehlers T, Sutter J, Varma-O’Brien S, Kirchmair J (2007) CAESAR: a new conformer generation algorithm based on recursive buildup and local rotational symmetry consideration. J Chem Inf Model 47(5):1923–1932PubMedCrossRef
47.
go back to reference Lagorce D, Pencheva T, Villoutreix BO, Miteva MA (2009) DG-AMMOS: a new tool to generate 3D conformation of small molecules using distance geometry and automated molecular mechanics optimization for in silico screening. BMC Chem. Bio 9(1):6CrossRef Lagorce D, Pencheva T, Villoutreix BO, Miteva MA (2009) DG-AMMOS: a new tool to generate 3D conformation of small molecules using distance geometry and automated molecular mechanics optimization for in silico screening. BMC Chem. Bio 9(1):6CrossRef
48.
go back to reference Sefraoui O, Aissaoui M, Eleuldj M (2012) OpenStack: toward an open-source solution for cloud computing. Int J Comput Appl 55(3):38–42 Sefraoui O, Aissaoui M, Eleuldj M (2012) OpenStack: toward an open-source solution for cloud computing. Int J Comput Appl 55(3):38–42
49.
go back to reference Stewart JJP (1990) MOPAC: a semiempirical molecular orbital program. J Comput Aided Mol Des 4(1):1–103PubMedCrossRef Stewart JJP (1990) MOPAC: a semiempirical molecular orbital program. J Comput Aided Mol Des 4(1):1–103PubMedCrossRef
50.
go back to reference Hawkins PC, Skillman AG, Warren GL, Ellingson BA, Stahl MT (2010) Conformer generation with OMEGA: algorithm and validation using high quality structures from the protein databank and cambridge structural database. J Chem Inf Model 50(4):572–584PubMedPubMedCentralCrossRef Hawkins PC, Skillman AG, Warren GL, Ellingson BA, Stahl MT (2010) Conformer generation with OMEGA: algorithm and validation using high quality structures from the protein databank and cambridge structural database. J Chem Inf Model 50(4):572–584PubMedPubMedCentralCrossRef
51.
go back to reference Ware B (2002) Open source development with LAMP: using Linux, Apache, MySQL and PHP. Addison-Wesley Longman Publishing Co., Inc., Reading Ware B (2002) Open source development with LAMP: using Linux, Apache, MySQL and PHP. Addison-Wesley Longman Publishing Co., Inc., Reading
53.
go back to reference Hukushima K, Nemoto K (1996) Exchange Monte Carlo method and application to spin glass simulations. J Phy Soc Jpn 65(6):1604–1608CrossRef Hukushima K, Nemoto K (1996) Exchange Monte Carlo method and application to spin glass simulations. J Phy Soc Jpn 65(6):1604–1608CrossRef
54.
go back to reference Ashburn TT, Thor KB (2004) Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov 3(8):673–683PubMedCrossRef Ashburn TT, Thor KB (2004) Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov 3(8):673–683PubMedCrossRef
55.
go back to reference Novac Natalia (2013) Challenges and opportunities of drug repositioning. Trends Pharmacol Sci 34(5):267–272PubMedCrossRef Novac Natalia (2013) Challenges and opportunities of drug repositioning. Trends Pharmacol Sci 34(5):267–272PubMedCrossRef
56.
go back to reference Smith Kelly M, Romanelli Frank (2005) Recreational use and misuse of phosphodiesterase 5 inhibitors. J Am Pharm Assoc 45(1):63–75CrossRef Smith Kelly M, Romanelli Frank (2005) Recreational use and misuse of phosphodiesterase 5 inhibitors. J Am Pharm Assoc 45(1):63–75CrossRef
57.
go back to reference Pfister DG (2012) Off-label use of oncology drugs: the need for more data and then some. J Clin Oncol, 584–586PubMedCrossRef Pfister DG (2012) Off-label use of oncology drugs: the need for more data and then some. J Clin Oncol, 584–586PubMedCrossRef
58.
go back to reference Jin G, Wong STC (2014) Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines. Drug Discov Today 19(5):637–644PubMedCrossRef Jin G, Wong STC (2014) Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines. Drug Discov Today 19(5):637–644PubMedCrossRef
60.
go back to reference Khrenova MG et al (2014) Modeling the role of G12 V and G13 V Ras mutations in the Ras-GAP-catalyzed hydrolysis reaction of guanosine triphosphate. Biochemistry 53(45):7093–7099PubMedCrossRef Khrenova MG et al (2014) Modeling the role of G12 V and G13 V Ras mutations in the Ras-GAP-catalyzed hydrolysis reaction of guanosine triphosphate. Biochemistry 53(45):7093–7099PubMedCrossRef
61.
go back to reference Spoerner M et al (2010) Conformational states of human rat sarcoma (Ras) protein complexed with its natural ligand GTP and their role for effector interaction and GTP hydrolysis. J Biol Chem 285(51):39768–39778PubMedPubMedCentralCrossRef Spoerner M et al (2010) Conformational states of human rat sarcoma (Ras) protein complexed with its natural ligand GTP and their role for effector interaction and GTP hydrolysis. J Biol Chem 285(51):39768–39778PubMedPubMedCentralCrossRef
62.
go back to reference Ma J, Karplus M (1997) Molecular switch in signal transduction: reaction paths of the conformational changes in ras p21. Proc Natl Acad Sci USA 94(22):11905–11910PubMedCrossRefPubMedCentral Ma J, Karplus M (1997) Molecular switch in signal transduction: reaction paths of the conformational changes in ras p21. Proc Natl Acad Sci USA 94(22):11905–11910PubMedCrossRefPubMedCentral
63.
go back to reference White MA et al (1995) Multiple Ras functions can contribute to mammalian cell transformation. Cell 80(4):533–541PubMedCrossRef White MA et al (1995) Multiple Ras functions can contribute to mammalian cell transformation. Cell 80(4):533–541PubMedCrossRef
64.
go back to reference Schubbert S, Shannon K, Bollag G (2007) Hyperactive Ras in developmental disorders and cancer. Nat Rev Cancer 7(4):295PubMedCrossRef Schubbert S, Shannon K, Bollag G (2007) Hyperactive Ras in developmental disorders and cancer. Nat Rev Cancer 7(4):295PubMedCrossRef
65.
go back to reference Gao C, Eriksson LA (2013) Impact of mutations on K-Ras-p 120GAP interaction. Comput Mol BioSci 3(02):9CrossRef Gao C, Eriksson LA (2013) Impact of mutations on K-Ras-p 120GAP interaction. Comput Mol BioSci 3(02):9CrossRef
66.
go back to reference Shurki A, Warshel A (2004) Why does the Ras switch “break” by oncogenic mutations? Proteins: Struct Funct Bioinf 55(1):1–10CrossRef Shurki A, Warshel A (2004) Why does the Ras switch “break” by oncogenic mutations? Proteins: Struct Funct Bioinf 55(1):1–10CrossRef
67.
go back to reference Lu S et al (2016) Ras conformational ensembles, allostery, and signaling. Chem Rev 116(11):6607–6665PubMedCrossRef Lu S et al (2016) Ras conformational ensembles, allostery, and signaling. Chem Rev 116(11):6607–6665PubMedCrossRef
68.
go back to reference Sharma N, Sonavane U, Joshi R (2017) Differentiating the pre-hydrolysis states of wild-type and A59G mutant HRas: an insight through MD simulations. Comput Biol Chem 69:96–109PubMedCrossRef Sharma N, Sonavane U, Joshi R (2017) Differentiating the pre-hydrolysis states of wild-type and A59G mutant HRas: an insight through MD simulations. Comput Biol Chem 69:96–109PubMedCrossRef
69.
go back to reference Sharma N, Sonavane U, Joshi R (2014) Probing the wild-type HRas activation mechanism using steered molecular dynamics, understanding the energy barrier and role of water in the activation. Eur Biophys J 43(2-3):81–95PubMedCrossRef Sharma N, Sonavane U, Joshi R (2014) Probing the wild-type HRas activation mechanism using steered molecular dynamics, understanding the energy barrier and role of water in the activation. Eur Biophys J 43(2-3):81–95PubMedCrossRef
70.
go back to reference Wang W, Fang G, Rudolph J (2012) Ras inhibition via direct Ras binding—is there a path forward? Bioorg Med Chem Lett 22(18):5766–5776PubMedCrossRef Wang W, Fang G, Rudolph J (2012) Ras inhibition via direct Ras binding—is there a path forward? Bioorg Med Chem Lett 22(18):5766–5776PubMedCrossRef
71.
go back to reference Branton D, Deamer DW, Marziali A, Bayley H, Benner SA, Butler T, Di Ventra M, Garaj S, Hibbs A, Huang X, Jovanovich, SB (2010) The potential and challenges of nanopore sequencing. In: Nanoscience and technology: A collection of reviews from Nature Journals, pp 261–268CrossRef Branton D, Deamer DW, Marziali A, Bayley H, Benner SA, Butler T, Di Ventra M, Garaj S, Hibbs A, Huang X, Jovanovich, SB (2010) The potential and challenges of nanopore sequencing. In: Nanoscience and technology: A collection of reviews from Nature Journals, pp 261–268CrossRef
Metadata
Title
Turbo Analytics: Applications of Big Data and HPC in Drug Discovery
Authors
Rajendra R. Joshi
Uddhavesh Sonavane
Vinod Jani
Amit Saxena
Shruti Koulgi
Mallikarjunachari Uppuladinne
Neeru Sharma
Sandeep Malviya
E. P. Ramakrishnan
Vivek Gavane
Avinash Bayaskar
Rashmi Mahajan
Sudhir Pandey
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-05282-9_11

Premium Partner