Bioinformatics Techniques for Drug Discovery

66 Pages·2018·1.573 MB·English

by  Aman Chandra Kaushik, Ajay Kumar, Shiv Bharadwaj, Ravi Chaudhary, Shakti Sahi

SPRINGER BRIEFS IN COMPUTER SCIENCE
Aman Chandra Kaushik 
Ajay Kumar · Shiv Bharadwaj 
Ravi Chaudhary · Shakti Sahi
Bioinformatics
Techniques for
Drug Discovery
Applications for
Complex Diseases
SpringerBriefs in Computer Science
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Aman Chandra Kaushik Ajay Kumar
(cid:129)
Shiv Bharadwaj Ravi Chaudhary
(cid:129)
Shakti Sahi
Bioinformatics Techniques
for Drug Discovery
Applications for Complex Diseases
123
AmanChandra Kaushik RaviChaudhary
Schoolof life Sciences Schoolof Biotechnology
andBiotechnology Gautam BuddhaUniversity
ShanghaiJiao Tong University Greater Noida, Uttar Pradesh
Shanghai India
China
Shakti Sahi
AjayKumar Schoolof Biotechnology
Schoolof Engineering Gautam BuddhaUniversity
Gautam BuddhaUniversity Greater Noida, Uttar Pradesh
Greater Noida, Uttar Pradesh India
India
ShivBharadwaj
Nanotechnology Research
andApplication Center
Sabanci University
Tuzla, Istanbul
Turkey
ISSN 2191-5768 ISSN 2191-5776 (electronic)
SpringerBriefs inComputer Science
ISBN978-3-319-75731-5 ISBN978-3-319-75732-2 (eBook)
https://doi.org/10.1007/978-3-319-75732-2
LibraryofCongressControlNumber:2018932352
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Preface
This book is an outgrowth or organized compilation of the recent bioinformatics
approaches used for the drug discovery, and is designed primarily for the
researchers and academicians in the respective field. It is not, however, an ele-
mentary book and presupposes knowledge of computational biology for post-
graduate level and research scholars. The authors have long held the view that the
lackofknowledgeonthefundamentalaspectsofthevariouscomputationaltoolsis
a serious shortcoming for the postgraduate education as well as research scholars.
Hence, the inclusions of greater details then are usually found in texts for research
scholarorpostgraduatespresumingtheexperimentincomputationalbiology.With
the currentdemand of new drugs for complex diseases as well as the development
of resistance in the diseases, computational tools have been recommended and
successfully established as solution to the growing demands of drug for the phar-
maceutical industries as well as research institutes. Chapters covering the recent
computational aided drug discovery and drug designing approaches in expanding
matter.Theusuallyrequiredmaterialhasbeenpresentedinaconciseform,andthen
details on special aspects have been described in the form of addenda. It is hoped
that this approach will meet the needs of beginners in the field of drug designing
anddiscovery,andalsoprovideresourcefulinformationtotheresearch scholarsor
researchers for more advanced study.
Bioinformatics approach in drug designing is an interdisciplinary field that
required sophisticated techniques and software tools to elucidate the hidden or
complex biological data. The intellectual challenge involved in the study of drug
discovery has attracted the scientists from fields of Computer Science, Biology,
Mathematics and Engineering science, and the field today constitutes a frontier of
computationalbiology.Allattemptshavebeenmadeinthepresentworktoprovide
an integrated approach covering all the essential aspects on drug discovery using
bioinformatics approach. If one visualizes the drug designing as an organized
collection of different interactions between the drug molecules or inhibitor and
target of interest, most commonly a protein, the emphasis given to the molecular
docking,dynamicssimulationsandmodelstovalidatetheirinhibitoryabilityonthe
target molecule in certain chapters will be understandable. The research scholars
v
vi Preface
willbeimpressedwiththefactthatthefundamentalstrategiesindrugdiscoveryare
the inhibition of target by blocking their active sites present in any complex dis-
eases. This is to be expected since the evolutionary diversification and complexa-
tion taken place in different diseases are much greater than that of agents or
molecules metabolic activities or biochemical pathways.
Chapter2givesinsightintotheligand-basedapproachfordrugdesigningusing
thecomputationaltechniqueofthesubject.Chapter3describesthestructure-based
approachfor drugdesigningusing computational technique and Chap.4integrates
the information on three-dimensional (3D) pharmacophore modelling based drug
designing by computational technique and other properties. Chapter 5 explains the
molecular dynamics simulation approach to investigate dynamic behaviour of
system through the application of Newtonian mechanics. Chapter 6 explains the
receptor thermodynamics of ligand–receptor or ligand–enzyme association and
Chap. 7 speaks about the thermodynamics cycles and their application in protein
targets. Finally, Chap. 8 provides the insights into different computational
approaches to understand the genomics and proteomics that help to predict the
target of interest.
Shanghai, China Aman Chandra Kaushik
Greater Noida, India Ajay Kumar
Istanbul, Turkey Shiv Bharadwaj
Greater Noida, India Ravi Chaudhary
Greater Noida, India Shakti Sahi
Contents
1 Brief Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Brief Evolutionary History of In Silico Approaches . . . . . . . . . . 2
1.2 Computational Drug Discovery and Design . . . . . . . . . . . . . . . . 3
1.3 Epigenetics: Beyond the Sequence. . . . . . . . . . . . . . . . . . . . . . . 6
1.4 Histones Modification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2 Ligand-Based Approach for In-silico Drug Designing. . . . . . . . . . . . 11
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Molecular Descriptors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.1 2D QSAR Descriptors . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.2 3D QSAR Descriptors . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.3 Multidimensional QSAR . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3 Constitutional Descriptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.4 Quantitative Structure–Activity Relationships . . . . . . . . . . . . . . . 14
2.5 Molecular Fingerprint and Similarity Searches . . . . . . . . . . . . . . 15
2.6 Similarity Searches in LB-CADD . . . . . . . . . . . . . . . . . . . . . . . 16
2.7 Similarity Networks and off Target Predictions. . . . . . . . . . . . . . 16
2.8 Fingerprint Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.9 Computational Methods for Biomolecular Docking. . . . . . . . . . . 17
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3 Structure-Based Approach for In-silico Drug Designing. . . . . . . . . . 21
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2 Protein Docking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2.1 Protein–Protein Docking. . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2.2 Protein–Ligand Docking. . . . . . . . . . . . . . . . . . . . . . . . . 23
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
vii
viii Contents
4 Three-Dimensional (3D) Pharmacophore Modelling-Based Drug
Designing by Computational Technique . . . . . . . . . . . . . . . . . . . . . . 27
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.1.1 Pharmacophore Model . . . . . . . . . . . . . . . . . . . . . . . . . . 29
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
5 MolecularDynamicsSimulationApproachtoInvestigateDynamic
Behaviour of System Through the Application of Newtonian
Mechanics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5.2 Molecular Dynamics Simulations. . . . . . . . . . . . . . . . . . . . . . . . 34
5.3 Monte Carlo Research with Metropolis Criterion . . . . . . . . . . . . 35
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
6 Receptor Thermodynamics of Ligand–Receptor
or Ligand–Enzyme Association. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
6.2 Database Searching. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
6.2.1 De Novo Drug Design. . . . . . . . . . . . . . . . . . . . . . . . . . 41
6.3 State-of-the-Art Free Energy Calculations. . . . . . . . . . . . . . . . . . 41
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
7 Thermodynamic Cycles and Their Application in Protein
Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
7.2 Protein Targets and Applications . . . . . . . . . . . . . . . . . . . . . . . . 44
7.3 4-Hydroxyphenylpyruvate Dioxygenase (HPPD). . . . . . . . . . . . . 45
7.4 Oligopeptide-Binding Protein a (OppA) . . . . . . . . . . . . . . . . . . . 46
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
8 Genomics and Proteomics Using Computational Biology . . . . . . . . . 47
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
8.2 Peptide Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
8.3 De Novo and Hybrid Algorithms. . . . . . . . . . . . . . . . . . . . . . . . 49
8.4 Sequence Database Search Algorithms. . . . . . . . . . . . . . . . . . . . 49
8.5 Scoring of Peptide Identifications. . . . . . . . . . . . . . . . . . . . . . . . 49
8.6 Peptide-Spectrum Match Scores and Common Thresholds. . . . . . 50
8.7 Fundamentals of Gene Transcription and Translation . . . . . . . . . 51
8.8 Genome Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
8.9 Definition of Genome Annotation . . . . . . . . . . . . . . . . . . . . . . . 53
8.10 Genome Annotation Strategies. . . . . . . . . . . . . . . . . . . . . . . . . . 53
8.11 Proteogenomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
About the Authors
Aman Chandra Kaushik He is a core computational
biologistwithproclivityforbiologicaldatabasesandnature
inspired algorithms. He holds Bachelor in Life Science
(DDUUniversity,India);MasterinBioinformatics(CSJM
University, India); Ph.D. in Bioinformatics (Indo-Israel
collaborative Project) and Post-doctorate in computational
biology from Ben Gurion University, Israel. Currently, he
is Research assistant at Shanghai Jiao Tong University,
China. He was a research fellow in Indian Council of
Medical Research (ICMR) sponsored project. He has
publishedresearcharticlesinvariousinternationaljournals
ofrepute.Healsoattendednationalaswellasinternational
conferences and presented his papers. He has also been
awarded several scholarships and travel grants including
Post-doc scholarship from Kreitman Postdoctoral
Fellowship (PDF); Post-doc scholarship from Shanghai
JiaoTongUniversitysponsoredbyMinistryofScienceand
Technology, China; Travel grant and total expenses
MCCMB 2017 Conference, Moscow, Russia from
Kreitman, Israeli Ministry of Science, ISF; Travel grant
and total expenses for “Worldwide innovative networking
inpersonalizedcancermedicine”,WIN2017Symposium,
Paris, France; Travel grant and total expenses for Joint
ICGEB-ICTP-APCTP Workshop from ICTP which gov-
ernedbyUNESCO,IAEAandItaly;4monthScholarship
from Ministry of Science, Technology Space Israel and
“Young Researcher Scholar Award” from GRDS
International.
ix