This document discusses identification and validation of drug targets. It covers the need for new drugs to combat multidrug resistance, stages of drug discovery including determination of crystal structures through X-ray crystallography and protein modeling. Protein-protein interactions, kinases, and characteristics of ideal drug targets are examined. Methods for target prediction include network-based, gene-based, and physiological approaches. The document outlines the scope and methodology, including a computational approach and web application incorporating BLAST and a SQL database to facilitate target identification and validation for pathogenic microbes like tuberculosis.

1. _________________________________________________________________________
Identification and Validation of Drug Targets
iv
CONTENTS
Ch. 1 INTRODUCTION 1
1.1 New drugs – Why? 1
1.2 Antibacterial drug discovery - a brief history 3
1.3 Multidrug resistance - Driving the need for new drugs 6
1.3.1 Molecular mechanism of drug resistance 8
1.4 Concerns for drug discovery and development 16
1.4.1 Stages of drug discovery 17
1.5 Determination of the crystal structure 18
1.5.1 X-ray crystallography and drug discovery 19
1.5.2 Protein Modeling 21
1.5.2.1 The Four Steps of Protein Modeling 22
1.5.2.2 Comparative or homology protein structure modeling 23
1.6 Protein modeling and drug discovery 24
1.6.1 Multidomain Protein Targets 25
1.7 In silico - its origin and revolution 26
1.7.1 In silico drug discovery 28
1.7.1.1 Chemo genomics approach 28
1.7.2 Virtual Screening and In silico Drug Targets 29
1.7.3 Protein–protein interactions 34
1.7.4 Kinases 35
1.8 Drug Targets 37
1.8.1 Characteristics of an ideal drug target (pathogenic organisms) 38
1.8.2 Identifying Drug Targets 38
1.9 Target prediction methods and strategies - an overview 41
1.9.1 Protein interaction network strategy for drug target identification 41
1.10 Methods for drug target identification 44
1.10.1 Mechanism driven - Determining novel drug targets from
network structures 45
1.10.2 Gene driven - Gene network strategy for drug target identification 47
1.10.3 Physiological approach - Protein interaction network strategy for
drug target identification 48
1.11 Our Approach 57

2. _________________________________________________________________________
Identification and Validation of Drug Targets
v
Ch. 2 SCOPE OF THE PRESENT INVESTIGATION 59
Ch. 3 REVIEW OF LITERATURE 60
3.1 Molecular Drug Targets 60
3.2 Target prediction methods and strategies 61
3.3 Target Selection Methods 62
3.3.1 Essential gene selection 63
3.3.2 Structural genomics based target selection 64
3.3.3 Phylogeny based target selection 65
3.3.4 Structure based target selection 66
3.3.5 Sequence motif based: Target selection 66
3.3.6 Gene expression based target selection 66
3.3.7 Finding antimicrobial drug targets using genetic foot printing 67
3.3.8 Target selection using comparative genomics 69
3.3.9 Genes of unknown function as drug targets 71
3.3.10 Proteins as drug targets 72
3.3.11 Molecular drug targets and structure based drug design 75
3.3.12 Common drug targets 77
3.3.13 Species-specific genes as drug targets 77
3.3.14 Nucleic acid as drug targets 78
3.3.15 RNA as drug target 78
3.3.16 Membranes as drug targets 79
3.3.17 GPCR as drug targets 80
3.3.18 Surrogate markers 81
3.3.19 Surrogate ligands 82
3.4 Drug target prediction – In vitro, in vivo and in silico 85
3.4.1 Genomic Studies and Identification of Diseased Gene 85
3.4.2 Pharmacogenetics for Improving Drug discovery and Development 86
3.4.3 Pharmacokinetics for Improving Drug discovery and Development 87
3.4.4 Virtual screening 88
3.4.5 Present state of the art: Computer-aided drug design 90
3.4.6 Genomics and drug development 92
3.4.7 Genomics and assay development 95
3.4.8 Protein–protein interaction technologies 96
3.5 Novel genomics approach 99

3. _________________________________________________________________________
Identification and Validation of Drug Targets
vi
Ch. 4 METHODOLOGY 101
4.1 Overall Approach 101
4.2 Web-based Application 101
4.2.1 BLAST Parameters 105
4.3 Selected pathogenic microbes 107
4.4 Comprehensive data analysis 108
4.5 Web-based Database 108
4.6 Tools and data resources 110
4.6.1 Servlet 110
4.6.2 JSP 110
4.7 Microsoft SQL Server 119
4.7.1 Relational Database Concepts 119
4.7.2 Features of RDBMS 120
4.7.3 Microsoft SQL Server 122
4.8 JDBC 126
4.8.1 JDBC Drivers 127
Ch. 5 RESULTS AND DISCUSSION 129
5.1 Computational approach for target identification and validation 129
5.2 Application Development 134
5.2.1 Data Analysis for Target prediction and Validation 135
5.2.2 A case scenario - Tuberculosis 140
5.3 Database Development 148
Ch. 6 SUMMARY 151
BIBLIOGRAPHY 153