Computation in BioInformatics Multidisciplinary Applications

Bioinformatics is a platform between the biology and information technology and this book provides readers with an understanding of the use of bioinformatics tools in new drug design.

The discovery of new solutions to pandemics is facilitated through the use of promising bioinformatics techniques and integrated approaches. This book covers a broad spectrum of the bioinformatics field, starting with the basic principles, concepts, and application areas. Also covered is the role of bioinformatics in drug design and discovery, including aspects of molecular modeling. Some of the chapters provide detailed information on bioinformatics related topics, such as silicon design, protein modeling, DNA microarray analysis, DNA-RNA barcoding, and gene sequencing, all of which are currently needed in the industry. Also included are specialized topics, such as bioinformatics in cancer detection, genomics, and proteomics. Moreover, a few chapters explain highly advanced topics, like machine learning and covalent approaches to drug design and discovery, all of which are significant in pharma and biotech research and development.

Audience

Researchers and engineers in computation biology, information technology, bioinformatics, drug design, biotechnology, pharmaceutical sciences.

S. Balamurugan, PhD is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. His PhD is in Information Technology, and he has published 45 books, 200+ international journals/conferences, and 35 patents.

Anand Krishnan, PhD is the NRF-DSI Innovation Fellow, Department of Chemical Pathology, University of the Free State (Bloemfontein Campus), Bloemfontein, South Africa. His expertise is in organic chemistry/medical biochemistry/integrative medicine/nano(bio)technology/drug discovery.

Dinesh Goyal, PhD is the Director at the Poornima Institute of Engineering and Technology, Jaipur, India. His research interests are related to information & network security, image processing, data analytics, and cloud computing.

Balakumar Chandrasekaran, PhD is an assistant professor at the Faculty of Pharmacy, Philadelphia University, Jordan. He has published many research articles and book chapters as well as two patents.

Boomi Pandi, PhD is an assistant professor in the Department of Bioinformatics, Alagappa University, Karaikudi, India. He has a number of international articles to his credit. Among his research interest are nanomaterials and polymer synthesis, bio-inorganic chemistry, and nano-drug delivery.

Preface xiii

1 Bioinfomatics as a Tool in Drug Designing 1
Rene Barbie Browne, Shiny C. Thomas and Jayanti Datta Roy

1.1 Introduction 1

1.2 Steps Involved in Drug Designing 3

1.2.1 Identification of the Target Protein/Enzyme 5

1.2.2 Detection of Molecular Site (Active Site) in the Target Protein 6

1.2.3 Molecular Modeling 6

1.2.4 Virtual Screening 9

1.2.5 Molecular Docking 10

1.2.6 QSAR (Quantitative Structure-Activity Relationship) 12

1.2.7 Pharmacophore Modeling 14

1.2.8 Solubility of Molecule 14

1.2.9 Molecular Dynamic Simulation 14

1.2.10 ADME Prediction 15

1.3 Various Softwares Used in the Steps of Drug Designing 16

1.4 Applications 18

1.5 Conclusion 20

References 20

2 New Strategies in Drug Discovery 25
Vivek Chavda, Yogita Thalkari and Swati Marwadi

2.1 Introduction 26

2.2 Road Toward Advancement 27

2.3 Methodology 30

2.3.1 Target Identification 30

2.3.2 Docking-Based Virtual Screening 32

2.3.3 Conformation Sampling 33

2.3.4 Scoring Function 34

2.3.5 Molecular Similarity Methods 35

2.3.6 Virtual Library Construction 37

2.3.7 Sequence-Based Drug Design 37

2.4 Role of OMICS Technology 38

2.5 High-Throughput Screening and Its Tools 40

2.6 Chemoinformatic 44

2.6.1 Exploratory Data Analysis 45

2.6.2 Example Discovery 46

2.6.3 Pattern Explanation 46

2.6.4 New Technologies 46

2.7 Concluding Remarks and Future Prospects 46

References 48

3 Role of Bioinformatics in Early Drug Discovery: An Overview and Perspective 49
Shasank S. Swain and Tahziba Hussain

3.1 Introduction 50

3.2 Bioinformatics and Drug Discovery 51

3.2.1 Structure-Based Drug Design (SBDD) 52

3.2.2 Ligand-Based Drug Design (LBDD) 53

3.3 Bioinformatics Tools in Early Drug Discovery 54

3.3.1 Possible Biological Activity Prediction Tools 55

3.3.2 Possible Physicochemical and Drug-Likeness Properties Verification Tools 58

3.3.3 Possible Toxicity and ADME/T Profile Prediction Tools 60

3.4 Future Directions With Bioinformatics Tool 61

3.5 Conclusion 63

Acknowledgements 64

References 64

4 Role of Data Mining in Bioinformatics 69
Vivek P. Chavda, Amit Sorathiya, Disha Valu and Swati Marwadi

4.1 Introduction 70

4.2 Data Mining Methods/Techniques 71

4.2.1 Classification 71

4.2.1.1 Statistical Techniques 71

4.2.1.2 Clustering Technique 73

4.2.1.3 Visualization 74

4.2.1.4 Induction Decision Tree Technique 74

4.2.1.5 Neural Network 75

4.2.1.6 Association Rule Technique 75

4.2.1.7 Classification 75

4.3 DNA Data Analysis 77

4.4 RNA Data Analysis 79

4.5 Protein Data Analysis 79

4.6 Biomedical Data Analysis 80

4.7 Conclusion and Future Prospects 81

References 81

5 In Silico Protein Design and Virtual Screening 85
Vivek P. Chavda, Zeel Patel, Yashti Parmar and Disha Chavda

5.1 Introduction 86

5.2 Virtual Screening Process 88

5.2.1 Before Virtual Screening 90

5.2.2 General Process of Virtual Screening 90

5.2.2.1 Step 1 (The Establishment of the Receptor Model) 91

5.2.2.2 Step 2 (The Generation of Small-Molecule Libraries) 92

5.2.2.3 Step 3 (Molecular Docking) 92

5.2.2.4 Step 4 (Selection of Lead Protein Compounds) 94

5.3 Machine Learning and Scoring Functions 94

5.4 Conclusion and Future Prospects 95

References 96

6 New Bioinformatics Platform-Based Approach for Drug Design 101
Vivek Chavda, Soham Sheta, Divyesh Changani and Disha Chavda

6.1 Introduction 102

6.2 Platform-Based Approach and Regulatory Perspective 104

6.3 Bioinformatics Tools and Computer-Aided Drug Design 107

6.4 Target Identification 109

6.5 Target Validation 110

6.6 Lead Identification and Optimization 111

6.7 High-Throughput Methods (HTM) 112

6.8 Conclusion and Future Prospects 114

References 115

7 Bioinformatics and Its Application Areas 121
Ragini Bhardwaj, Mohit Sharma and Nikhil Agrawal

7.1 Introduction 121

7.2 Review of Bioinformatics 124

7.3 Bioinformatics Applications in Different Areas 126

7.3.1 Microbial Genome Application 126

7.3.2 Molecular Medicine 129

7.3.3 Agriculture 130

7.4 Conclusion 131

References 131

8 DNA Microarray Analysis: From Affymetrix CEL Files to Comparative Gene Expression 139
Sandeep Kumar, Shruti Shandilya, Suman Kapila, Mohit Sharma and Nikhil Agrawal

8.1 Introduction 140

8.2 Data Processing 140

8.2.1 Installation of Workflow 140

8.2.2 Importing the Raw Data for Processing 141

8.2.3 Retrieving Sample Annotation of the Data 142

8.2.4 Quality Control 143

8.2.4.1 Boxplot 144

8.2.4.2 Density Histogram 145

8.2.4.3 MA Plot 145

8.2.4.4 NUSE Plot 145

8.2.4.5 RLE Plot 145

8.2.4.6 RNA Degradation Plot 145

8.2.4.7 QCstat 148

8.3 Normalization of Microarray Data Using the RMA Method 148

8.3.1 Background Correction 148

8.3.2 Normalization 149

8.3.3 Summarization 149

8.4 Statistical Analysis for Differential Gene Expression 151

8.5 Conclusion 153

References 153

9 Machine Learning in Bioinformatics 155
Rahul Yadav, Mohit Sharma and Nikhil Agrawal

9.1 Introduction and Background 156

9.1.1 Bioinformatics 158

9.1.2 Text Mining 159

9.1.3 IoT Devices 159

9.2 Machine Learning Applications in Bioinformatics 159

9.3 Machine Learning Approaches 161

9.4 Conclusion and Closing Remarks 162

References 162

10 DNA-RNA Barcoding and Gene Sequencing 165
Gifty Sawhney, Mohit Sharma and Nikhil Agrawal

10.1 Introduction 166

10.2 RNA 169

10.3 DNA Barcoding 172

10.3.1 Introduction 172

10.3.2 DNA Barcoding and Molecular Phylogeny 177

10.3.3 Ribosomal DNA (rDNA) of the Nuclear Genome (nuDNA)—ITS 178

10.3.4 Chloroplast DNA 180

10.3.5 Mitochondrial DNA 181

10.3.6 Molecular Phylogenetic Analysis 181

10.3.7 Metabarcoding 189

10.3.8 Materials for DNA Barcoding 190

10.4 Main Reasons of DNA Barcoding 191

10.5 Limitations/Restrictions of DNA Barcoding 192

10.6 RNA Barcoding 192

10.6.1 Overview of the Method 193

10.7 Methodology 194

10.7.1 Materials Required 195

10.7.2 Barcoded RNA Sequencing High-Level Mapping of Single-Neuron Projections 196

10.7.3 Using RNA to Trace Neurons 196

10.7.4 A Life Conservation Barcoder 198

10.7.5 Gene Sequencing 199

10.7.5.1 DNA Sequencing Methods 200

10.7.5.2 First-Generation Sequencing Techniques 204

10.7.5.3 Maxam’s and Gilbert’s Chemical Method 204

10.7.5.4 Sanger Sequencing 205

10.7.5.5 Automation in DNA Sequencing 206

10.7.5.6 Use of Fluorescent-Marked Primers and ddNTPs 206

10.7.5.7 Dye Terminator Sequencing 207

10.7.5.8 Using Capillary Electrophoresis 207

10.7.6 Developments and High-Throughput Methods in DNA Sequencing 208

10.7.7 Pyrosequencing Method 209

10.7.8 The Genome Sequencer 454 FLX System 210

10.7.9 Illumina/Solexa Genome Analyzer 210

10.7.10 Transition Sequencing Techniques 211

10.7.11 Ion-Torrent’s Semiconductor Sequencing 211

10.7.12 Helico’s Genetic Analysis Platform 211

10.7.13 Third-Generation Sequencing Techniques 212

10.8 Conclusion 212

Abbreviations 213

Acknowledgement 214

References 214

11 Bioinformatics in Cancer Detection 229
Mohit Sharma, Umme Abiha, Parul Chugh, Balakumar Chandrasekaran and Nikhil Agrawal

11.1 Introduction 230

11.2 The Era of Bioinformatics in Cancer 230

11.3 Aid in Cancer Research via NCI 232

11.4 Application of Big Data in Developing Precision Medicine 233

11.5 Historical Perspective and Development 235

11.6 Bioinformatics-Based Approaches in the Study of Cancer 237

11.6.1 SLAMS 237

11.6.2 Module Maps 238

11.6.3 COPA 239

11.7 Conclusion and Future Challenges 240

References 240

12 Genomic Association of Polycystic Ovarian Syndrome: Single-Nucleotide Polymorphisms and Their Role in Disease Progression 245
Gowtham Kumar Subbaraj and Sindhu Varghese

12.1 Introduction 246

12.2 FSHR Gene 252

12.3 IL-10 Gene 252

12.4 IRS-1 Gene 253

12.5 PCR Primers Used 254

12.6 Statistical Analysis 255

12.7 Conclusion 258

References 259

13 An Insight of Protein Structure Predictions Using Homology Modeling 265
S. Muthumanickam, P. Boomi, R. Subashkumar, S. Palanisamy, A. Sudha, K. Anand, C. Balakumar, M. Saravanan, G. Poorani, Yao Wang, K. Vijayakumar and M. Syed Ali

13.1 Introduction 266

13.2 Homology Modeling Approach 268

13.2.1 Strategies for Homology Modeling 269

13.2.2 Procedure 269

13.3 Steps Involved in Homology Modeling 270

13.3.1 Template Identification 270

13.3.2 Sequence Alignment 271

13.3.3 Backbone Generation 271

13.3.4 Loop Modeling 271

13.3.5 Side Chain Modeling 272

13.3.6 Model Optimization 272

13.3.6.1 Model Validation 272

13.4 Tools Used for Homology Modeling 273

13.4.1 Robetta 273

13.4.2 M4T (Multiple Templates) 273

13.4.3 I-Tasser (Iterative Implementation of the Threading Assembly Refinement) 273

13.4.4 ModBase 274

13.4.5 Swiss Model 274

13.4.6 PHYRE2 (Protein Homology/Analogy Recognition Engine 2) 274

13.4.7 Modeller 274

13.4.8 Conclusion 275

Acknowledgement 275

References 275

14 Basic Concepts in Proteomics and Applications 279
Jesudass Joseph Sahayarayan, A.S. Enogochitra and Murugesan Chandrasekaran

14.1 Introduction 280

14.2 Challenges on Proteomics 281

14.3 Proteomics Based on Gel 283

14.4 Non-Gel–Based Electrophoresis Method 284

14.5 Chromatography 284

14.6 Proteomics Based on Peptides 285

14.7 Stable Isotopic Labeling 286

14.8 Data Mining and Informatics 287

14.9 Applications of Proteomics 289

14.10 Future Scope 290

14.11 Conclusion 291

References 292

15 Prospects of Covalent Approaches in Drug Discovery: An Overview 295
Balajee Ramachandran, Saravanan Muthupandian and Jeyakanthan Jeyaraman

15.1 Introduction 296

15.2 Covalent Inhibitors Against the Biological Target 297

15.3 Application of Physical Chemistry Concepts in Drug Designing 299

15.4 Docking Methodologies—An Overview 301

15.5 Importance of Covalent Targets 302

15.6 Recent Framework on the Existing Docking Protocols 303

15.7 S_N2 Reactions in the Computational Approaches 304

15.8 Other Crucial Factors to Consider in the Covalent Docking 305

15.8.1 Role of Ionizable Residues 305

15.8.2 Charge Regulation 306

15.8.3 Charge-Charge Interactions 306

15.9 QM/MM Approaches 309

15.10 Conclusion and Remarks 310

Acknowledgements 311

References 311

Index 321

The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.