Computation in BioInformatics

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 xiii1 Bioinfomatics as a Tool in Drug Designing 1Rene Barbie Browne, Shiny C. Thomas and Jayanti Datta Roy1.1 Introduction 11.2 Steps Involved in Drug Designing 31.2.1 Identification of the Target Protein/Enzyme 51.2.2 Detection of Molecular Site (Active Site) in the Target Protein 61.2.3 Molecular Modeling 61.2.4 Virtual Screening 91.2.5 Molecular Docking 101.2.6 QSAR (Quantitative Structure-Activity Relationship) 121.2.7 Pharmacophore Modeling 141.2.8 Solubility of Molecule 141.2.9 Molecular Dynamic Simulation 141.2.10 ADME Prediction 151.3 Various Softwares Used in the Steps of Drug Designing 161.4 Applications 181.5 Conclusion 20References 202 New Strategies in Drug Discovery 25Vivek Chavda, Yogita Thalkari and Swati Marwadi2.1 Introduction 262.2 Road Toward Advancement 272.3 Methodology 302.3.1 Target Identification 302.3.2 Docking-Based Virtual Screening 322.3.3 Conformation Sampling 332.3.4 Scoring Function 342.3.5 Molecular Similarity Methods 352.3.6 Virtual Library Construction 372.3.7 Sequence-Based Drug Design 372.4 Role of OMICS Technology 382.5 High-Throughput Screening and Its Tools 402.6 Chemoinformatic 442.6.1 Exploratory Data Analysis 452.6.2 Example Discovery 462.6.3 Pattern Explanation 462.6.4 New Technologies 462.7 Concluding Remarks and Future Prospects 46References 483 Role of Bioinformatics in Early Drug Discovery: An Overview and Perspective 49Shasank S. Swain and Tahziba Hussain3.1 Introduction 503.2 Bioinformatics and Drug Discovery 513.2.1 Structure-Based Drug Design (SBDD) 523.2.2 Ligand-Based Drug Design (LBDD) 533.3 Bioinformatics Tools in Early Drug Discovery 543.3.1 Possible Biological Activity Prediction Tools 553.3.2 Possible Physicochemical and Drug-Likeness Properties Verification Tools 583.3.3 Possible Toxicity and ADME/T Profile Prediction Tools 603.4 Future Directions With Bioinformatics Tool 613.5 Conclusion 63Acknowledgements 64References 644 Role of Data Mining in Bioinformatics 69Vivek P. Chavda, Amit Sorathiya, Disha Valu and Swati Marwadi4.1 Introduction 704.2 Data Mining Methods/Techniques 714.2.1 Classification 714.2.1.1 Statistical Techniques 714.2.1.2 Clustering Technique 734.2.1.3 Visualization 744.2.1.4 Induction Decision Tree Technique 744.2.1.5 Neural Network 754.2.1.6 Association Rule Technique 754.2.1.7 Classification 754.3 DNA Data Analysis 774.4 RNA Data Analysis 794.5 Protein Data Analysis 794.6 Biomedical Data Analysis 804.7 Conclusion and Future Prospects 81References 815 In Silico Protein Design and Virtual Screening 85Vivek P. Chavda, Zeel Patel, Yashti Parmar and Disha Chavda5.1 Introduction 865.2 Virtual Screening Process 885.2.1 Before Virtual Screening 905.2.2 General Process of Virtual Screening 905.2.2.1 Step 1 (The Establishment of the Receptor Model) 915.2.2.2 Step 2 (The Generation of Small-Molecule Libraries) 925.2.2.3 Step 3 (Molecular Docking) 925.2.2.4 Step 4 (Selection of Lead Protein Compounds) 945.3 Machine Learning and Scoring Functions 945.4 Conclusion and Future Prospects 95References 966 New Bioinformatics Platform-Based Approach for Drug Design 101Vivek Chavda, Soham Sheta, Divyesh Changani and Disha Chavda6.1 Introduction 1026.2 Platform-Based Approach and Regulatory Perspective 1046.3 Bioinformatics Tools and Computer-Aided Drug Design 1076.4 Target Identification 1096.5 Target Validation 1106.6 Lead Identification and Optimization 1116.7 High-Throughput Methods (HTM) 1126.8 Conclusion and Future Prospects 114References 1157 Bioinformatics and Its Application Areas 121Ragini Bhardwaj, Mohit Sharma and Nikhil Agrawal7.1 Introduction 1217.2 Review of Bioinformatics 1247.3 Bioinformatics Applications in Different Areas 1267.3.1 Microbial Genome Application 1267.3.2 Molecular Medicine 1297.3.3 Agriculture 1307.4 Conclusion 131References 1318 DNA Microarray Analysis: From Affymetrix CEL Files to Comparative Gene Expression 139Sandeep Kumar, Shruti Shandilya, Suman Kapila, Mohit Sharma and Nikhil Agrawal8.1 Introduction 1408.2 Data Processing 1408.2.1 Installation of Workflow 1408.2.2 Importing the Raw Data for Processing 1418.2.3 Retrieving Sample Annotation of the Data 1428.2.4 Quality Control 1438.2.4.1 Boxplot 1448.2.4.2 Density Histogram 1458.2.4.3 MA Plot 1458.2.4.4 NUSE Plot 1458.2.4.5 RLE Plot 1458.2.4.6 RNA Degradation Plot 1458.2.4.7 QCstat 1488.3 Normalization of Microarray Data Using the RMA Method 1488.3.1 Background Correction 1488.3.2 Normalization 1498.3.3 Summarization 1498.4 Statistical Analysis for Differential Gene Expression 1518.5 Conclusion 153References 1539 Machine Learning in Bioinformatics 155Rahul Yadav, Mohit Sharma and Nikhil Agrawal9.1 Introduction and Background 1569.1.1 Bioinformatics 1589.1.2 Text Mining 1599.1.3 IoT Devices 1599.2 Machine Learning Applications in Bioinformatics 1599.3 Machine Learning Approaches 1619.4 Conclusion and Closing Remarks 162References 16210 DNA-RNA Barcoding and Gene Sequencing 165Gifty Sawhney, Mohit Sharma and Nikhil Agrawal10.1 Introduction 16610.2 RNA 16910.3 DNA Barcoding 17210.3.1 Introduction 17210.3.2 DNA Barcoding and Molecular Phylogeny 17710.3.3 Ribosomal DNA (rDNA) of the Nuclear Genome (nuDNA)—ITS 17810.3.4 Chloroplast DNA 18010.3.5 Mitochondrial DNA 18110.3.6 Molecular Phylogenetic Analysis 18110.3.7 Metabarcoding 18910.3.8 Materials for DNA Barcoding 19010.4 Main Reasons of DNA Barcoding 19110.5 Limitations/Restrictions of DNA Barcoding 19210.6 RNA Barcoding 19210.6.1 Overview of the Method 19310.7 Methodology 19410.7.1 Materials Required 19510.7.2 Barcoded RNA Sequencing High-Level Mapping of Single-Neuron Projections 19610.7.3 Using RNA to Trace Neurons 19610.7.4 A Life Conservation Barcoder 19810.7.5 Gene Sequencing 19910.7.5.1 DNA Sequencing Methods 20010.7.5.2 First-Generation Sequencing Techniques 20410.7.5.3 Maxam’s and Gilbert’s Chemical Method 20410.7.5.4 Sanger Sequencing 20510.7.5.5 Automation in DNA Sequencing 20610.7.5.6 Use of Fluorescent-Marked Primers and ddNTPs 20610.7.5.7 Dye Terminator Sequencing 20710.7.5.8 Using Capillary Electrophoresis 20710.7.6 Developments and High-Throughput Methodsin DNA Sequencing 20810.7.7 Pyrosequencing Method 20910.7.8 The Genome Sequencer 454 FLX System 21010.7.9 Illumina/Solexa Genome Analyzer 21010.7.10 Transition Sequencing Techniques 21110.7.11 Ion-Torrent’s Semiconductor Sequencing 21110.7.12 Helico’s Genetic Analysis Platform 21110.7.13 Third-Generation Sequencing Techniques 21210.8 Conclusion 212Abbreviations 213Acknowledgement 214References 21411 Bioinformatics in Cancer Detection 229Mohit Sharma, Umme Abiha, Parul Chugh, Balakumar Chandrasekaran and Nikhil Agrawal11.1 Introduction 23011.2 The Era of Bioinformatics in Cancer 23011.3 Aid in Cancer Research via NCI 23211.4 Application of Big Data in Developing Precision Medicine 23311.5 Historical Perspective and Development 23511.6 Bioinformatics-Based Approaches in the Study of Cancer 23711.6.1 SLAMS 23711.6.2 Module Maps 23811.6.3 COPA 23911.7 Conclusion and Future Challenges 240References 24012 Genomic Association of Polycystic Ovarian Syndrome: Single-Nucleotide Polymorphisms and Their Role in Disease Progression 245Gowtham Kumar Subbaraj and Sindhu Varghese12.1 Introduction 24612.2 FSHR Gene 25212.3 IL-10 Gene 25212.4 IRS-1 Gene 25312.5 PCR Primers Used 25412.6 Statistical Analysis 25512.7 Conclusion 258References 25913 An Insight of Protein Structure Predictions Using Homology Modeling 265S. Muthumanickam, P. Boomi, R. Subashkumar, S. Palanisamy, A. Sudha, K. Anand, C. Balakumar, M. Saravanan, G. Poorani, Yao Wang, K. Vijayakumar and M. Syed Ali13.1 Introduction 26613.2 Homology Modeling Approach 26813.2.1 Strategies for Homology Modeling 26913.2.2 Procedure 26913.3 Steps Involved in Homology Modeling 27013.3.1 Template Identification 27013.3.2 Sequence Alignment 27113.3.3 Backbone Generation 27113.3.4 Loop Modeling 27113.3.5 Side Chain Modeling 27213.3.6 Model Optimization 27213.3.6.1 Model Validation 27213.4 Tools Used for Homology Modeling 27313.4.1 Robetta 27313.4.2 M4T (Multiple Templates) 27313.4.3 I-Tasser (Iterative Implementation of the Threading Assembly Refinement) 27313.4.4 ModBase 27413.4.5 Swiss Model 27413.4.6 PHYRE2 (Protein Homology/Analogy Recognition Engine 2) 27413.4.7 Modeller 27413.4.8 Conclusion 275Acknowledgement 275References 27514 Basic Concepts in Proteomics and Applications 279Jesudass Joseph Sahayarayan, A.S. Enogochitra and Murugesan Chandrasekaran14.1 Introduction 28014.2 Challenges on Proteomics 28114.3 Proteomics Based on Gel 28314.4 Non-Gel–Based Electrophoresis Method 28414.5 Chromatography 28414.6 Proteomics Based on Peptides 28514.7 Stable Isotopic Labeling 28614.8 Data Mining and Informatics 28714.9 Applications of Proteomics 28914.10 Future Scope 29014.11 Conclusion 291References 29215 Prospects of Covalent Approaches in Drug Discovery: An Overview 295Balajee Ramachandran, Saravanan Muthupandian and Jeyakanthan Jeyaraman15.1 Introduction 29615.2 Covalent Inhibitors Against the Biological Target 29715.3 Application of Physical Chemistry Concepts in Drug Designing 29915.4 Docking Methodologies—An Overview 30115.5 Importance of Covalent Targets 30215.6 Recent Framework on the Existing Docking Protocols 30315.7 SN2 Reactions in the Computational Approaches 30415.8 Other Crucial Factors to Consider in the Covalent Docking 30515.8.1 Role of Ionizable Residues 30515.8.2 Charge Regulation 30615.8.3 Charge-Charge Interactions 30615.9 QM/MM Approaches 30915.10 Conclusion and Remarks 310Acknowledgements 311References 311Index 321