Computation in BioInformatics

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
Preface
1 Bioinfomatics as a Tool in Drug Designing
	1.1 Introduction
	1.2 Steps Involved in Drug Designing
		1.2.1 Identification of the Target Protein/Enzyme
		1.2.2 Detection of Molecular Site (Active Site) in the Target Protein
		1.2.3 Molecular Modeling
		1.2.4 Virtual Screening
		1.2.5 Molecular Docking
		1.2.6 QSAR (Quantitative Structure-Activity Relationship)
		1.2.7 Pharmacophore Modeling
		1.2.8 Solubility of Molecule
		1.2.9 Molecular Dynamic Simulation
		1.2.10 ADME Prediction
	1.3 Various Softwares Used in the Steps of Drug Designing
	1.4 Applications
	1.5 Conclusion
	References
2 New Strategies in Drug Discovery
	2.1 Introduction
	2.2 Road Toward Advancement
	2.3 Methodology
		2.3.1 Target Identification
		2.3.2 Docking-Based Virtual Screening
		2.3.3 Conformation Sampling
		2.3.4 Scoring Function
		2.3.5 Molecular Similarity Methods
		2.3.6 Virtual Library Construction
		2.3.7 Sequence-Based Drug Design
	2.4 Role of OMICS Technology
	2.5 High-Throughput Screening and Its Tools
	2.6 Chemoinformatic
		2.6.1 Exploratory Data Analysis
		2.6.2 Example Discovery
		2.6.3 Pattern Explanation
		2.6.4 New Technologies
	2.7 Concluding Remarks and Future Prospects
	References
3 Role of Bioinformatics in Early Drug Discovery: An Overview and Perspective
	3.1 Introduction
	3.2 Bioinformatics and Drug Discovery
		3.2.1 Structure-Based Drug Design (SBDD)
		3.2.2 Ligand-Based Drug Design (LBDD)
	3.3 Bioinformatics Tools in Early Drug Discovery
		3.3.1 Possible Biological Activity Prediction Tools
		3.3.2 Possible Physicochemical and Drug-Likeness Properties Verification Tools
		3.3.3 Possible Toxicity and ADME/T Profile Prediction Tools
	3.4 Future Directions With Bioinformatics Tool
	3.5 Conclusion
	Acknowledgements
	References
4 Role of Data Mining in Bioinformatics
	4.1 Introduction
	4.2 Data Mining Methods/Techniques
		4.2.1 Classification
			4.2.1.1 Statistical Techniques
			4.2.1.2 Clustering Technique
			4.2.1.3 Visualization
			4.2.1.4 Induction Decision Tree Technique
			4.2.1.5 Neural Network
			4.2.1.6 Association Rule Technique
			4.2.1.7 Classification
	4.3 DNA Data Analysis
	4.4 RNA Data Analysis
	4.5 Protein Data Analysis
	4.6 Biomedical Data Analysis
	4.7 Conclusion and Future Prospects
	References
5 In Silico Protein Design and Virtual Screening
	5.1 Introduction
	5.2 Virtual Screening Process
		5.2.1 Before Virtual Screening
		5.2.2 General Process of Virtual Screening
			5.2.2.1 Step 1 (The Establishment of the Receptor Model)
			5.2.2.2 Step 2 (The Generation of Small-Molecule Libraries)
			5.2.2.3 Step 3 (Molecular Docking)
			5.2.2.4 Step 4 (Selection of Lead Protein Compounds)
	5.3 Machine Learning and Scoring Functions
	5.4 Conclusion and Future Prospects
	References
6 New Bioinformatics Platform-Based Approach for Drug Design
	6.1 Introduction
	6.2 Platform-Based Approach and Regulatory Perspective
	6.3 Bioinformatics Tools and Computer-Aided Drug Design
	6.4 Target Identification
	6.5 Target Validation
	6.6 Lead Identification and Optimization
	6.7 High-Throughput Methods (HTM)
	6.8 Conclusion and Future Prospects
	References
7 Bioinformatics and Its Application Areas
	7.1 Introduction
	7.2 Review of Bioinformatics
	7.3 Bioinformatics Applications in Different Areas
		7.3.1 Microbial Genome Application
		7.3.2 Molecular Medicine
		7.3.3 Agriculture
	7.4 Conclusion
	References
8 DNA Microarray Analysis: From Affymetrix CEL Files to Comparative Gene Expression
	8.1 Introduction
	8.2 Data Processing
		8.2.1 Installation of Workflow
		8.2.2 Importing the Raw Data for Processing
		8.2.3 Retrieving Sample Annotation of the Data
		8.2.4 Quality Control
	8.3 Normalization of Microarray Data Using the RMA Method
		8.3.1 Background Correction
		8.3.2 Normalization
		8.3.3 Summarization
	8.4 Statistical Analysis for Differential Gene Expression
	8.5 Conclusion
	References
9 Machine Learning in Bioinformatics
	9.1 Introduction and Background
		9.1.1 Bioinformatics
		9.1.2 Text Mining
		9.1.3 IoT Devices
	9.2 Machine Learning Applications in Bioinformatics
	9.3 Machine Learning Approaches
	9.4 Conclusion and Closing Remarks
	References
10 DNA-RNA Barcoding and Gene Sequencing
	10.1 Introduction
	10.2 RNA
	10.3 DNA Barcoding
		10.3.1 Introduction
		10.3.2 DNA Barcoding and Molecular Phylogeny
		10.3.3 Ribosomal DNA (rDNA) of the Nuclear Genome (nuDNA)—ITS
		10.3.4 Chloroplast DNA
		10.3.5 Mitochondrial DNA
		10.3.6 Molecular Phylogenetic Analysis
		10.3.7 Metabarcoding
		10.3.8 Materials for DNA Barcoding
	10.4 Main Reasons of DNA Barcoding
	10.5 Limitations/Restrictions of DNA Barcoding
	10.6 RNA Barcoding
		10.6.1 Overview of the Method
	10.7 Methodology
		10.7.1 Materials Required
		10.7.2 Barcoded RNA Sequencing High-Level Mapping of Single-Neuron Projections
		10.7.3 Using RNA to Trace Neurons
		10.7.4 A Life Conservation Barcoder
		10.7.5 Gene Sequencing
			10.7.5.1 DNA Sequencing Methods
			10.7.5.2 First-Generation Sequencing Techniques
			10.7.5.3 Maxam’s and Gilbert’s Chemical Method
			10.7.5.4 Sanger Sequencing
			10.7.5.5 Automation in DNA Sequencing
			10.7.5.6 Use of Fluorescent-Marked Primers and ddNTPs
			10.7.5.7 Dye Terminator Sequencing
			10.7.5.8 Using Capillary Electrophoresis
		10.7.6 Developments and High-Throughput Methods in DNA Sequencing
		10.7.7 Pyrosequencing Method
		10.7.8 The Genome Sequencer 454 FLX System
		10.7.9 Illumina/Solexa Genome Analyzer
		10.7.10 Transition Sequencing Techniques
		10.7.11 Ion-Torrent’s Semiconductor Sequencing
		10.7.12 Helico’s Genetic Analysis Platform
		10.7.13 Third-Generation Sequencing Techniques
	10.8 Conclusion
	Abbreviations
	Acknowledgement
	References
11 Bioinformatics in Cancer Detection
	11.1 Introduction
	11.2 The Era of Bioinformatics in Cancer
	11.3 Aid in Cancer Research via NCI
	11.4 Application of Big Data in Developing Precision Medicine
	11.5 Historical Perspective and Development
	11.6 Bioinformatics-Based Approaches in the Study of Cancer
		11.6.1 SLAMS
		11.6.2 Module Maps
		11.6.3 COPA
	11.7 Conclusion and Future Challenges
	References
12 Genomic Association of Polycystic Ovarian Syndrome: Single-Nucleotide Polymorphisms and Their Role in Disease Progression
	12.1 Introduction
	12.2 FSHR Gene
	12.3 IL-10 Gene
	12.4 IRS-1 Gene
	12.5 PCR Primers Used
	12.6 Statistical Analysis
	12.7 Conclusion
	References
13 An Insight of Protein Structure Predictions Using Homology Modeling
	13.1 Introduction
	13.2 Homology Modeling Approach
		13.2.1 Strategies for Homology Modeling
		13.2.2 Procedure
	13.3 Steps Involved in Homology Modeling
		13.3.1 Template Identification
		13.3.2 Sequence Alignment
		13.3.3 Backbone Generation
		13.3.4 Loop Modeling
		13.3.5 Side Chain Modeling
		13.3.6 Model Optimization
			13.3.6.1 Model Validation
	13.4 Tools Used for Homology Modeling
		13.4.1 Robetta
		13.4.2 M4T (Multiple Templates)
		13.4.3 I-Tasser (Iterative Implementation of the Threading Assembly Refinement)
		13.4.4 ModBase
		13.4.5 Swiss Model
		13.4.6 PHYRE2 (Protein Homology/Analogy Recognition Engine 2)
		13.4.7 Modeller
		13.4.8 Conclusion
	Acknowledgement
	References
14 Basic Concepts in Proteomics and Applications
	14.1 Introduction
	14.2 Challenges on Proteomics
	14.3 Proteomics Based on Gel
	14.4 Non-Gel–Based Electrophoresis Method
	14.5 Chromatography
	14.6 Proteomics Based on Peptides
	14.7 Stable Isotopic Labeling
	14.8 Data Mining and Informatics
	14.9 Applications of Proteomics
	14.10 Future Scope
	14.11 Conclusion
	References
15 Prospects of Covalent Approaches in Drug Discovery: An Overview
	15.1 Introduction
	15.2 Covalent Inhibitors Against the Biological Target
	15.3 Application of Physical Chemistry Concepts in Drug Designing
	15.4 Docking Methodologies—An Overview
	15.5 Importance of Covalent Targets
	15.6 Recent Framework on the Existing Docking Protocols
	15.7 SN2 Reactions in the Computational Approaches
	15.8 Other Crucial Factors to Consider in the Covalent Docking
		15.8.1 Role of Ionizable Residues
		15.8.2 Charge Regulation
		15.8.3 Charge-Charge Interactions
	15.9 QM/MM Approaches
	15.10 Conclusion and Remarks
	Acknowledgements
	References
Index
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