Computation in BioInformatics

Name: Computation in BioInformatics | Multidisciplinary Applications
Brand: Wiley
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Multidisciplinary Applications

S. Balamurugan Anand T. Krishnan Dinesh Goyal Balakumar Chandrasekaran Boomi Pandi(Herausgeber*in)

Wiley (Verlag)

1. Auflage

Erschienen am 27. September 2021

352 Seiten

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Inhalt

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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