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E-raamat: Computation in BioInformatics: Multidisciplinary Applications

Edited by (University of the Free State (Bloemfontein Campus), Bloemfontein, South Africa), Edited by , Edited by (Poornima Institute of Engineering and Technology, Jaipur, India), Edited by (Intelligent Research Consultancy Services (iRCS), India), Edited by (Philadelphia University, Jo)
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  • Ilmumisaeg: 05-Oct-2021
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  • ISBN-13: 9781119654766
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Computation in BioInformatics: Multidisciplinary ApplicationsSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 05-Oct-2021
  • Kirjastus: Wiley-Scrivener
  • Keel: eng
  • ISBN-13: 9781119654766
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COMPUTATION IN BIOINFORMATICS 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.
Preface xiii
1 Bioinfomatics as a Tool in Drug Designing 1(24)
Rene Barbie Browne
Shiny C. Thomas
Jayanti Datta Roy
1.1 Introduction
1(2)
1.2 Steps Involved in Drug Designing
3(13)
1.2.1 Identification of the Target Protein/Enzyme
5(1)
1.2.2 Detection of Molecular Site (Active Site) in the Target Protein
6(1)
1.2.3 Molecular Modeling
6(3)
1.2.4 Virtual Screening
9(1)
1.2.5 Molecular Docking
10(2)
1.2.6 QSAR (Quantitative Structure-Activity Relationship)
12(2)
1.2.7 Pharmacophore Modeling
14(1)
1.2.8 Solubility of Molecule
14(1)
1.2.9 Molecular Dynamic Simulation
14(1)
1.2.10 ADME Prediction
15(1)
1.3 Various Softwares Used in the Steps of Drug Designing
16(2)
1.4 Applications
18(2)
1.5 Conclusion
20(1)
References
20(5)
2 New Strategies in Drug Discovery 25(24)
Vivek Chavda
Yogita Thalkari
Swati Marwadi
2.1 Introduction
26(1)
2.2 Road Toward Advancement
27(3)
2.3 Methodology
30(8)
2.3.1 Target Identification
30(2)
2.3.2 Docking-Based Virtual Screening
32(1)
2.3.3 Conformation Sampling
33(1)
2.3.4 Scoring Function
34(1)
2.3.5 Molecular Similarity Methods
35(2)
2.3.6 Virtual Library Construction
37(1)
2.3.7 Sequence-Based Drug Design
37(1)
2.4 Role of OMICS Technology
38(2)
2.5 High-Throughput Screening and Its Tools
40(4)
2.6 Chemoinformatic
44(2)
2.6.1 Exploratory Data Analysis
45(1)
2.6.2 Example Discovery
46(1)
2.6.3 Pattern Explanation
46(1)
2.6.4 New Technologies
46(1)
2.7 Concluding Remarks and Future Prospects
46(2)
References
48(1)
3 Role of Bioinformatics in Early Drug Discovery: An Overview and Perspective 49(20)
Shasank S. Swain
Tahziba Hussain
3.1 Introduction
50(1)
3.2 Bioinformatics and Drug Discovery
51(3)
3.2.1 Structure-Based Drug Design (SBDD)
52(1)
3.2.2 Ligand-Based Drug Design (LBDD)
53(1)
3.3 Bioinformatics Tools in Early Drug Discovery
54(7)
3.3.1 Possible Biological Activity Prediction Tools
55(3)
3.3.2 Possible Physicochemical and Drug-Likeness Properties Verification Tools
58(2)
3.3.3 Possible Toxicity and ADME/T Profile Prediction Tools
60(1)
3.4 Future Directions With Bioinformatics Tool
61(2)
3.5 Conclusion
63(1)
Acknowledgements
64(1)
References
64(5)
4 Role of Data Mining in Bioinformatics 69(16)
Vivek P. Chavda
Amit Sorathiya
Disha Valu
Swati Marwadi
4.1 Introduction
70(1)
4.2 Data Mining Methods/Techniques
71(6)
4.2.1 Classification
71(17)
4.2.1.1 Statistical Techniques
71(2)
4.2.1.2 Clustering Technique
73(1)
4.2.1.3 Visualization
74(1)
4.2.1.4 Induction Decision Tree Technique
74(1)
4.2.1.5 Neural Network
75(1)
4.2.1.6 Association Rule Technique
75(1)
4.2.1.7 Classification
75(2)
4.3 DNA Data Analysis
77(2)
4.4 RNA Data Analysis
79(1)
4.5 Protein Data Analysis
79(1)
4.6 Biomedical Data Analysis
80(1)
4.7 Conclusion and Future Prospects
81(1)
References
81(4)
5 In Silico Protein Design and Virtual Screening 85(16)
Vivek P. Chavda
Zeel Patel
Yashti Parmar
Disha Chavda
5.1 Introduction
86(2)
5.2 Virtual Screening Process
88(6)
5.2.1 Before Virtual Screening
90(1)
5.2.2 General Process of Virtual Screening
90(36)
5.2.2.1 Step 1 (The Establishment of the Receptor Model)
91(1)
5.2.2.2 Step 2 (The Generation of Small-Molecule Libraries)
92(1)
5.2.2.3 Step 3 (Molecular Docking)
92(2)
5.2.2.4 Step 4 (Selection of Lead Protein Compounds)
94(1)
5.3 Machine Learning and Scoring Functions
94(1)
5.4 Conclusion and Future Prospects
95(1)
References
96(5)
6 New Bioinformatics Platform-Based Approach for Drug Design 101(20)
Vivek Chavda
Soham Sheta
Divyesh Changani
Disha Chavda
6.1 Introduction
102(2)
6.2 Platform-Based Approach and Regulatory Perspective
104(3)
6.3 Bioinformatics Tools and Computer-Aided Drug Design
107(2)
6.4 Target Identification
109(1)
6.5 Target Validation
110(1)
6.6 Lead Identification and Optimization
111(1)
6.7 High-Throughput Methods (HTM)
112(2)
6.8 Conclusion and Future Prospects
114(1)
References
115(6)
7 Bioinformatics and Its Application Areas 121(18)
Ragini Bhardwaj
Mohit Sharma
Nikhil Agrawal
7.1 Introduction
121(3)
7.2 Review of Bioinformatics
124(2)
7.3 Bioinformatics Applications in Different Areas
126(5)
7.3.1 Microbial Genome Application
126(3)
7.3.2 Molecular Medicine
129(1)
7.3.3 Agriculture
130(1)
7.4 Conclusion
131(1)
References
131(8)
8 DNA Microarray Analysis: From Affymetrix CEL Files to Comparative Gene Expression 139(16)
Sandeep Kumar
Shruti Shandilya
Suman Kapila
Mohit Sharma
Nikhil Agrawal
8.1 Introduction
140(1)
8.2 Data Processing
140(8)
8.2.1 Installation of Workflow
140(1)
8.2.2 Importing the Raw Data for Processing
141(1)
8.2.3 Retrieving Sample Annotation of the Data
142(1)
8.2.4 Quality Control
143(5)
8.2.4.1 Boxplot
144(1)
8.2.4.2 Density Histogram
145(1)
8.2.4.3 MA Plot
145(1)
8.2.4.4 NUSE Plot
145(1)
8.2.4.5 RLE Plot
145(1)
8.2.4.6 RNA Degradation Plot
145(3)
8.2.4.7 QCstat
148(1)
8.3 Normalization of Microarray Data Using the RMA Method
148(3)
8.3.1 Background Correction
148(1)
8.3.2 Normalization
149(1)
8.3.3 Summarization
149(2)
8.4 Statistical Analysis for Differential Gene Expression
151(2)
8.5 Conclusion
153(1)
References
153(2)
9 Machine Learning in Bioinformatics 155(10)
Rahul Yadav
Mohit Sharma
Nikhil Agrawal
9.1 Introduction and Background
156(3)
9.1.1 Bioinformatics
158(1)
9.1.2 Text Mining
159(1)
9.1.3 IoT Devices
159(1)
9.2 Machine Learning Applications in Bioinformatics
159(2)
9.3 Machine Learning Approaches
161(1)
9.4 Conclusion and Closing Remarks
162(1)
References
162(3)
10 DNA-RNA Barcoding and Gene Sequencing 165(64)
Gifty Sawhney
Mohit Sharma
Nikhil Agrawal
10.1 Introduction
166(3)
10.2 RNA
169(3)
10.3 DNA Barcoding
172(19)
10.3.1 Introduction
172(5)
10.3.2 DNA Barcoding and Molecular Phylogeny
177(1)
10.3.3 Ribosomal DNA (rDNA) of the Nuclear Genome (nuDNA)-ITS
178(2)
10.3.4 Chloroplast DNA
180(1)
10.3.5 Mitochondrial DNA
181(1)
10.3.6 Molecular Phylogenetic Analysis
181(8)
10.3.7 Metabarcoding
189(1)
10.3.8 Materials for DNA Barcoding
190(1)
10.4 Main Reasons of DNA Barcoding
191(1)
10.5 Limitations/Restrictions of DNA Barcoding
192(1)
10.6 RNA Barcoding
192(2)
10.6.1 Overview of the Method
193(1)
10.7 Methodology
194(18)
10.7.1 Materials Required
195(1)
10.7.2 Barcoded RNA Sequencing High-Level Mapping of Single-Neuron Projections
196(1)
10.7.3 Using RNA to Trace Neurons
196(2)
10.7.4 A Life Conservation Barcoder
198(1)
10.7.5 Gene Sequencing
199(9)
10.7.5.1 DNA Sequencing Methods
200(4)
10.7.5.2 First-Generation Sequencing Techniques
204(1)
10.7.5.3 Maxam's and Gilbert's Chemical Method
204(1)
10.7.5.4 Sanger Sequencing
205(1)
10.7.5.5 Automation in DNA Sequencing
206(1)
10.7.5.6 Use of Fluorescent-Marked Primers and ddNTPs
206(1)
10.7.5.7 Dye Terminator Sequencing
207(1)
10.7.5.8 Using Capillary Electrophoresis
207(1)
10.7.6 Developments and High-Throughput Methods in DNA Sequencing
208(1)
10.7.7 Pyrosequencing Method
209(1)
10.7.8 The Genome Sequencer 454 FLX System
210(1)
10.7.9 Illumina/Solexa Genome Analyzer
210(1)
10.7.10 Transition Sequencing Techniques
211(1)
10.7.11 Ion-Torrent's Semiconductor Sequencing
211(1)
10.7.12 Helico's Genetic Analysis Platform
211(1)
10.7.13 Third-Generation Sequencing Techniques
212(1)
10.8 Conclusion
212(1)
Abbreviations
213(1)
Acknowledgement
214(1)
References
214(15)
11 Bioinformatics in Cancer Detection 229(16)
Mohit Sharma
Umme Abiha
Parul Chugh
Balakumar Chandrasekaran
Nikhil Agrawal
11.1 Introduction
230(1)
11.2 The Era of Bioinformatics in Cancer
230(2)
11.3 Aid in Cancer Research via NCI
232(1)
11.4 Application of Big Data in Developing Precision Medicine
233(2)
11.5 Historical Perspective and Development
235(2)
11.6 Bioinformatics-Based Approaches in the Study of Cancer
237(3)
11.6.1 SLAMS
237(1)
11.6.2 Module Maps
238(1)
11.6.3 COPA
239(1)
11.7 Conclusion and Future Challenges
240(1)
References
240(5)
12 Genomic Association of Polycystic Ovarian Syndrome: Single-Nucleotide Polymorphisms and Their Role in Disease Progression 245(20)
Gowtham Kumar Subbaraj
Sindhu Varghese
12.1 Introduction
246(6)
12.2 FSHR Gene
252(1)
12.3 IL-10 Gene
252(1)
12.4 IRS-1 Gene
253(1)
12.5 PCR Primers Used
254(1)
12.6 Statistical Analysis
255(3)
12.7 Conclusion
258(1)
References
259(6)
13 An Insight of Protein Structure Predictions Using Homology Modeling 265(14)
S. Muthumanickam
P. Boomi
R. Subashkumar
S. Palanisamy
A. Sudha
K. Anand
C. Balakumar
M. Saravanan
G. Poorani
Yao Wang
K. Vijayakumar
M. Syed Ali
13.1 Introduction
266(2)
13.2 Homology Modeling Approach
268(2)
13.2.1 Strategies for Homology Modeling
269(1)
13.2.2 Procedure
269(1)
13.3 Steps Involved in Homology Modeling
270(3)
13.3.1 Template Identification
270(1)
13.3.2 Sequence Alignment
271(1)
13.3.3 Backbone Generation
271(1)
13.3.4 Loop Modeling
271(1)
13.3.5 Side Chain Modeling
272(1)
13.3.6 Model Optimization
272(1)
13.3.6.1 Model Validation
272(1)
13.4 Tools Used for Homology Modeling
273(2)
13.4.1 Robetta
273(1)
13.4.2 M4T (Multiple Templates)
273(1)
13.4.3 I-Tasser (Iterative Implementation of the Threading Assembly Refinement)
273(1)
13.4.4 ModBase
274(1)
13.4.5 Swiss Model
274(1)
13.4.6 PHYRE2 (Protein Homology/Analogy Recognition Engine 2)
274(1)
13.4.7 Modeller
274(1)
13.4.8 Conclusion
275(1)
Acknowledgement
275(1)
References
275(4)
14 Basic Concepts in Proteomics and Applications 279(16)
Jesudass Joseph Sahayarayan
A.S. Enogochitra
Murugesan Chandrasekaran
14.1 Introduction
280(1)
14.2 Challenges on Proteomics
281(2)
14.3 Proteomics Based on Gel
283(1)
14.4 Non-Gel-Based Electrophoresis Method
284(1)
14.5 Chromatography
284(1)
14.6 Proteomics Based on Peptides
285(1)
14.7 Stable Isotopic Labeling
286(1)
14.8 Data Mining and Informatics
287(2)
14.9 Applications of Proteomics
289(1)
14.10 Future Scope
290(1)
14.11 Conclusion
291(1)
References
292(3)
15 Prospects of Covalent Approaches in Drug Discovery: An Overview 295(26)
Balajee Ramachandran
Saravanan Muthupandian
Jeyakanthan Jeyaraman
15.1 Introduction
296(1)
15.2 Covalent Inhibitors Against the Biological Target
297(2)
15.3 Application of Physical Chemistry Concepts in Drug Designing
299(2)
15.4 Docking Methodologies-An Overview
301(1)
15.5 Importance of Covalent Targets
302(1)
15.6 Recent Framework on the Existing Docking Protocols
303(1)
15.7 SN2 Reactions in the Computational Approaches
304(1)
15.8 Other Crucial Factors to Consider in the Covalent Docking
305(4)
15.8.1 Role of Ionizable Residues
305(1)
15.8.2 Charge Regulation
306(1)
15.8.3 Charge-Charge Interactions
306(3)
15.9 QM/MM Approaches
309(1)
15.10 Conclusion and Remarks
310(1)
Acknowledgements
311(1)
References
311(10)
Index 321
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.
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