Details

Computation in BioInformatics


Computation in BioInformatics

Multidisciplinary Applications
1. Aufl.

von: S. Balamurugan, Anand T. Krishnan, Dinesh Goyal, Balakumar Chandrasekaran, Boomi Pandi

187,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 05.10.2021
ISBN/EAN: 9781119654766
Sprache: englisch
Anzahl Seiten: 352

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Beschreibungen

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

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