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• Contributors
• Nitin Sharma, Manika Choudhary, Vikas Kumar Chapter 1 Introduction to bioinformatics
• 1.1 Introduction
• 1.2 Biological databases
• 1.3 Bioinformatics tools
• 1.4 Scope of bioinformatics
• 1.5 Applications of bioinformatics
• 1.5.1 Medicine
• 1.5.2 Microbial genome applications
• 1.5.3 Agriculture
• 1.5.4 Bioweapon creation
• 1.5.5 Evolutionary studies
• 1.5.6 Forensic science
• 1.5.7 Antibiotic resistance
• 1.6 Limitations
• 1.7 Conclusions
• Varruchi Sharma, Imran Sheikh, Vikas Kushwaha, Shagun Gupta, Ankur Kaushal, Seema Ramniwas, Poonam Bansal, Anupam Sharma, Vandana Sharma, J. K. Sharma, Anil Panwar, Anil Kumar Sharma Chapter 2 Biological databases and bioinformatics tools
• 2.1 Introduction
• 2.2 Classification of biological databases
• 2.2.1 Primary database
• 2.2.2 Secondary database
• 2.2.3 Composite database
• 2.2.4 Additional databases
• 2.3 Biological database retrieval system
• 2.3.1 Entrez
• 2.3.2 Sequence retrieval system (SRS)
• 2.3.3 DBGET/LinkDB
• 2.4 Bioinformatics tools
• 2.5 Conclusions
• Varruchi Sharma, Poonam Bansal, Imran Sheikh, Anupam Sharma, Damanjeet Kaur, Amit Joshi, Anil K. Sharma Chapter 3 Fundamentals of bioinformatics
• 3.1 Introduction
• 3.2 Bioinformatics association with other domains
• 3.3 Applications of bioinformatics
• 3.4 Database development
• 3.5 Sequence alignment and its types
• 3.6 Scoring a sequence alignment
• 3.7 Dot plot method
• 3.8 Applications of pairwise sequence alignment method
• 3.9 Dynamic programming
• 3.10 Longest common subsequence
• 3.11 Conclusions
• Varruchi Sharma, Imran Sheikh, Vikas Kushwaha, Anil Panwar, Seema Ramniwas, Anupam Sharma, Vandana Sharma, J. K. Sharma, Sonal Datta, Anil K. Sharma Chapter 4 Tools used in sequence alignment
• 4.1 Multiple sequence alignment
• 4.2 BLAST
• 4.2.1 Working of BLAST
• 4.2.2 Programs of BLAST
• 4.2.3 BLAST results
• 4.3 T-Coffee
• 4.4 HMMER
• 4.4.1 Working with HMMER (https://www.ebi.ac.uk/Tools/hmmer/) (Figure 4.7)
• 4.5 DIAMOND
• 4.6 ScalaBLAST
• 4.7 Sequence alignment/map
• 4.8 MALIGN
• 4.9 COMPASS
• 4.10 Conclusion
• Ramesh C. Thakur, Akshay Sharma, Renuka Sharma Chapter 5 Recent advances in the discovery of drug molecules: trends, scope, and relevance
• 5.1 Introduction
• 5.2 Natural drugs
• 5.2.1 Plant, animal, and microbial sources
• 5.2.2 Animal sources
• 5.2.3 Microbial sources
• 5.2.4 Marine sources
• 5.2.5 Mineral (metallic and nonmetallic) sources
• 5.2.6 Geographical or habitat sources
• 5.3 Synthetic drugs or designer drugs
• 5.3.1 Semisynthetic
• 5.4 Drug content
• 5.4.1 Active ingredient
• 5.4.2 Excipient: inactive ingredient
• 5.5 Drug categories
• 5.5.1 Stimulants
• 5.5.2 Inhalants
• 5.5.3 Cannabinoids
• 5.5.4 Depressants
• 5.5.5 Opioids and morphine derivatives
• 5.5.6 Anabolic steroids
• 5.5.7 Hallucinogens
• 5.5.8 Prescription drugs
• 5.6 Drug design
• 5.7 Lipinski’s rule
• 5.8 Hydrophobicity of a drug
• 5.9 Role of partition coefficient in drug design
• 5.10 Biological activity of a drug
• 5.11 Natural products as leads
• 5.12 Natural products-based drug development
• 5.13 Conclusion and future recommendations
• Meenakshi Rajpoot, Varruchi Sharma, Shagun Gupta, Ankur Kaushal, Anupam Sharma, Vandana Sharma, J. K. Sharma, Anil Panwar, Seema Ramniwas, Damanjeet Kaur, Anil K. Sharma Chapter 6 Computer-aided drug design and drug discovery
• 6.1 Introduction
• 6.2 Structure-based drug design
• 6.2.1 Protein structure determination and hot spot prediction
• 6.2.2 Virtual screening
• 6.2.3 Docking and scoring functions
• 6.3 Ligand-based drug designing
• 6.3.1 Similarity searching method
• 6.3.2 Pharmacophore modeling
• 6.3.3 Quantitative structure–activity relationship
• 6.4 Sequence-based drug design
• 6.5 Role of molecular dynamics simulations in drug discovery
• 6.6 Success stories of computational drug discovery approaches
• 6.7 Conclusion and future perspectives
• Vikas Kushwaha, Anu Prabha, Varruchi Sharma, Ashwanti Devi, Seema Ramniwas, Anupam Sharma, Anil K. Sharma, Imran Sheikh, Anil Panwar, Damanjeet Kaur Chapter 7 Immunoinformatics: computational keys to immune system secrets
• 7.1 Introduction
• 7.2 Data sources
• 7.2.1 Laboratory immunological experiments
• 7.2.2 Scientific literature
• 7.2.3 Immunological databases
• 7.2.4 B-cell epitope databases
• 7.2.5 T-cell epitope databases
• 7.3 Immunological tools
• 7.3.1 B-cell epitope prediction
• 7.3.2 T-cell epitope prediction
• 7.3.3 Linkers
• 7.3.4 Allergenicity and antigenicity prediction tool
• 7.3.5 Design optimization
• 7.4 Conclusion
• Vikas Kumar, Nitin Sharma Chapter 8 Phylogenetic analysis
• 8.1 Introduction
• 8.2 Phylogenetic tree and its construction
• 8.3 Various tools in phylogenetic analysis
• Sheetal Dagar, Anil Panwar, Varruchi Sharma, Imran Sheikh, Vikas Kushwaha, Damanjeet Kaur, Anil K. Sharma, Srikant Kaushik Chapter 9 Basic structure of proteins: current paradigms, trends, and perspective
• 9.1 Amino acids
• 9.1.1 Classification
• 9.2 Peptides
• 9.3 Dihedral angles
• 9.4 Proteins
• 9.5 Hierarchy of proteins
• 9.6 Primary structure
• 9.7 Secondary structure
• 9.8 Alpha helix
• 9.9 Beta sheet
• 9.10 Beta turns
• 9.11 Loops or coils
• 9.12 Tertiary structure
• 9.13 Motifs and domains
• 9.14 Domain
• 9.15 Quaternary structure
• 9.16 Methods for determination of proteins’ 3D structure
• 9.17 X-ray crystallography
• 9.18 Protein secondary and tertiary structure prediction (programs)
• Homology modeling
• Threading/fold recognition
• Ab initio structure prediction
• Secondary structure prediction
• 9.19 Conclusion
• Index