Book Club System: How It Works & How You Win – Bio Informatics Gate

Course Content

Before You Start: Book Club Orientation

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Book Club System: How It Works & How You Win

Computer-Aided Drug Design | Book Club

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Computer-Aided Drug Design | Book Club

chapter 1

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1 .2 Structure Based Drug Designing

1.2.1 Target Identification .mp4

1 .2. 2 Modeling and Visualization of Macromolecule Structure

1 .2. 3 Binding Site Prediction and Analysis

1. 2. 4 Molecular Docking

1. 2. 4. 1 Flexible Docking

1. 2 .4. 2 Rigid Docking

1. 2. 5 Structure Based Virtual Screening

1. 2. 6 Validation of Molecular Docking

1. 3 Ligand Based Designing

1.3.2 Quantitative Structure–Activity Relationship (QSAR)

1 .4 Computation of HOMO and LUMO Energy

1. 5 ADMET Prediction and Analysis

1. 6 Molecular Dynamics Simulation

1. 7 Identification of New Drug Like Molecules

1. 8 Discovery and Designing of Natural Lead Compounds for Liver Cancer A Case Study

1. 9 Examples of Drugs Synthesized Using CADD

1. 10 SuccessandLimitations

1 . 11 Conclusion

Chapter 2

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Molecular Modeling of Proteins Methods

1 .1 Amino Acids

2. 1. 2 Basic Principles of Protein Structure

2. 2 Explosion of Protein Related Data

2. 3 Protein Structure Determination

2. 3 .1 X Ray Crystallography

2. 3. 2 NMR Spectroscopy

2. 3 .3 3D Electron Microscopy

2. 4 Protein Structure Prediction

2. 4. 1 Homology or Comparative Modeling

2 .4. 1. 1 Template Recognition and Initial Alignment

2. 4. 1. 2 Alignment Correction

2. 4. 1 .3 Modeling Structurally Conserved Region SCR and Backbone Generation

2. 4. 1. 4 Loop Modeling

2. 4. 1 .5 Side Chain Modeling

2 .4. 1 .6 Model Optimization

2. 4. 1. 7 Model Validation

2. 4. 2 Fold Recognition or Threading Method

2 .4. 3 Ab Initio Methods

2. 5 Evaluation and Validation of Modeled Structure

2 .6 Recent Advances in Prediction Approaches

2. 7 Applications

Chapter 3

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3 .1 Introduction

3 .2 Target Molecule

3. 3 Binding Site and Active Site

3. 4 Ligand Molecule

3. 5 Binding Affinity

3. 6 Chemical Specificity

3. 7 Binding Site and Molecular Interactions

3. 7. 1 Protein–Drug Interactions

3. 7. 2 Drug–Nucleic Acid Interactions

3. 7 .3 Protein–Protein Interactions

3 .7. 4 Interaction of Protein with Nucleic Acid, Lipid, and Carbohydrate

3. 8 Binding Site Prediction

3. 8. 1 Evolutionary AlgorithmsSequence Based Predictions

3 .8. 2 Energy Based Algorithms

3. 8 .3 Geometry Based AlgorithmStructure Based Predictions 2

3 .9 Approaches

3. 10 Prediction Tools and Servers

3. 11 Validation of Binding Site

3. 12 Role of the Binding Site in Drug Designing

3. 13 Recent Advances and Future Perspective

Chapter 4

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4. 1 Introduction

4. 1. 1 ADMET Prediction

4. 1. 2 ADMET Parameters and their Role

4. 2 Importance of ADMET

4. 3 The Evolving Science of ADMET

4. 4 Blood–Brain Barrier Models

4 .5 ADMET Prediction

4. 6 Strategies for the Designing of ADMET Model

4. 6 .3 ADMET Prediction Methods and Tools

4. 7 ADMET Tools

4. 8 Challenges in Present Scenario and Future Prospective

End of ch 4 Role of ADMET Tools in Current Scenario

Chapter 5

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5. 1 Introduction

5 .2 Therapeutic Target Information

5. 2 .1 Universal Protein Resource UniProt

5. 2. 2 uniport

5. 2 .2 Protein Data Bank PDB

5 .2. 3 Molecular Modeling Database

5. 2. 4 Therapeutic Target Database

5. 2. 5 Herbal Ingredients Targets HIT Database

5. 2. 6 SuperTarget

5. 3 Chemical Information

5. 3. 1 PubChem

5. 3. 4 Chemical Entities of Biological Interest ChEBI

5. 3 .5 NCI Database

5. 3. 7 ChemSpider

5 .3 .8 BindingDB

5. 3 .9 PDBbind

5. 3 .10 Toxin and Toxin Target Database T3DB

5 .3. 12 Super Natural II

5. 3. 13 Naturally Occurring Plant Based Anti Cance

5. 3 .14 Dictionary of Natural Products Online

5 .3 .15 Ligand Expo

5 .3 .16 SuperLigands

5 .3 .17 Toxicology Data Network

5 .4 Drug Molecule Information

5. 4. 1 DrugBank

5. 4. 2 SuperDRUG2

5 .4 .3 PharmGKB

5. 4 .4 Search Tool for Interactions of Chemicals STITCH

5. 5 Metabolomic Pathway Information

5. 5. 2 Metabolome Database HMDB

5 .5. 3 Small Molecule Pathway Database SMPDB

5. 5 .5 MetaboLights Database

5. 5 .7 Reactome

5 .5. 8 WikiPathways

5. 6 Disease and Physiology Information

5. 6. 1 Online Mendelian Inheritance in Man OMIM

5 .6 .2 METAGENE

5. 6. 3 RAMEDIS

5. 7 Peptide Information

5. 7 .1 PepBank

5 .7 .2 StraPep

5. 7. 3 Antimicrobial Peptide Database APD

5. 7. 5 CancerPPD

5 .8 Challenges and Future Perspective

END OF CH 5 Database Resources for Drug Discovery

Chapter 6

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Molecular Docking and Structure Based Drug Design

6. 1 Introduction

6 .2 Docking Guidelines

6. 2. 2 Docking Process

6. 2. 3 Ligand and Protein Preparation

6. 2. 4 Ligand Conformations Strategies

6 .2. 5 Scoring Functions

6. 2. 5 .1 Force Field

6. 2. 5. 2 Empirical Scorings

6. 2. 5. 3 Knowledge Based Scoring

6. 2. 6 Ensemble Docking

6. 2. 7 Consensus Docking

6. 3 Different Types of Docking Based on Interactions

6. 3. 1 Protein–Ligand Docking

6. 3. 2 Protein–Peptide like Ligand Docking

6. 3. 3 Protein–Protein Docking

6. 4 Water Solvation and Docking

6. 5 DockingTools

6. 6 Virtual Screening

6. 7 Analysis of Docking Results

6. 8 Limitations of Docking Algorithms and Future Scope

6. 9 Major Developments in Docking

END OF CH 6 Molecular Docking and Structure Based Drug

Chapter 7

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Molecular Dynamics Simulation of Protein

7 .1 History and Background

7. 2 Introduction

7. 3 Principle of MD Simulation

7. 3. 1 Periodic Boundary Conditions

7. 3. 2 Ewald Summation Techniques

7 3 3 Particle Mesh Ewald Method

7. 3. 4 Thermostats in MD

7. 3. 5 Solvent Models

7. 3. 6 Energy Minimization Methods in MD Simulations

7. 4 Current Tools for MD Simulation

7. 4. 2 GROMACS

7. 4 .4 CHARMM GUI

7. 4 .6 Quantum MechanicsMolecular Mechanics QMMM

7. 4 .7 HyperChem

7 .5 Other Advance Methods for MD Simulation

7. 6 Analysis of MD Trajectories Through GUI Based Software

7. 6. 3 Chimera

7. 7 Structural Parameters for Analysis of MD Simulation

7 .7 .3 RadiusofGyration

7. 7. 4 Protein–Ligand Contacts

7. 7. 6 Principal Component Analysis or Essential Dynamics

7. 7. 7 Secondary Structure Analysis

7. 8 Application of MD Simulation

7. 8. 2 Application in the Drug Designing

7. 8. 2. 2 Inhibitor Designing Against Fasciola gigantica Thioredoxin Glutathione Reductase

7 .8 .3 Unfolding Studies

7. 8. 3. 2 GdnHCl Induced Unfolding Analysis

7 .8. 3 .3 pH Induced Effects on the Structure and Stability of the Protein

END OF CH 7 Molecular Dynamics Simulation of Protein

Chapter 8

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ST OF CH 8 Computational Approaches for Drug Target

8. 1 Introduction

8. 2 Drug Targets

8. 3 Drug Target Identification

8 .4 Computational Approaches for Drug Target Identification

8. 5 Homology BasedApproaches

8. 5 .1 Human Homologs

8. 5. 2 Human Microbiome Homologs

8 .5 .3 Essentiality

8 .5 .4 Virulence Factor Homologs

8. 5. 5 Drug Target Homologs

8 .5 .6 Cellular Location

8. 5. 7 Role in the Biological Pathway

8 .5 .8 Case Study Subtractive Approach for Drug Target Identification

8 .6 Network Based Approaches

8. 6. 2 Limitations

8. 7 Properties of an Ideal Drug Target

8. 8 Druggability

8. 9 Computational Methods for Druggability Assessment

8 .9 .2 Structure Based Methods

8. 9 .3 Quantification of Druggability

8. 9. 4 Major Concern

8 .10 Target Based Drug Discovery

Chapter 9

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ST OF CH9 Computational Screening Techniques for Lead

9. 1 Introduction

9 .2 High Throughput Screening

9. 2, 1 Assay Design

9. 2 .2 Biochemical Assays

9. 2. 3 Whole Cell Assays

9 .2 .4 Automatic Methods of Library Generation and Robotics in HTS

9. 2. 5 Profiling

9. 2 .6 Screening Expense and Outsourcing Screen

9. 3 QSAR Theories

9. 4 Molecular Descriptors Used in QSAR

9 .5 Methods of QSAR

9. 5. 1 2D QSAR Methods

9. 5. 2 3D QSAR

9. 5 .3 4D QSAR

9. 5 .4 5D QSAR

9. 5. 5 4D vs 5D QSAR

9. 6 .1 Absorption

9. 6. 2 Distribution

9. 6 .3 Metabolism

9. 6. 4 Excretion

9. 7 Toxicological Screening

9. 7. 1 Acute Systemic Toxicity

9. 7 .3 Structural Alerts and Rule Based Method

9 .7. 5 Quantitative Structure Activity Relationship Model Using a Statistical Method

9 ,8 Limitations and Future Scope

END OF CH9 Computational Screening Techniques for Lead Des

Chapter 10

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ST OF CH 10 Advances in Pharmacophore Modeling

10 .1 Introduction

10 .2 Features in a Pharmacophore

10. 3 Pharmacophore Modeling

10. 3. 2 Building a Pharmacophore

10 .3 .3 Algorithms Used to Build a Pharmacophore

10 .3. 4 Structure Based Pharmacophore

10. 4 Tools for Pharmacophore Building

10. 5 Validation of a Pharmacophore Hypothesis

10 .6 A Case Study of Structure and Ligand Based Pharmacophore

Interaction of Protein with Nucleic Acid, Lipid, and Carbohydrate

10. 7. 2 Pharmacophore Fingerprint

10. 7. 3 De Novo Ligand Design

10. 8 Success Stories in Pharmacophore Based Drug Designing

10. 9 Significance of Pharmacophore

10 .10 Downside of Pharmacophore Modeling

10. 11 Conclusion

Chapter 11

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In Silico Designing of Vaccines Methods, Tools, and Their Limitations ..Abstract

11. 1 Introduction

11 .1 .1 Live Attenuated Vaccine

11. 1 2 Inactivated Vaccine

11. 1. 3 Subunit Vaccine

11. 1. 4 Recombinant Vector and DNA Vaccines

11 .1. 5 Epitope Based Vaccines

11. 2 Band TCell Epitopes

11 .3 Bioinformatics in Vaccine Design

11 .4 Prediction Tools for Class I and II MHC Binding

11. 5 CTL Epitope Prediction

11. 5 .1 NetCTL

11 .7 Methods for In Silico Designing of Epitope Based Vaccines

11. 7. 1 Selection of Proteins

11. 7. 2 Epitope Prediction and Analysis

11. 7. 3 Molecular Docking and Molecular Dynamics Simulation

11 .7. 4 Construction of Vaccine

11. 8 Case Studies of Vaccine Designing

11. 8 .2 Vaccine Designing for Bacteria

11. 8 .3 Vaccine Designing for Other Parasites

11. 9 Limitations and Challenges

11. 10 In Silico Designing of Vaccines Methods, Tools, and Their Limitations

Chapter 12

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Abstract ….Machine Learning Approaches to Rational Drug Design

12. 1 Drug Industry

12. 2 Drug Discovery Pipeline

12. 3 Complexity of the Problem and Role of ML Techniques

12. 4 Genetic Algorithms

12 .4. 2 Genetic Algorithm Operators

12. 5 Artificial Neural Networks

12. 6 Deep LearningDL

12. 7 Support Vector Machines

12. 8 Artificial Intelligence and Drug Discovery

12. 9 ANNs, GAs, and Other ML Algorithms inDrug Discovery

12. 9. 1 Molecular Docking

12. 9. 2 Pharmacophore Modeling

12. 9 .3 Quantitative Structure–Activity Relationship QSAR

Computer-Aided Drug Design | Book Club |

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