Innovations and Implementations of Computer Aided Drug Discovery Strategies in Rational Drug Design

This book presents various computer-aided drug discovery methods for the design and development of ligand and structure-based drug molecules. A wide variety of computational approaches are now being used in various stages of drug discovery and development, as well as in clinical studies. Yet, despite the rapid advances in computer software and hardware, combined with the exponential growth in the available biological information, there are many challenges that still need to be addressed, as this book shows. In turn, it shares valuable insights into receptor-ligand interactions in connection with various biological functions and human diseases. The book discusses a wide range of phylogenetic methods and highlights the applications of Molecular Dynamics Simulation in the drug discovery process. It also explores the application of quantum mechanics in order to provide better accuracy when calculating protein-ligand binding interactions and predicting binding affinities. In closing, the book provides illustrative descriptions of major challenges associated with computer-aided drug discovery for the development of therapeutic drugs. Given its scope, it offers a valuable asset for life sciences researchers, medicinal chemists and bioinformaticians looking for the latest information on computer-aided methodologies for drug development, together with their applications in drug discovery. Contents About the Editor Chapter 1: CADD: Some Success Stories from Sanjeevini and the Way Forward 1.1 Introduction to Computer Aided Drug Design (CADD) 1.2 Active Site Prediction 1.2.1 Introduction 1.2.2 Active Site Prediction Servers AADS 1.3 Virtual High Throughput Screening 1.3.1 RASPD 1.3.2 BAITOC 1.4 Docking and Scoring 1.4.1 Protein-Ligand Docking Server ParDOCK BAPPL Bappl+ 1.5 Sanjeevini 1.5.1 Sanjeevini ́s Success Stories 1.6 Dhanvantri 1.7 Sanjeevini Application in Android Mode 1.8 Conclusions References Chapter 2: Virtual Screening: Practical Application of Docking, Consensus Scoring and Rescoring Using Binding Free Energy 2.1 Introduction 2.1.1 Docking Algorithms: Autodock4, Autodock Vina, DOCK6 2.1.2 Consensus Scoring 2.1.3 Rescoring Using Binding Free Energy 2.2 Virtual Screening and Rescoring Workflow 2.2.1 Computational Requirements 2.2.2 Virtual Screening Using Docking Preprocessing of the Receptor and Ligand Library Receptor Ligand Library Transformation of the Receptor and Ligand Library Processing Autodock4 Autodock Vina DOCK6 Consensus Scoring Enrichment Plot 2.2.3 Rescoring Using MMPBSA/MMGBSA Preparation of the System: Receptor-Ligand Library Complex Parameterization of Receptor and Ligand Library Refinement of the Receptor-Ligand Complex Rescoring 2.3 Conclusion References Chapter 3: Aspects of Protein Structure, Function, and Dynamics in Rational Drug Designing 3.1 Introduction 3.1.1 Rational Drug Designing 3.2 Role of Protein Structural Biology in Rational Drug Design 3.3 Role of Protein Dynamics in Drug Design 3.4 Role of Computer-Aided Drug Design (CADD) 3.4.1 Molecular Docking Sampling Algorithms Scoring Functions 3.4.2 Molecular Dynamics Simulations 3.5 Case Study: Computational Screening of Potential Lead Molecules and Experimental Validation of Their Anti-Influenza Effect 3.6 Summary References Chapter 4: Role of Advanced Computing in the Drug Discovery Process 4.1 Introduction 4.1.1 Structure-Based Drug Designing Approach 4.1.2 Ligand-Based Drug Design Approach Pharmacophore Modelling QSAR Multiple Linear Regression Partial Least Square Regression 4.2 Data Mining in Drug Discovery 4.2.1 Artificial Neural Network Initialization of Weights Multilayer Perceptrons Backpropagation of Error 4.2.2 Machine Learning Supervised Machine Learning Regression Linear and Multiple Linear Regression Classification Unsupervised Learning Principal Component Analysis K-Means Cluster DB-Scan Reinforcement Learning 4.3 Deep Learning 4.4 High-Performance Computing in Drug Discovery 4.5 GPU Computing 4.6 Parallel Programming Models 4.6.1 OpenMP 4.6.2 Message Passing Interface (MPI) 4.6.3 Compute Unified Device Architecture (CUDA) 4.6.4 Open Computing Language (OpenCL) 4.6.5 Application of Parallel Programming Model and Tools in Binding Site Prediction and Docking 4.7 Molecular Dynamics Simulation (MD) 4.7.1 Applications of Advanced Computing in Molecular Dynamics Simulation (MD) 4.8 Cloud Computing 4.8.1 Applications of Cloud Computing in Drug Discovery 4.9 Conclusion References Chapter 5: Protein Structure, Dynamics and Assembly: Implications for Drug Discovery 5.1 Introduction 5.2 Protein Structure 5.2.1 Use of Protein Structures in Rational Drug Design and Discovery 5.2.2 Overview of Drug Discovery Program and Few Successful Examples of SBDD 5.3 Applications of Knowledge Derived from Homologous Proteins in Drug Design 5.3.1 Homology Detection in the Identification of Potential Drug Targets in Pathogens 5.3.2 3-D Modeling of Drug Targets Using Structures of Homologous Proteins 5.3.3 Homology and Drug Promiscuity 5.3.4 Drug Specificity and Potency 5.3.5 Homologous Proteins in Polypharmacology 5.4 Protein Dynamics 5.4.1 Role of Dynamics in Enzymatic Function 5.4.2 Role of Dynamics in Membrane Receptor Function 5.4.3 Methods for Studying Protein Dynamics 5.4.4 Evolution of Protein Dynamics in Target Identification 5.4.5 Role of Dynamics in Drug Design and its Therapeutic Response 5.5 Protein-Protein Complexes 5.5.1 Protein-Protein Complexes in Drug Discovery 5.6 Conclusions References Chapter 6: Recent Trends in Computer-Aided Drug Design 6.1 Introduction 6.2 Computational Identification of Unique Therapeutic Drug Targets in Bacterial Pathogens and Designing Lead Molecules: A Cas... 6.2.1 Approaches for Drug Target Identification 6.2.2 Identification of Unique Targets Based on Comparative Genomics 6.2.3 Subtractive Metabolomics Approach for Identification of Unique Enzymes in Unique Pathways (Naik et al. 2010) 6.2.4 Sequence Analysis 6.2.5 Homology Model Construction 6.2.6 Docking Studies 6.3 Modeling and Molecular Dynamics of Plasmodium falciparum SPECT Protein and Screening of Biogenic Compounds 6.3.1 Structure Modelling, Refinement and Validation 6.3.2 Prediction of Binding Site 6.3.3 Ligand Preparation 6.3.4 Molecular Docking 6.3.5 Molecular Dynamics Simulation 6.4 Conclusion References Chapter 7: Predicting Protein Folding and Protein Stability by Molecular Dynamics Simulations for Computational Drug Discovery 7.1 Introduction 7.1.1 Protein Folding and Its Mechanism 7.2 A Brief Outline of Protein Structure Prediction 7.2.1 Secondary Structure Prediction 7.2.2 Tertiary Structure Prediction 7.3 Computational Perspectives of Protein Folding 7.3.1 Molecular Dynamics Simulation and Its Application in Protein Folding 7.3.2 Molecular Dynamics Simulation Complementing the Experiments 7.3.3 Free Energy Landscapes and Protein Folding 7.4 MDS Methods in Protein Folding 7.4.1 Replica Exchange Molecular Dynamics (REMD) 7.4.2 Coarse Grained Models for MDS 7.4.3 Accelerated Molecular Dynamics (aMD) 7.4.4 Umbrella Sampling 7.4.5 Markov State Model (MSM) for Protein Folding 7.4.6 Path Sampling Approaches for Protein Folding 7.5 Assessment of Protein Stability Through MDS 7.6 Enhancing Drug Discovery by MDS 7.7 Conclusions References Chapter 8: Magnitude and Advancements of CADD in Identifying Therapeutic Intervention against Flaviviruses 8.1 Introduction 8.2 Geographical Distribution of Flaviviruses 8.2.1 Dengue 8.2.2 ZIKA 8.2.3 West Nile 8.2.4 Yellow Fever Virus 8.2.5 Role of Structural and Non-Structural Proteins of Flaviviruses Structural Proteins Non Structural Proteins 8.3 Computational Strategies for Drug Discovery 8.3.1 Bioinformatics Approaches Involved Indrug Design 8.4 Drug Discovery on Flaviviruses 8.4.1 Homology Modelling and Sequence Alignment on Flaviviral Proteins 8.4.2 Structure Based Drug Design for the Identification of Potent Small Molecules 8.4.3 Evolutionary Studies Based on the Computational Technique 8.4.4 Ligand Based Studies on the Flaviviral Proteins 8.4.5 Free Energy Calculation 8.4.6 Conformational Analysis on the Flaviviral Proteins 8.5 Conclusion References Chapter 9: Elucidating Protein-Ligand Interactions Using High Throughput Biophysical Techniques 9.1 Introduction 9.2 Nuclear Magnetic Resonance Spectroscopy 9.2.1 Theoretical Aspects of NMR Spectroscopy and Beyond 9.2.2 Chemical-Shift Perturbations (CSPs) 9.2.3 Relaxation Derived Conformational Dynamics 9.2.4 Exchange Spectroscopy (EXSY) 9.2.5 Saturation Transfer Difference NMR (STD NMR) 9.2.6 Water-LOGSY 9.2.7 Translational Diffusion Measurements 9.2.8 Merits and Demerits of NMR Spectroscopy 9.3 Surface Plasmon Resonance 9.3.1 Theoretical Aspects of Surface Plasmon Resonance 9.3.2 Probing Protein-Ligand Interactions Using SPR 9.3.3 Merits and Demerits of SPR 9.4 Isothermal Titration Calorimetry 9.4.1 Theoretical Aspects of Isothermal Titration Calorimetry 9.4.2 Applications of ITC 9.4.3 Merits and Demerits of ITC 9.5 Fluorescence Spectroscopy 9.5.1 Theoretical Aspects of Fluorescence Spectroscopy 9.5.2 Applications of Fluorescence Spectroscopy 9.5.3 Merits and Demerits of Fluorescence Spectroscopy 9.6 Concluding Remarks References Chapter 10: In Silico Approach in Drug Design and Drug Discovery: An Update 10.1 Introduction 10.2 Drug Discovery Approaches 10.2.1 Traditional Drug Discovery Approach 10.2.2 Limitation of Traditional Drug Discovery 10.2.3 Modern Drug Discovery Approach In-Silico Drug Design (Computer-Aided Drug Design) Structure-Based Drug Design (SBDD) Target Determination Homology Modeling Protein Folding Ligands Search Molecular Docking Molecular Modeling Scoring Functions Ligand Based Drug Design Molecular Dynamics (MD) Simulations Force Field in MD Simulations Site Identification by Ligand Competitive Saturation (SILCS) SILCS-Pharm Similarity Search Lead Optimization Using Structure-Activity Relationship (SAR) Single Step Free Energy Perturbation (SSFEP) Pharmacophore-Based Approaches Molecular Descriptors 10.2.4 Recent Developments of In Silico Approach and Applications In Drug Discovery In Phenotypic and Target-Based Approach In Silico Prediction of Potential Drug-Like Compounds from Plants In-Silico Study in 3D Adenosine Receptors with Antagonists In Metal-Organic Frameworks 10.3 Conclusions References Links Chapter 11: Biological Implications of Polyethylene Glycol and PEGylation: Therapeutic Approaches Based on Biophysical Studies... 11.1 Introduction 11.2 Importance of Structure and Functional Dynamics in PEGylation 11.2.1 Protein Structure-Based Drug Design 11.2.2 Computer-Aided Drug Design 11.2.3 Virtual Screening 11.2.4 Molecular Dynamic Simulation 11.3 PEG in Drug Delivery 11.4 Gene Therapy 11.5 Protein Folding Studies 11.6 Cosmetic Industry 11.7 Food Industry 11.8 Bone and Tissue Engineering 11.9 Bioimaging and Radiotherapy 11.10 Application of PEGs in Cutting Edge Technologies 11.11 Miscellaneous Applications 11.12 Conclusion References Chapter 12: Molecular Dynamics Simulation in Drug Discovery: Opportunities and Challenges 12.1 Introduction 12.2 Computer-Aided Drug Design 12.2.1 Structure-Based Drug Discovery 12.2.2 Ligand-Based Drug Design 12.3 Molecular Dynamics Simulation 12.3.1 Background 12.3.2 Energy Minimization 12.3.3 Explicit and Implicit Solvation 12.3.4 Periodic Boundary Conditions 12.3.5 Computation of Long-Ranged Coulomb Interactions 12.3.6 Work-Flow for MD Simulation 12.4 Linking Wet-Lab Experiments with MD Simulations 12.5 Challenges in MD Simulations 12.6 Opportunities and Applications of MDS 12.6.1 MD-Derived Observables for Drug Discovery 12.6.2 Application in Molecular Docking and Drug Design 12.6.3 Application in Elucidating the Allosteric Binding Sites in Proteins 12.6.4 Application in Refining Protein Structure Predictions 12.6.5 Application in Determination of Peptide Structures 12.7 Future and Challenges of MD Simulation 12.8 Conclusion References Chapter 13: Molecular Dynamic Simulation of Intrinsically Disordered Proteins and Relevant Forcefields 13.1 Introduction 13.2 IDPs and IDPRs: Structure-Function Relationship 13.3 IDPs in the Human Genome: Organizing Functions or Problems? 13.4 Characterization of IDP and IDPRs 13.5 Molecular Dynamics Simulations: Relevance with Structure Biology 13.6 MD Force Fields and Their Role in Conformation Dynamics 13.7 MD Simulation Terminology: Structural Conformation Assessment 13.7.1 Energy Minimization, Equilibration, and Timescale 13.7.2 RMSD, RMSF and Radius of Gyration 13.8 IDPs and Replica Exchange MD: In Perspective of p53-CTD 13.9 Future Prospects References