Quick Guideline for Computational Drug Design (Revised Edition) - Sheikh Arslan Sehgal - E-Book

Quick Guideline for Computational Drug Design (Revised Edition) E-Book

Sheikh Arslan Sehgal

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Beschreibung

Bioinformatics allows researchers to answer biological questions with advanced computational methods which involves the application of statistics and mathematical modeling. Structural bioinformatics enables the prediction and analysis of 3D structures of macromolecules while Computer Aided Drug Designing (CADD) assists scientists to design effective active molecules against diseases. However, the concepts in structural bioinformatics and CADD can be complex to understand for students and educated laymen.
This quick guideline is intended as a basic manual for beginner students and instructors involved in bioinformatics and computational chemistry courses. Readers will learn the basics of structural bioinformatics, primary and secondary analysis and prediction, structural visualization, structural analysis and molecular docking. Therefore, the book is a useful handbook for aspiring scholars who wish to learn the basic concepts in computational analysis of biomolecules.

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Seitenzahl: 114

Veröffentlichungsjahr: 2021

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Table of Contents
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End User License Agreement (for non-institutional, personal use)
Usage Rules:
Disclaimer:
Limitation of Liability:
General:
FOREWORD
PREFACE
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
DEDICATION
Introduction to Structural Bioinformatics
Abstract
MOTIVATION
BIOINFORMATICS
Bioinformatics Approaches
Static
Dynamic
Structural Bioinformatics
Software and Tools
Open-source Bioinformatics Software
Web Services in Bioinformatics
Virtual Screening
Homology Modeling
Steps of Homology Modeling
Template Selection
Target-Template Alignment
Model Construction
Model Assessment
Accuracy
Importance of Homology modeling
Homology Modeling Tools
Sequence Retrieval
UniProt KB
Characterization of Homology Modeling Tools
Structure Prediction Tools
Model Validation Tools
Sequence Analysis Tools
Model Visualization Tools
Virtual Screening and Docking
Recent Advancements in Computational Drug Design
REFERENCES
Protein Primary Sequence Analysis
Abstract
MOTIVATION
PROTPARAM EXPASY
Introduction
Brief Instructions
Requirements
Input
Sequence Submission
Results Interpretation
Conclusion
Limitations of Primary Sequence Analyses
ProtParam Expassy
REFERENCE
Secondary Structure Analyses
Abstract
MOTIVATION
SECONDARY STRUCTURE OF PROTEINS
ProFunc
Brief Instructions
Requirements
Input
Sequence Submission
Accessing of Data
LIMITATIONS OF SECONDARY STRUCTURE ANALYSES TOOLS
ProFunc
REFERENCES
3D Structure Prediction
Abstract
MOTIVATION
STRUCTURE PREDICTION TOOLS
PHYRE2
Introduction
Brief Instructions
Requirements
Input
Sequence Submission
Results Interpretation (Results Screen)
Secondary Structure and Disorder Prediction
Domain Analysis
Detailed Template Information
Alignment View
Conclusion
I-TASSER
Introduction
Brief Instructions
Requirements
Input
Sequence Submission
Accessing Data
Results Interpretation (Output)
Structure Prediction
Top 10 Threading Templates Used by I-TASSER
Top 5 Final Model Predicted by I-TASSER
Structurally Closely Related Protein
Ligand Binding Site
Conclusion
RaptorX
Introduction
Brief Instructions
Requirements
Input
Sequence Submission
Accessing Data
Results Interpretation (Output)
Input Sequence and Domain Partition
Prediction Results
Detailed Prediction Results
SWISS-MODEL
Introduction
Brief Instructions
Requirements
Input
Sequence Submission
Results Interpretation (Results Screen)
Summary
Templates
Models
Conclusion
3D-JIGSAW
Introduction
Brief Instructions
Requirements
Input
Sequence Submission
Accessing Data
Results Interpretation
Conclusion
INTFOLD2
Introduction
Brief Instructions
Requirements
Input
Sequence Submission
Accessing Data
Results Interpretation (Output)
Conclusion
HHPRED
Introduction
Brief Instructions
Requirements
Input
Sequence Submission
Accessing Data
Results Interpretation (Output)
Conclusion
M4T
Introduction
Brief Instructions
Requirements
Input
Sequence Submission
Accessing Data
Results Interpretation (Output)
Conclusion
MODELLER
Introduction
Downloading and Installation
Modeling Steps
Searching for Structures Related to PSEN2
BLAST (Basic Local Alignment Searching Tool)
Template PDB File
Aligning PSEN2 with the Template
Model Building
LIMITATIONS OF STRUCTURE PREDICTION TOOLS
Phyre2
I-Tasser
RaptorX
Swiss-model
3D-Jigsaw
Intfold2
M4T
Modeller
REFERENCES
Protein 3D Structure Assessment, Evaluation, and Validation
Abstract
MOTIVATION
MODEL EVALUATION TOOLS
ERRAT
Introduction
Brief Instructions
Requirements
Input
Sequence Submission
Results Interpretation (Output)
Conclusion
RAMPAGE
Introduction
Brief Instructions
Requirements
Input
Sequence Submission
Results Interpretation
Conclusion
ANOLEA
Introduction
Brief Instructions
Requirements
Input
Results Interpretation
Conclusion
MOL PROBITY
Introduction
Brief Instructions
Requirements
Input
Results Interpretation
Conclusion
VERIFY 3D
Introduction
Brief Instructions
Requirements
Input
Sequence Submission
Results Interpretation
Conclusion
PDBSUM GENERATE
Introduction
Brief Instructions
Requirements
Input
Sequence Submission
Accessing Data
Results Interpretation
Output
Customization of Plots
LIMITATIONS OF STRUCTURE EVALUATION TOOLS ERRAT
Rampage
Verify3D
Pdbsum
REFERENCES
Ligand-Based Computational Molecular Docking Analysis
Abstract
Introduction
Brief Instruction: MOE (Molecular Operating Environment)
REFERENCES
Protein-Ligand Interactions
Abstract
Motivation
Molecular Operating Environment (MOE)
Protein-Ligand Complex
3D Structure Visualization through MOE
Load and Visualize the Complex
Selection Panel
Protein Active Site and Rendering
Active Site
Rendering
Ribbon Panel
Atoms Panel
Contacts Panel
Fog and ZClip
Molecular Surfaces and Maps
2D Ligand Interaction Diagram
Rendering and Export Image
REFERENCE
Quick Guideline for Computational Drug Design (Revised Edition)
Authored by
Sheikh Arslan Sehgal
Department of Bioinformatics
University of Okara
Pakistan
Rana Adnan Tahir
Department of Biosciences
COMSATS University Islamabad
Sahiwal Campus
Sahiwal, Pakistan
&
Muhammad Waqas
Department Bioinformatics and Biotechnology,
Government College University Faisalabad
Faisalabad, Pakistan

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FOREWORD

There are many good textbooks available on Protein Structure prediction and Molecular docking analysis, however, there is no good introductory-level book for the field of Structural Bioinformatics and computational Drug Design. This book aims to give an introduction to Structural Bioinformatics, which is where the previous topics meet to explore three-dimensional protein structures through computational analyses. We provide an overview of existing computational techniques, to validate, predict, analyze and dock protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure validation, Protein structure prediction, and molecular docking analyses.

Computational methods have long been practiced in biology and perhaps, Fibonacci series was the most remarkable historical breakthrough in which Fibonacci used mathematical models for biological systems. Another exemplary work was performed by Gregor Mendel, who identified a biological pattern of inheritance by devising the Punnett square model for alleles of genes. Mathematical model integration was then continued by Thomas Hunt Morgan and later by his student who managed to group genes locus-wise on chromosomes of Drosophila Melanogaster. Population geneticists used the allelic frequency model to calculate the frequency of the most prevalent alleles in a population. With the sequencing of protein amino acids and DNA nucleotides, a new era of mathematical expansion in biology started. Margaret Dayhoff in 1978 designed Point Accepted Mutation (PAM) matrices to find out replacements of amino acids in the primary structure of protein through the process of Natural Selection. Smith-Waterman and Needleman-Wunsch algorithms are key algorithms in alignment with gene sequences. In 1992, Henikoff presented the Blosum matrix scoring function for protein alignment. Together, these alignment algorithms have currently become the basics or starting point of most biological experiments. The alignment algorithms have helped us to discover not only mutational differences between sequences of genes but also these algorithms have been implemented to draw evolutionary relationships between the living organisms. Martin Karplus, Michael Levitt, and Arieh Warshel jointly shared the 2013 Noble Prize for successful implementation of Mathematical simulation models to biological systems. They started a new era of Structural Biology that tends to find out 3D structures of Biomolecules and their interactions with each other computationally. The accuracy of simulations is more than 90% that makes it a favorable choice of an experiment when X-ray crystallography or NMR data is not available. Molecular docking is another technique employed to find out interactions between two biomolecules (preferably Protein-Protein interactions or Protein-small molecule interactions) with a minimum free energy state. Molecular docking has started a new era of Computational Biology called Computer-Aided Drug Design to design drugs against fatal diseases in exponentially less time.

Bioinformatics is an interdisciplinary field that has played a central role in the integration of computational techniques to solve biological problems. “Far too many diseases do not have proven preventions or treatments. To make a difference for the millions of Americans who suffer from these diseases, we must gain better insights into the biological, environmental, and behavioral factors that drive these diseases. Precision medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in environment, lifestyle and genes for each person.” (On January 20, 2015, President Obama announced the Precision Medicine Initiative® – PMI).

This era belongs to Personalized Medicine or Precision Medicine which collectively integrates all the Bioinformatics methods and analyses techniques including DNA sequences, family history, medical history, and environment to design individual-specific medicines that will surely revolutionize lifestyles, lifespan, and thus Medical Science to its new heights.

As Bioinformatics methods and techniques are now considered as basic requirements for most biological experiments, this book will help researchers to have an idea of how to initialize, design, and add new layers of knowledge. This book does not cover all, but most of the major domains and utilizes most cited and most used tools and techniques. The material of the book has been designed for novices to take them up to pro-level in Bioinformatics. Many small tricks have also been discussed that would help researchers to easily understand the theory, perform the experiment, and analyze the results. It is believed that this book must be part of every laboratory that needs Bioinformatics analyses for their experiments.

Muhammad Wajid, PhD School of Applied Biology (SAB) University of Okara, Okara, Pakistan

PREFACE

Bioinformatics is to solve biological problems with advanced computational manners by applying statistical and mathematical approaches. Structural bioinformatics is to predict and analyze 3D structures of macromolecules while Computer-aided drug design (CADD) helps scientists to design effective active molecules against diseases. However, the concept of structural bioinformatics including CADD is still hard to grasp for students and even more for educated laymen. The discussion of the bulk amount of informational data in biological sciences is still an understatement. Bioinformatics studies are being practiced all over the world by universities, scientific groups, international and national companies, and consortia, and it would not be underestimating if bioinformatics will be thought of as the bedrock of future biological sciences. Bioinformatics evolved into complete interdisciplinary sciences to solve biological problems by utilizing statistical, mathematical, and computational approaches.