Computation in BioInformatics E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
COMPUTATION IN BIOINFORMATICS 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. 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. Audience Researchers and engineers in computation biology, information technology, bioinformatics, drug design, biotechnology, pharmaceutical sciences.
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Table of Contents
Cover
Title Page
Copyright
Preface
1 Bioinfomatics as a Tool in Drug Designing
1.1 Introduction
1.2 Steps Involved in Drug Designing
1.3 Various Softwares Used in the Steps of Drug Designing
1.4 Applications
1.5 Conclusion
References
2 New Strategies in Drug Discovery
2.1 Introduction
2.2 Road Toward Advancement
2.3 Methodology
2.4 Role of OMICS Technology
2.5 High-Throughput Screening and Its Tools
2.6 Chemoinformatic
2.7 Concluding Remarks and Future Prospects
References
3 Role of Bioinformatics in Early Drug Discovery: An Overview and Perspective
3.1 Introduction
3.2 Bioinformatics and Drug Discovery
3.3 Bioinformatics Tools in Early Drug Discovery
3.4 Future Directions With Bioinformatics Tool
3.5 Conclusion
Acknowledgements
References
4 Role of Data Mining in Bioinformatics
4.1 Introduction
4.2 Data Mining Methods/Techniques
4.3 DNA Data Analysis
4.4 RNA Data Analysis
4.5 Protein Data Analysis
4.6 Biomedical Data Analysis
4.7 Conclusion and Future Prospects
References
5
In Silico
Protein Design and Virtual Screening
5.1 Introduction
5.2 Virtual Screening Process
5.3 Machine Learning and Scoring Functions
5.4 Conclusion and Future Prospects
References
6 New Bioinformatics Platform-Based Approach for Drug Design
6.1 Introduction
6.2 Platform-Based Approach and Regulatory Perspective
6.3 Bioinformatics Tools and Computer-Aided Drug Design
6.4 Target Identification
6.5 Target Validation
6.6 Lead Identification and Optimization
6.7 High-Throughput Methods (HTM)
6.8 Conclusion and Future Prospects
References
7 Bioinformatics and Its Application Areas
7.1 Introduction
7.2 Review of Bioinformatics
7.3 Bioinformatics Applications in Different Areas
7.4 Conclusion
References
8 DNA Microarray Analysis: From Affymetrix CEL Files to Comparative Gene Expression
8.1 Introduction
8.2 Data Processing
8.3 Normalization of Microarray Data Using the RMA Method
8.4 Statistical Analysis for Differential Gene Expression
8.5 Conclusion
References
9 Machine Learning in Bioinformatics
9.1 Introduction and Background
9.2 Machine Learning Applications in Bioinformatics
9.3 Machine Learning Approaches
9.4 Conclusion and Closing Remarks
References
10 DNA-RNA Barcoding and Gene Sequencing
10.1 Introduction
10.2 RNA
10.3 DNA Barcoding
10.4 Main Reasons of DNA Barcoding
10.5 Limitations/Restrictions of DNA Barcoding
10.6 RNA Barcoding
10.7 Methodology
10.8 Conclusion
Abbreviations
Acknowledgement
References
11 Bioinformatics in Cancer Detection
11.1 Introduction
11.2 The Era of Bioinformatics in Cancer
11.3 Aid in Cancer Research via NCI
11.4 Application of Big Data in Developing Precision Medicine
11.5 Historical Perspective and Development
11.6 Bioinformatics-Based Approaches in the Study of Cancer
11.7 Conclusion and Future Challenges
References
12 Genomic Association of Polycystic Ovarian Syndrome: Single-Nucleotide Polymorphisms and Their Role in Disease Progression
12.1 Introduction
12.2 FSHR Gene
12.3 IL-10 Gene
12.4 IRS-1 Gene
12.5 PCR Primers Used
12.6 Statistical Analysis
12.7 Conclusion
References
13 An Insight of Protein Structure Predictions Using Homology Modeling
13.1 Introduction
13.2 Homology Modeling Approach
13.3 Steps Involved in Homology Modeling
13.4 Tools Used for Homology Modeling
Acknowledgement
References
14 Basic Concepts in Proteomics and Applications
14.1 Introduction
14.2 Challenges on Proteomics
14.3 Proteomics Based on Gel
14.4 Non-Gel–Based Electrophoresis Method
14.5 Chromatography
14.6 Proteomics Based on Peptides
14.7 Stable Isotopic Labeling
14.8 Data Mining and Informatics
14.9 Applications of Proteomics
14.10 Future Scope
14.11 Conclusion
References
15 Prospects of Covalent Approaches in Drug Discovery: An Overview
15.1 Introduction
15.2 Covalent Inhibitors Against the Biological Target
15.3 Application of Physical Chemistry Concepts in Drug Designing
15.4 Docking Methodologies—An Overview
15.5 Importance of Covalent Targets
15.6 Recent Framework on the Existing Docking Protocols
15.7 S
N
2 Reactions in the Computational Approaches
15.8 Other Crucial Factors to Consider in the Covalent Docking
15.9 QM/MM Approaches
15.10 Conclusion and Remarks
Acknowledgements
References
Index
Also of Interest
End User License Agreement
Guide
Cover
Table of Contents
Title Page
Copyright
Preface
Begin Reading
Index
Also of Interest
End User License Agreement
List of Illustrations
Chapter 1
Figure 1.1 Flowchart of
in silico
approaches in drug designing.
Chapter 2
Figure 2.1 High-throughput data used in bioinformatics.
Figure 2.2 Integrated OMICS in drug discovery.
Figure 2.3 Role of integrated omics in clinical biology.
Figure 2.4 Screening methods in drug discovery.
Figure 2.5 Chemoinformatic in drug discovery.
Chapter 3
Figure 3.1 Schematic demonstration of a computer-aided drug discovery.
Figure 3.2 Schematic presentation of structure-based drug design.
Figure 3.3 Schematic presentation of ligand-based drug design.
Figure 3.4 A schematic presentation of protein-ligand interaction visualization ...
Chapter 4
Figure 4.1 Data mining techniques.
Figure 4.2 Application of statistics in data mining.
Figure 4.3 Data collection techniques.
Figure 4.4 Types of clustering methods.
Figure 4.5 Type of association rules.
Figure 4.6 Classification technique.
Figure 4.7 Importance of DNA sequence data analysis.
Figure 4.8 Disadvantages of next-generation sequencing data analysis.
Chapter 5
Figure 5.1 Approaches for virtual screening.
Figure 5.2 Screening strategies.
Figure 5.3 Structure-based virtual screening workflow.
Figure 5.4 Road map for high-throughput screening.
Figure 5.5 Softwares for removing garbage from the collection.
Figure 5.6 Workflow of virtual screening process.
Figure 5.7 Software packages for library design.
Figure 5.8 Binding sites detection programs [38, 41–43].
Figure 5.9 Sampling algorithms for molecular docking.
Figure 5.10 Advantages of virtual screening process over HTS.
Chapter 6
Figure 6.1 Screening strategies for the targets for the drug discovery process.
Figure 6.2 Overview on drug discovery process.
Figure 6.3 Platform-based drug development process.
Figure 6.4 Pathways for the development of novel biotherapeutics.
Figure 6.5 Bifurcation of CADD.
Figure 6.6 Properties of a promising drug target [48].
Figure 6.7 Target validation steps.
Figure 6.8 Arms of high-throughput methods (HTM).
Figure 6.9 HTM tools for toxicological evaluation.
Chapter 7
Figure 7.1 Bioinformatics use in six steps [23, 24].
Figure 7.2 Bioinformatics applications in different areas [34].
Chapter 8
Figure 8.1 Image analysis of chips. To check the integrity of microarray chips, ...
Figure 8.2 (a to c) Boxplot (a), Density histogram (b), and MA plot (c). These p...
Figure 8.3 Boxplots of array data after normalization.
Figure 8.4 MA plots to compare the normalization method. Before normalization (a...
Figure 8.5 Result showing log fold change in gene expression along with p-value.
Figure 8.6 Volcano plot of the log fold change in gene expression. The scattered...
Chapter 9
Figure 9.1 Typical example of the correlation between Artificial Intelligence, M...
Chapter 10
Figure 10.1 Flowchart showing a remarkable brief history of genetics from 1865 t...
Figure 10.2 Showing the structure of chromosome at molecular level [231].
Figure 10.3 Different types of RNA [232].
Figure 10.4 Structure of DNA and RNA [234].
Figure 10.5 Sizes (bp) vs. chromosome numbers (2n) of various taxa plant genomes...
Figure 10.6 Sample of ITS phylogeny. The cladogaram of trees of the legume. BioE...
Figure 10.7 Schematic description of the preparation of the small RNA cDNA libra...
Figure 10.8 General overview of bioinformatic analysis pipeline [237].
Figure 10.9 Overview of gene sequencing.
Chapter 11
Figure 11.1 Role of bioinformatics in cancer detection.
Figure 11.2 Pipeline analysis.
Figure 11.3 Different bioinformatics-based approaches.
Figure 11.4 Steps involved in SLAMS.
Figure 11.5 Steps involved in COPA.
Chapter 12
Figure 12.1 Prevalence of PCOS and its sign and symptoms.
Figure 12.2 Differences between normal ovulation and cyst formation (PCOS).
Figure 12.3 Factors and food that cause PCOS.
Figure 12.4 Factors and reasons behind the infertility condition.
Figure 12.5 Insulin resistance (IR) causes and symptoms.
Chapter 14
Figure 14.1 The difference between the genomics and proteomics. (Source: https:/...
Figure 14.2 Gel-based proteomics. (Source: https://www.slideshare.net/AngelSFord...
Chapter 15
Figure 15.1 The difference between non-covalent and covalent complexes. (a) Inte...
Figure 15.2 Covalent bond between protein and peptide substrate (PDB: 1AIM, www....
Figure 15.3 OXA23-imipenem interactions (the picture represents the significance...
Figure 15.4 The pKa values of existing ionizable residues in the protein will ha...
List of Tables
Chapter 1
Table 1.1 The list of modeling softwares that are generally used in protein mode...
Table 1.2 The list of molecular docking softwares is represented in tabular form...
Table 1.3 The list of molecular docking tools is represented in tabular form.
Table 1.4 The list of softwares used in the steps of drug designing is represent...
Chapter 3
Table 3.1 List of some widely and freely available tools and software’s for mole...
Table 3.2 List of some widely and freely used tools for physicochemical, toxicit...
Chapter 5
Table 5.1 Types of screening modes [27].
Chapter 7
Table 7.1 Milestones in bioinformatics [22].
Chapter 10
Table 10.1 Types of DNA [230].
Table 10.2 Comparison of different types of RNA [233].
Table 10.3 Difference between DNA and RNA [235].
Table 10.4 Several key articles concerning DNA barcodes have been published sinc...
Table 10.5 A brief of the commonly used programs used for the phylogeny and DNA ...
Table 10.6 Comparison of the commonly used NGS methods.
Chapter 15
Table 15.1 The drug discovery process and descriptions.
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
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Series Editor: Dr S. Balamurugan ([email protected])
Scope: Artificial Intelligence and Soft Computing Techniques play an impeccable role in industrial transformation. The topics to be covered in this book series include Artificial Intelligence, Machine Learning, Deep Learning, Neural Networks, Fuzzy Logic, Genetic Algorithms, Particle Swarm Optimization, Evolutionary Algorithms, Nature Inspired Algorithms, Simulated Annealing, Metaheuristics, Cuckoo Search, Firefly Optimization, Bio-inspired Algorithms, Ant Colony Optimization, Heuristic Search Techniques, Reinforcement Learning, Inductive Learning, Statistical Learning, Supervised and Unsupervised Learning, Association Learning and Clustering, Reasoning, Support Vector Machine, Differential Evolution Algorithms, Expert Systems, Neuro Fuzzy Hybrid Systems, Genetic Neuro Hybrid Systems, Genetic Fuzzy Hybrid Systems and other Hybridized Soft Computing Techniques and their applications for Industrial Transformation. The book series is aimed to provide comprehensive handbooks and reference books for the benefit of scientists, research scholars, students and industry professional working towards next generation industrial transformation.
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Computation in Bioinformatics
Multidisciplinary Applications
Edited by
S. Balamurugan
Founder & Chairman, Albert Einstein Engineering and Research Labs (AEER Labs), Vice Chairman, Renewable Energy Society of India (RESI), India
Anand Krishnan
NRF-DSI Innovation Fellow, Department of Chemical Pathology University of the Free State (Bloemfontein Campus), Bloemfontein, South Africa
Dinesh Goyal
Poornima Institute of Engineering & Technology, Jaipur, India
Balakumar Chandrasekaran
Faculty of Pharmacy, Philadelphia University, Amman, Jordan
and
Boomi Pandi
Department of Bioinformatics, Alagappa University, Karaikudi, India
This edition first published 2021 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2021 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-65471-1
Cover image: Pixabay.ComCover design by Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
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Preface
The past couple of years will be remembered for the COVID-19 pandemic and ensuing lockdowns, wherein almost every country saw a series of lockdowns and consequent suffering. To overcome the pandemic, the discovery of vaccines and other alternative therapies were attempted worldwide. Currently, the discovery of new solutions to the pandemic can be facilitated through the use of promising bioinformatics techniques and integrated approaches. Hence, this book provides readers with an understanding of the use of bioinformatics tools in new drug design and the discoveries which are inevitable in the present situation. The book covers a broad spectrum of the bioinformatics field, starting with the basic principles, concepts, and application areas under multidisciplinary sections. 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 silico 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, which are highly relevant to the present scenario. 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. Therefore, the contents of this book will be useful for students, scientists, researchers, and professionals working in the field of bioinformatics, drug design, pharmacoinformatics and medicinal chemistry under the umbrella of academia and industry.
Our sincere gratitude especially goes to all the contributors for their useful insights concerning bioinformatics and its multidisciplinary applications. We sincerely thank Scrivener Publishing for their assistance, constant support, and patience in finalizing this book.
S. BalamuruganCoimbatore, India
Krishnan AnandBloemfontein, South Africa
Dinesh GoyalJaipur, India
Balakumar ChandrasekaranAmman, Jordan
Boomi PandiKaraikudi, India
1Bioinfomatics as a Tool in Drug Designing
Rene Barbie Browne, Shiny C. Thomas and Jayanti Datta Roy*
Department of BioSciences, Assam Don Bosco University, Sonapur, Assam, India
Abstract
Drug discovery is the method of identifying and validating a disease target and discovering and developing a chemical compound which can interact with its specific target. This process is very complex and time consuming, requiring multidisciplinary expertise and innovative approaches. To overcome the difficulties and complexity, in silico approach is used that reduces the time and expenditure. This chapter addresses the importance of bioinformatics in drug designing. It focuses on bioinformatics tools like AutoDock, LigPlot, FlexX, and many other softwares which play an important role in rational designing of drug. Thus, the main goal of this chapter is to provide an overview of the importance of bioinformatics tools in designing a drug.
Keywords: AutoDock, LigPlot, FleX, GenBank, SWISS-PROT, PDB
1.1 Introduction
Bioinformatics is a multidisciplinary field of life sciences merging biology, computer science, and information technology into a single discipline [1]. A wide range of subject areas is included in this field. These subject areas are structural biology, gene expression studies, and genomics. Computational techniques play an important role analyzing information that are associated with biomolecules on a large scale [2].
The main goal of bioinformatics aims toward better understanding of living cells and how it functions at the molecular level. Besides being essential for basic genomic and molecular biology research, bioinformatics plays a pivotal role on many areas of biotechnology and biomedical sciences [3]. In this aspect, bioinformatics play a vital role in designing of novel drugs. The interactions between protein and ligand investigated computationally provide rational basis for rapidly identifying novel synthetic drugs [4]. Information available regarding the 3D structure of proteins makes it easier to design molecule in such a way that they are capable of binding to the receptor site of a target protein with great affinity and specificity. Consequently, it significantly reduces time and cost necessary to develop drugs with higher potency, fewer side effects, and less toxicity than using the traditional trial-and-error approach.
This field of computational study has also reduced the sacrifice of animals in research. Nowadays, the number of potential drug candidate molecules is increasing with the use of computational simulation and informatics methods. These methods help in reducing the number of animals sacrificed in drug discovery process [5]. By efficient use of existing knowledge, computational studies have also helped in reducing the number of animal experiments which is required in basic biological sciences [6].
Bioinformatics tools are now appreciably used for developing novel drugs, leading to a new variety of research. Discovery and development of a new drug is generally very complex process consuming a whole lot of time and resources. So, bioinformatics techniques in designing tools are now broadly used so as to growth the efficiency of designing and developing a novel synthetic drug [4]. Drug discovery is the method of identifying, validating a disease target, followed by designing a chemical compound which can interact with that target resulting in inhibition of biological response which increases the rate of the disease. All these processes can be supported by various computational tools and methodology. Some of the factors which need to be observed during identification of the drug target are sequences of protein and nucleotide, mapping information, functional prediction, and data of protein and gene expression. Bioinformatics tools have helped in collecting the information of all these factors leading to the development of primary and secondary databases of nucleic acid sequences, protein sequences, and structures. Some of the commonly used databases include GenBank, SWISS-PROT, PDB, PIR, SCOP, and CATH. These databases have become indispensable tools to accumulate information regarding disease target. Databases like PubChem and ChemFaces provide structural and biological information of known drug like compounds which helps to identify the drug target for designing drug in research field [7]. These databases help in saving time, money, and efforts of the researchers.
Designing of drugs using bioinformatics tools can be broadly classified into two main categories, viz.,
a) Structure-based drug design (SBDD)
b) Ligand-based drug design (LBDD)
a) Structure-Based Drug Design (SBDD):
Designing of drugs using SBDD method utilizes the 3D structure of the biological target which can be acquired via X-ray crystallography or NMR spectroscopy techniques [8]. Candidate drugs can be predicted on the basis of its binding affinity to the target using the structural information of the biological target. If the structure of the biological target/receptor is unavailable, then in that case, the structure can be predicted using homology modeling. It usually requires the amino acid sequence of the target protein, which when submitted constructs models that can be compared with the 3D structure of similar homologous protein (template). In order to know the interactions or bio-affinity for all tested compounds, molecular docking of each compound is performed into the binding site of the target, predicting the electrostatic fit between them.
b) Ligand-Based Drug Design (LBDD):
In this method of designing drug, the structural information of the small molecule/compound is known which binds to the target. The compounds/ligands which help in developing a Pharmacophore model possess all the important structural features necessary for binding to a target active site. Most common techniques used in this approach are Pharmacophore modeling and quantitative structure activity relationships (3D QSAR). These techniques are used in developing models with predictive ability that are suitable for lead identification and optimization [9]. Compound which are similar in structure also possess the same biological interaction with their target protein.
1.2 Steps Involved in Drug Designing
The flowchart in Figure 1.1 has been constructed to outline the phases that are involved in drug designing using in-silico approaches.
Figure 1.1 Flowchart of in silico approaches in drug designing.
1.2.1 Identification of the Target Protein/Enzyme
Before designing a novel synthetic drug, one needs to know all about the signaling pathways which lead to the disease. A novel drug needs to be designed in such a way that can interact with the target protein without interfering with normal metabolism. The most conventional method is to block the activity of the protein with a small molecule which can be the prospective drug. Virtual screenings of the target for compounds that can bind and inhibit the protein/enzyme are now performed using various bioinformatics softwares. Another strategy is to find other proteins which can regulate the activity of the target by binding and forming a complex, thereby controlling the disease.
PDB:
The Protein Data Bank (PDB) is the repository of information about the 3D structure structures of biological molecules which include nucleic acids and proteins (
https://www.rcsb.org
). The main function of this database is to provide 3D structural data of all the organisms which includes yeast, bacteria, plants, and other animals including humans. Techniques such as X-ray crystallography, electron microscopy, and nuclear magnetic resonance (NMR) spectroscopy help in extracting the information of the 3Dstructure of the macromolecules [10].
Swiss Target Prediction:
It is a web server which can accurately predict the targets of bioactive molecules based on similarity measures with known ligands [11]. In this web server, the predictions can be carried out in five different organisms, and mapping predictions by homology. The SwissTargetPrediction server is easily is accessible and is free of charge without any registration (
www.swisstargetprediction.ch
)
SPPIDER:
The SPPIDER protein interface recognition is a server that can be used to predict residues that needs to be in the putative protein interfaces by considering single protein chain with resolved 3D structure [12]. It can analyze protein-protein complex with given 3D structural information and can identify residues that are being in contact (
http://sppider.cchmc.org/
).
1.2.2 Detection of Molecular Site (Active Site) in the Target Protein
If a drug that needs to bind to a particular on a particular protein or nucleotide is known, then it can be tailor made to bind at that site. This is often performed computationally using several different techniques. Traditionally, the primary way is to identify compounds which can interact with the specific molecular site responsible for the disease. A second method is to test the specific compound against various molecular sites known for the occurrence of the disease. However, if the 3D structure of the protein target is not available, then the method of molecular modeling needs to be performed in order to construct the structure for further analysis.
CASTp:
Computed Atlas of Surface Topography of proteins (CASTp) is an online resource which is used for locating, delineating and measuring of concave surface regions on the 3D structures of protein [13]. It includes pockets which are located on protein surfaces. This server can be used to study surface features and functional regions of proteins. The server is updated daily and can be accessed at
http://cast.engr.uic.edu
Active Site Prediction Server:
Active Site Prediction of Protein server help in computing the cavities in a given target protein. This sever can be easily accessed at
http://www.scfbio-iitd.res.in/dock/ActiveSite_new.jsp
.
3DLigandSite:
It is an automated method which can predict the ligand binding sites. One can submit a sequence or a protein structure and once submitted Phyre is run to predict the structure. The structure can then be used to search a structural library in order to identify homologous structures with bound ligands. These ligands are then superimposed onto the protein structure in order to predict a ligand binding site [14]. It can be accessed at
http://www.sbg.bio.ic.ac.uk/3dligandsite
.
1.2.3 Molecular Modeling
When desired structure of a target is not available, determining of the structure experimentally becomes difficult. In such conditions, designing of protein structure from pre-existing data and sequence becomes necessary. In designing of drugs, protein-ligand binding plays an important role. So, it is important to have a 3D structure of a protein. The 3D structures of protein are searched in a widely used database called PDB which provides a repository for all the known protein 3D structures [15]. X-ray crystallography and NMR spectroscopy are the two important techniques which determine the proteins 3D structure experimentally. This can be performed using in silico approach which provides a “homology-based modeling” method for protein modeling also referred as molecular modeling. It is an important computational technique which helps in designing the structure of a novel compound. It plays an important role in the study of various biological pathways which includes protein folding and stability, enzyme catalysis, identification of novel proteins, and other macromolecules [16]. This methodology works on the basis of sequence similarity, i.e., “proteins with similar sequences have similar structures”. The models which are generated usually bear template significant sequence of more than 30% [17]. It is an accurate method allowing the researches to obtain an authenticated structure which might be useful as a drug after further validations. Because of this, virtual screening is necessary and has become an important part of drug discovery process [18]. The most common modeling softwares along with their description has been depicted in Table 1.1. Some of the important steps involved in molecular modeling are as follows:
a) Recognition of template and sequence alignment: Recognition of the template is the beginning step in homology modeling. To identify the homologous sequences of unknown protein, one can search the unknown against the pre-existing ones whose structure is known and identified. The homologous sequences can be identified by similarity searches which can be performed using sequence alignment programs such as BLAST (Basic Local Alignment Search Tool).
b) Model building: Some of the methods involved in building a model are spatial restraint, rigid-body assembly, segment matching, and artificial evolution.
c) Refinement modeling: Refinement of model involves addition, deletion, and substitution of amino acid residues, which includes loop modeling and side-chain modeling. This kind of modeling is based on molecular dynamics simulations, genetic algorithms, and Monte Carlo methods. AMBER, CHARMM22, and MM3 are commonly used force fields for energy minimization of modeled structures.
1. Loop modeling: In homologous protein sequences, insertion and substitution of amino acid residues in variable portion of the protein are referred as loops.
2. Side-chain modeling: It involves substitution of the side chains on the backbone structure of the protein. The substitution is analyzed by Root Mean Square Deviation (RMSD) values.
d) Validation of modeled protein structure: The protein structure obtained after homology modeling needs to be validated in order to check the accuracy of the modeling. This can be performed using web servers like WHATCHECK, WHAT IF, VADAR, and PROCHECK.
e) Small molecule databases: Screening of compounds in drug discovery to identify novel and drug-like properties can be performed using small molecule databases like NCBI, PubChem, and ChEMBL [19].
Table 1.1 The list of modeling softwares that are generally used in protein modeling is represented in tabular form.
S. no.
Name of software
Description
Reference
1
MODELLER
It involves homology modeling of the three-dimensional structures of the target protein.
[20]
2
UCSF Chimera
It helps in the visualization and analysis of molecular structures.
[21]
3
SWISS PDB VIEWER
It allows to analyze and model proteins.
[22]
4
Geno3D
It is an automatic web server for protein molecular modeling.
[23]
5
SWISS MODEL
Automated comparative modeling of protein structures can be performed.
[24]
6
CCP4
It helps in macromolecular structure determination.
[25]
7
Abalone
It is a modeling program which involves molecular dynamics of biopolymers
[26]
8
Tinker
It performs molecular mechanics and dynamics along with some unique features for biopolymers.
[27]
MODELLER:
It is a computer program which models 3D structures of proteins and their assemblies. This program is the most frequently used program for homology modeling. In order to construct, one needs to provide aligned sequence which will be modeled with known structures [27]. The program will then easily construct/build a model with no hydrogen atoms (
https://salilab.org/modeller/
).
SWISS PDB VIEWER:
The Swiss PDB Viewer is a free molecular graphics program that helps us to evaluate various proteins at the same time. The proteins can be placed one on top of another to reason the structural alignments and compare their active sites [28]. This program can be easily accessed at
https://spdbv.vital-it.ch/
.
SWISS MODEL:
It is a protein structure homology-modeling server which is fully automated. One can easily obtain it through ExPASy web server or from Swiss PDB Viewer. Their main aim is to model protein and make it easier to all researchers of life sciences [29]. It can be accessed at
https://swissmodel.expasy.org
.
1.2.4 Virtual Screening
Virtual screening is an in silico method used in drug designing. They are involved in identifying active compounds using chemical databases. It helps in identifying the structure of those compounds that may act as lead compounds with maximum affinity for a drug target [30]. The drug target may be a protein or enzyme. Virtual screening approaches are mainly of two types: structure-based and ligand-based. In structure-based virtual screening, molecular docking studies help in screening of target protein against ligands/compounds that are present in chemical libraries. The process of docking is usually based on the functional scores and binding strength of the compound with its target. Virtual screening uses computational programs to evaluate huge libraries of compounds automatically [31]. It is an accurate method allowing the researches to obtain an authenticated structure which might be useful as a drug after further validations. Because of this, virtual screening is necessary and has become an important part of drug discovery process [18].
MTiOpenScreen:
This approach involves docking of small molecules and virtual screening. The screening can be done in one run which can be up to 5,000 small molecules in different databases. The run can also be up to 10,000 molecules selected among 15,000 compounds that are prepared to be docked which is provided at MTiOpenScreen. The web server can be obtained from
http://bioserv.rpbs.univ-parisdiderot.fr/services/MTiOpenScreen/
.
ICM-VLS:
ICM Virtual Ligand Screening (VLS) is a combination of internal coordinate docking methodology with a sophisticated global optimization scheme. Its accuracy and fast potentials have led to an efficient virtual screening methodology in which ligands are fully and continuously flexible. It can be accessed at
http://www.molsoft.com/vls.html
.
1.2.5 Molecular Docking
Molecular docking is a computer simulation methodology which predicts the binding affinity of the target protein with the ligand at the atomistic level. The most important goal of molecular docking studies is to predict the binding conformation of protein-ligand and to estimate its interaction. It is also one of the main tools for virtual screening procedures, where a library of several compounds is “docked” against one drug target returning the best hit. Identifying the active site of the target protein where the ligand will bind is the first important step which needs to be performed before docking. This can be performed using programs like Q-SiteFinder, LigA Site, Meta Pocket, and CASTp [32]. A molecular docking study where the process of docking is performed without predicting the active site is referred as “Blind Docking” [33]. Here, Tables 1.2 and 1.3 show some of the most common Molecular docking software programs along with their specifications.
AutoDock:
It is an automated program to predict ligand and protein (bio-macromolecular targets) interactions. With recent advancement in bimolecular, X-ray crystallography is helping to provide structural information of complex bio-molecules such as protein and nucleic acids. The structures can be taken/downloaded and can be used as targets for new drug molecules in controlling diseases and disorders of human, animal, and plant and understanding of fundamental aspects of biology [41]. It can be downloaded from
http://www.scripps.edu/olson/forli/autodock_flex_rings.html
(accessed 12.12.16).
GOLD:
Genetic Optimization for Ligand Docking is a genetic algorithm which provides docking of flexible ligand and a protein with flexible hydroxyl groups. The software uses a scoring function that is based on favorable conformations found in Cambridge Structural Database. The speed of GOLD and the reliability of its predictions depend on the control of different values of the genetic algorithm parameters. It provides reliable results both protein and ligand (
http://www.ccdc.cam.ac.uk/Solutions/GoldSuite/Pages/GOLD.aspx
) (accessed 20.12.16).
Table 1.2 The list of molecular docking softwares is represented in tabular form.
S. no.
Name of software
Description
Reference
1
AutoDock
It performs automated docking of flexible ligands to macromolecules.
[34]
2
DockVision
It performs Monte Carlo, Genetic Algorithm, and database screening docking algorithms.
[35, 36]
3
GOLD
It helps in identifying correct binding modes of the active target molecules.
[37]
4
Docking Server
It provides a web-based interface for the molecular docking of protein and ligand.
[38]
5
SwissDock
It is a protein ligand server that is accessed through ExPASy.
[39]
6
CombiBUILD
It is a structure-based drug design program which helps in designing of combinatorial libraries.
[37]
7
QM Polarized Ligand Docking
It performs the function of both Glide and Q-Site applications of Schrodinger Suit.
[40]
8
Docking Server
It provides a web-based, easy to use interface for the molecular docking of protein and ligand.
[34]
10
Click2Drug
It is a protein ligand server that is accessed through ExPASy.
http://www.click2drug.org/
Table 1.3 The list of molecular docking tools is represented in tabular form.
Tool
Brief description with uses
BLAST
Basic local alignment search tool; used for sequencing of DNA and protein.
RasMol
Raster molecule tool; used for molecular visualization of RNA/DNA and protein.
Discovery studio
Software; used for modeling and simulation.
Pub Med
Free search engine; used for searching matter related to medical and life sciences.
PDB
Protein Data Bank; used to collect information related to macromolecule.
Chem Draw
They are a part of the Chem office program which are used for drawing chemical molecule.
Marvin Sketch
These are advanced chemical editors that are used for drawing chemical structures and reactions.
PubChem
Database; used to collect information about structure and physiochemical properties of chemical compound.
AutoDock
Software; used for molecular docking.
1.2.6 QSAR (Quantitative Structure-Activity Relationship)
It is a statistical approach which attempts to correlate relationships between physical and chemical properties of molecules to their biological activities. QSAR predicts the molecular properties from their structure without any need to perform the experiment using in vitro or in vivo. This method saves time and resources [39]. Descriptors which are commonly used are number of rotatable bonds LogP and molecular weight (MW). This approach is used in optimizing lead which is the most important step of discovering drugs. The two techniques in 3D QSAR developed for LBDD are comparative molecular field analysis (CoMFA) and Comparative molecular similarity indices analysis (CoMSIA). Based on the data dimensions many QSAR approaches range from 1D QSAR to 6D QSAR.
a) CoMFA (Comparative Molecular Field Analysis)
It is categorized as 3D QSAR computational technique in which incorporation of experimental activities (log units of KI or IC 50) and the 3D structures of the molecules are done in the study. For this study, a set of derivatives of bioactive compounds having different substitutions is first selected. All of these compounds are then distributed into 30% test set and 70% training set. For QSAR performance, several softwares are available. An important aspect in CoMFA analysis is that it requires a common substructure with good alignment having the same conformation in all molecules.
b) CoMSIA (Comparative Molecular Similarity Indices Analysis)
It is a more advanced method of CoMFA, having fewer limitations. In this approach, SEAL similarity method is used as descriptors. Some of the descriptors used in this method are steric, electrostatic, hydrophobic, and hydrogen bonding. In the ligand binding areas, the unfavored region or the favored regions are indicated by generated contours. Sybyl-X 2.0 and E-Dragon are the software used for QSAR studies.
OCHEM:
It is a web-based platform which aims to automate and simplify the typical steps that are required for QSAR modeling. It performs all the steps of a typical modeling workflow and provides facilities to use these data in the modeling process. It can be accessed at
https://ochem.eu/home/show.do
.
Discovery Studio:
This software helps in analyzing the molecular structures/sequences and modeling it. It provides tools for performing analysis of basic data including functionality for editing and viewing data. It is a free viewer which can be used to open data generated by other softwares in the Discovery Studio. It can be downloaded from
https://www.3dsbiovia.com/products/collaborative-science/bio-via-discovery-studio/visualization-download.php
.
1.2.7 Pharmacophore Modeling
This is a powerful method which can easily categorize a group of molecules/ligands on the basis of active and inactive compounds. They provide set of molecular characteristics that is essential for the macromolecular recognition of ligands triggering a biological reaction. Some of the essential features modeled in pharmacophore are aromatic, hydrophobic, hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), and anion and cation residues. Two main types of Pharmacophore modeling are structure-based modeling and ligand-based modeling.
Structure-based Pharmacophore modeling depends on the 3D structure of the protein obtained from PDB. These structures in PDB are provided by X-ray crystallography technique and/or NMR spectroscopy techniques. In the absence of 3D structure of protein, ligand-based Pharmacophore modeling is performed. Some of the softwares which are used for pharmacophore modeling are HypoGen, HipHop, DISCO, and PHASE.
PHASE:
It is a user friendly pharmacophore modeling solution for LBDD and SBDD. It creates hypotheses from protein-ligand complexes and apo proteins with Schrödinger’s unique e-Pharmacophores technology. It can be accessed at
https://www.schrodinger.com
› phase.
1.2.8 Solubility of Molecule
Once the above steps have been done, the prospective compound is checked for whether the compound is water soluble or readily soluble in lipid which will affect the entry of the cells. The ability of a drug to make entrance into the cell and to bind to the target is an important factor which will determine its potency.
SwissADME:
This web tool can analyze drug-likeness and pharmacokinetics of molecules. It evaluates the affability of small molecules in order to compute the physicochemistry of one or several small molecules. It can be easily accessed at
http://www.swissadme.ch/index.php
.
1.2.9 Molecular Dynamic Simulation
It is a computational method which involves the solution of Newton’s equation for motion to examine the dynamicity of the biological macromolecules. It provides comprehensive information on the fluctuations and conformational changes of proteins and nucleic acids. It helps in understanding the constancy of complexes of protein-ligand or of individual protein. Docking of protein-ligand complex with the ideal binding affinity is usually subjected to MD simulation. The protein topology is initially obtained by standard parameters using GROMACS or LEAP program. Online server PRODRG program is used for generating ligand topology [42]. It allows the study of interactions which occurs between different macromolecules during various cellular life processes and also analyzing of biological processes occurring in a living system. GROMACS is one of the most commonly used MD simulation softwares [43]. It produces trajectory files which carries the information of every conformational change that would have occurred on each atom during simulation. It provides a platform for the researchers to study the stability and minimization of energy of proteins as well as protein bound complexes. Some of software packages like NAMD, GROMACS, CHARMM, and AMBER are used for molecular dynamic simulation [44].
GROMACS:
It is one of the most commonly used molecular dynamic simulation softwares. Input files are taken in PDB format which then produces trajectory files that carry the information of each and every conformational changes taking place that would have occurred on each atom during simulation. It requires several commands to process this software. Using this software, researchers are able to study the stability and minimization of energy of proteins as well as protein bound complexes. It can be downloaded from
http://www.gromacs.org
.
1.2.10 ADME Prediction
It plays an important role in the process of drug discovery. Most drugs with poor pharmacokinetic and toxicity predictions fail in the clinical trials. The toxicity and the molecular property are important properties in a drug whose prediction will help in determining the positivity or negativity of the drug during clinical trials. This method of prediction follows Lipinski’s rule of five [45]. The Lipinski’s rule of five states that HBAs must be less than 10, HBDs must be less than 5, MW of the ligand must not be more than 500 Daltons; the number of rotatable bonds should be less than 10 and the milogP value must be less than five. The Lipinski’s rule accepts compounds with one violation and those satisfying these rules can be orally available for humans. Softwares like PreADMET, VolSurf, admet-SAR, QikProp, PASS, and Molinspiration are used for ADME prediction [46, 47].
MOLINSPIRATION:
It provides a wide range of cheminformatic softwares and tools which supports manipulation and processing of molecules. It also includes SD file conversion, SMILES, fragmentation of molecules, creation of tautomers, and calculating molecular properties that are required in QSAR, modeling and designing of drugs, depiction of high quality molecule, and molecular database tools which supports substructure and similarity searches. Molinspiration is user friendly and can be easily accessed at
https://www.molinspiration.com/
.
1.3 Various Softwares Used in the Steps of Drug Designing
Bioinformatics tools provides information about potential targets that include nucleotide and protein sequencing information, protein and gene expression data, prediction of the function, information of the pathway, mapping information, disease associations, information of the structure, and taxonomic distribution, among others. This helps in reducing time, effort, and money in characterization of different targets. The field of bioinformatics has thus become a major component of the drug discovery pipeline, playing a vital role for validating drug targets. Table 1.4 illustrates some of the most commonly used software for drug design, along with their descriptions.
Table 1.4 The list of softwares used in the steps of drug designing is represented in tabular form.
Sr. no.
Software name
Description
1
DDDPlus
It helps in dissolution and disintegration study.
2
GastroPlus
Correlation for various formulations in in vivo and in vitro.
3
MapCheck
It helps in comparing dose or fluency measurement.
4
AutoDock
They help in evaluating the ligand-protein interaction.
5
Schrodinger
They perform ligand-receptor docking.
6
GOLD
They perform protein-ligand docking.
7
BioSuite
It performs genome analyzing and sequence analyzing.
8
Maestro
It involves molecular modeling analysis.
9
ArgusLab
They perform Molecular docking calculations and provides molecular modeling package.
10
GRAMM
Protein-protein docking and protein-ligand docking.
11
SYBYL-X Suite
It involves molecular modeling and ligand-based designing.
12
Sanjeevini
It can predict protein-ligand binding affinity.
13
PASS
It can create and analysis of SAR models.
14
AMIDE (A Medical Image Data Examiner)
They provide medical image analysis in molecular imaging.
15
Discovery Studio
®
Visualizer
It helps in viewing and analyzing protein data.
16
Imaging Software SCGE-Pro
They perform cytogenetic and DNA damage analysis.
17
Xenogen Living Image Software
It involves in vivo imaging display and analysis.
18
GeneSpring
It can identify variation across set of sample and for correction method in samples.
19
QSARPro
It involves protein-protein interaction study.
20
REST 2009 Software
They perform analysis of gene expression data.
21
EthoWatcher
It performs behavior analysis.
22
MARS (Multimodal Animal Rotation System)
It can perform animal activity tracking, enzyme activity, and nanoparticle tracking and delivery study.
1.4 Applications
Bioinformatics plays an important role in defining and classifying the nucleotide compositions of human genome sequence. This field helps in identifying and analyzing a large number of biological drug targets, thereby greatly increasing the possibility of potential drugs. This approach provides strategies and algorithm to predict new drug targets and also stores and control available drug target information. The annual expenditure of developing a new drug has been reduced due to the application of bioinformatics in drug discovery. They play a major role in determining the variation of species on the basis of similarity or dissimilarity of gene structure or amino acid sequence in protein. The level of sequence similarity can also be determined using bioinformatics analysis tools. Bioinformatics techniques are mainly applied in two different phases of drug discovery. First is extracting interesting information. Second is finding important genes and proteins, thereby speeding the process of drug discovery.
Genome sequencing of various organisms has become possible due to bioinformatics. There are almost hundred organisms whose genome has been mapped so far using bioinformatics tools [48]. The databases of these organisms are increasing day by day as every day a new information about any organism. Bioinformatics and genomics have been adopted by pharmaceutical industries for drug targets and drug discovery. The possibility of designing and developing drugs is due to the understanding of molecular biology with the help of bioinformatics tools. In the recent years, bioinformatics has made it easier for the researchers to easily target the molecules in the in vitro environment. Screening of newly developed compounds can now be done against the molecules of the proteins or genetically modified cells thereby giving efficient results. This way of drug development has made the process of identification of the disease easier in an organism.
Several studies have been carried which highlights the role and application of bioinformatics tools in drug designing. Some of these are as follows.
In 2013, an investigation was done to evaluate the activity of anti-dengue in compounds that are isolated from eight Carica papaya [49]. It was investigated against NS2B-NS3 protease of dengue 2 virus (DENV-2). In this study, admetSAR was used to screen the ADMET properties of the compounds extracted from Carica papaya [49].
In 2014, molecular dynamics study was carried out to evaluate the constancy of the complexes of protein-ligand and individual protein. The study was also carried out for double mutant (toho-1-R274N/R276N in Escherichia coli) and triple mutant (toho-1-E166A/R274N/R276N in Escherichia coli) systems of class A β-lactamases and also for point mutant (SHV-E166A in Klebsiella pneumoniae) [50].
In 2014, to reveal the potential anti-mycobacterium activity of pyrrole hydrazine derivatives which acts on enoyl-acyl carrier protein reductase was carried out using CoMFA and CoMSIA analysis [51].
In 2016, Malathi and Ramaiah performed structure-based virtual screening to analyze the inhibtors that are potential for OXA-10 ESBL expressing P. aeruginosa. This was done in opposition to millions of compounds that are present in ZINC database. For this study, Molinspiration tool was used. The tool was used to filter the imipenem analogs that is based on the Lipinski’s rule of five [52].
In 2016, identification of novel inhibitors for Penicillin binding protein 2a (PBP2a) of ceftaroline-resistant methicillin-resistant Staphylococcus aureus (MRSA) was used for virtual screening using Dock blaster server [53].
Acinetobacter baumannii (A. baumannii), a Gram negative, coccobacilli which is associated with nosocomial infections has developed resistance to all known classes of antibiotics. The infections have been treated with the carbapenem group of antibiotics like imipenem and meropenem. According to the reports, A. baumannii has obtained resistance to imipenem due to the secretion of carbapenem hydrolysing class D betalactamases (CHDLs). A study was carried out in 2016, to search for the possible mechanism of imipenem resistance in OXA-143 and OXA-231 (D224A) CHDLs expressing A. baumannii. This was performed using molecular docking and dynamics simulation studies.
Malathi et al., in 2016, carried out a study to find the possible mechanism of imipenem resistance in OXA-143 and OXA-231 (D224A) CHDLs expressing A. baumannii by implementing molecular docking and dynamics study. Their study revealed that OXA-143 CHDL-imipenem complex has better binding affinity than OXA-231 (D224A) CHDL-imipenem complex. Their results also indicated that binding affinity of OXA-143 with imipenem was strong when compared with OXA-243. Hence, they could conclude that this mechanism might be the probable reason for imipenem resistance in OXA-143 expressing A. baumannii strains [54].
In 2017, Suganya et al. investigated the anti-dyslipidemic property. This property was studied on plant compounds against HMG-CoA reductase. Molecular dynamic study was performed to analyze the stability of the rutin-HMG CoA complex. It was observed that the resulted plots reveal the constancy of the Epicatechin-HMG CoA complex instead of the free HMG CoA [55].
In 2018, Kist et al. have performed a search which was ligand-based Pharmacophore in order to investigate non-ATP competitive inhibitors for mammalian or mechanistic target of rapamycin (mTOR).
The spatial arrangement of protein model and ligand model was generated in order to design a model by ZINCPharmer platform.
This was done with the help of hydrophobic interactions of residues like C19, C5, C21, C45, C43, and C49 of rapamycin. Thus, it results in the generation of eight new inhibitors with better activity [56].
1.5 Conclusion
The field of bioinformatics plays a pivotal role in designing novel synthetic drugs. Bioinformatics is providing a huge support in order to overcome the cost and time in drug discovery and development. A broad range of softwares and databases related to drug can be obtained using bioinformatics, thereby helping in drug designing purposes. For drug designing, the tools which were discussed in this chapter are playing a major role in the enhancement of modified drugs development. With the use of bioinformatics tools in designing drugs, promising drug candidates can be constructed thereby providing a hope for betterment in drug discovery area.
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