Brucella Melitensis: Identification and Characterization of Potential Drug Targets

Brucella Miletensis: Identification and Characterization of Potential Drug Targets presents a systematic approach to identifying and characterizing drug targets using bioinformatics. The book shows the potential of bioinformatic tools in the identification of virulence targets in pathogenic bacteria and viruses, in general, and in B. militensis 16M in particular. Chapters identify putative genes as potential drug targets, employ a subtractive genomic approach, consider the virulent genes of this bacteria that negatively affects humans, list twelve potential virulence genes as drug targets, and consider the screening of potential drugs against the bacteria’s virulence genes through molecular modeling, computational screening, drug discovery and molecular docking studies.

In addition, the book demonstrates in silico approaches that offer insights into the identification of drug targets in B.melitensis 16M. The title employs a step-by-step approach to understanding drug targets by identifying and characterizing vaccine targets for Brucella melitensis, in silico screening, and the identification of novel drug targets from the total Brucella melitensis proteome. Other sections cover computational modeling and evaluation of the best potential drug targets through comparative modeling, molecular docking, and dynamics simulations of novel drug targets and in silico validation and ADMET analysis for best lead molecules.

• Covers the identification and characterization of vaccine targets for Brucella melitensis
• Presents in silico screening and the identification of novel drug targets
• Gives computational modeling and evaluations for potential drug targets
• Offers molecular docking and dynamics simulations for novel drug targets
• Details in silico validation and ADMET analysis for best lead molecules

• Language: English
• Publisher: Academic Press
• Release date: Mar 3, 2021
• ISBN: 9780323899970

Book preview

Brucella Melitensis

Identification and Characterization of Potential Drug Targets

Editors

Jangampalli Adi Pradeepkiran

Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA

S.B. Sainath

Department of Biotechnology, Vikrama Simhapuri University, Nellore, Andhra Pradesh, India

Table of Contents

Cover image

Title page

Copyright

Contributors

Preface

Overview of the book

Chapter 1. Introduction to brucellosis

Abbreviations

1.1. Brucellosis

1.2. Bioterrorism

1.3. Microbiology of Brucella

1.4. Summary

Chapter 2. Identification and characterization of vaccine targets for Brucella melitensis through in silico approaches

Abbreviations

2.1. Introduction to subtractive genomics

2.2. Methodology to identify vaccine targets

2.3. Identification and characterization of drug targets

2.4. Summary

Chapter 3. Computational modeling and evaluation of best potential drug targets through comparative modeling

Abbreviations

3.1. Introduction to protein modeling

3.2. Methodology for protein modeling

3.3. Identification of proteins as drug targets in B. melitensis and their modeling approaches

3.4. Discussion

3.5. Summary

Chapter 4. Molecular docking and dynamics simulations of novel drug targets

Abbreviations

4.1. Introduction to molecular docking

4.2. Methodology for molecular docking and dynamic simulations

4.3. Molecular docking and simulations studies of identified drug targets against selected ligands

4.4. Summary

Chapter 5. In silico validation and ADMET analysis for the best lead molecules

Abbreviations

5.1. Introduction to ADMET analysis

5.2. Methodology for ADMET analysis

5.3. ADMET analysis of best lead molecules against identified drug targets in B. melitensis 16M

5.4. Summary

Chapter 6. Summary and conclusions

6.1. Major highlights of this book

Index

Copyright

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ISBN: 978-0-323-85681-2

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Contributors

Praveen K. Balne,     One-Health Vision Research Program, Departments of Veterinary Medicine & Surgery and Biomedical Sciences, College of Veterinary Medicine, University of Missouri, Columbia, MO, USA

M. Bhaskar,     Department of Zoology, Sri Venkateswara University, Tirupati, Andhra Pradesh, India

Kanipakam Hema,     DBT-BioCARe Translational Bioinformatics Group, International Center for Genetic Engineering and Biotechnology, New Delhi, India

P. Gopi Krishna,     Department of Zoology, VSUPG Centre Kavali, Nellore, Andhra Pradesh, India

Manne Munikumar,     Nutrition Information, Communication & Health Education (Niche), ICMR-National Institute of Nutrition, Hyderabad, Telangana, India

Pradeep Natarajan,     BIF Center, Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela, Odisha, India

Jangampalli Adi Pradeepkiran

Department of Internal Medicine, Texas Tech University of Health Science Centre, Lubbock, TX, United States

Department of Zoology, Sri Venkateswara University, Tirupati, Andhra Pradesh, India

M. Hanuma Reddy,     Department of Marine Biology, Vikrama Simhapuri University, Nellore, Andhra Pradesh, India

S.B. Sainath,     Department of Biotechnology, Vikrama Simhapuri University, Nellore, Andhra Pradesh, India

K.V.L. Shrikanya,     Department of Biotechnology, Vikrama Simhapuri University, Nellore, Andhra Pradesh, India

Ch Venkatrayulu,     Department of Marine Biology, Vikrama Simhapuri University, Nellore, Andhra Pradesh, India

Preface

Nowadays, the application of computational approaches in the area of biomedicine is gaining importance globally. One of the emerging areas of bioinformatics is the identification of drug targets, which in turn opened a new era in the area of Biomedicine. Further, the economic constraints associated with in vivo experiments forced many researchers to search alternative approaches to identify drug targets and to design drugs against those identified in harmful pathogens. One of the approaches is to apply suitable bioinformatic tools to identify drug targets and to design drugs accordingly. Therefore, this book provides an overview of important subject areas in the identification of drug targets using computational approaches. The basic idea in writing this book is to provide a comprehensive data in the identification of drug targets and to design drugs against such identified targets in harmful pathogens, Brucella.

This book further elaborates the application of in silico approaches to identify and characterize drug targets in Brucella melitensis 16M. Thus, one would use this book to strengthen his or her knowledge in interdisciplinary area of informatics (computational approaches) and biology/medicine. This book is focused on practical applications of bioinformatics by justifying theoretical and research outcome. This book covers major aspects of potential drug targets in B. melitensis 16M by applying fundamental (modeling to evaluation of drug targets) to advanced (dynamic simulations to ADMET analysis) approaches of bioinformatics. This book not only describes the identification of drug targets but also provides comprehensive aspects related to screening of potential drugs (small molecules) against B. melitensis 16M virulence genes through molecular modeling, computational screening, drug discovery, and molecular docking studies.

Another major goal of this book is to present a broad appeal to biologists at various levels from Bachelors to top-level research scientists working in the area of drug discovery. At Bachelors and Masters level, the contents in the book will be helpful to understand the potential of bioinformatic tools in the identification of virulence targets in pathogenic bacteria and viruses in general and B. melitensis 16M in particular. At research level, this book presents a systematic approach to identify and characterize the drug targets. Understanding the stepwise approaches covered in this book toward the identification of drug targets, researchers would design and perform similar type of approaches to identify drug targets in various pathogenic bacteria.

It is certain that the target audience (Bachelors, Masters, and Research Scholars in Life Sciences, researchers of both academic and industrial sectors) needs to understand the topic at two levels depthwise and breadthwise encompassing basic concepts and theoretical and practical knowledge about identification of potential drug targets using in silico analysis. As this book covers holistic approach with respect to identification of drug targets to screening of drugs against the identified potential virulence targets, it is more attractive to postgraduation students and research scholars in the area of biology, medicine, biotechnology, and bioinformatics. This book is also attractive to researchers in various medical and industrial sectors, especially vaccine development companies. The chapters included in this book explain many concepts and presents qualitative and quantitative aspects to biotechnology and bioinformatics. The bioinformatic pipelines used for the identification of drug targets are represented in the form of figures and are included where necessary for clarity. Moreover, the style of figures included in every chapter has been kept deliberately simple so that the readers can remember and reproduce those figures at ease. At the end of each chapter, suitable references mostly from recent reviews and a few classic research papers are included to gain further insights into the concerned topic.

We definitely hope that the readers find the contents of this book helpful to pave a way for students at the level of graduate, postgraduate, and research. The computational tools mentioned in this book might be helpful for the researchers to identify drug targets and design appropriate drugs in silico before addressing the same in vivo.

Organization of the book

The stepwise approaches covered in this book are as follows:

1. Identification and characterization of vaccine targets for B. melitensis

2. In silico screening and identification of novel drug targets from total B. melitensis 16 proteome

3. Computational modeling and evaluation of best potential drug targets through comparative modeling

4. Molecular docking and dynamics simulations of novel drug targets

5. In silico validation and ADMET analysis for best lead molecules

Overview of the book

The role of computational strategies, i.e., blending of chemoinformatics and biology in drug discovery, is well appreciated; especially, this is true in case of handling harmful pathogens. Nowadays, the identification of potential drug targets is one of the critical factors for effective therapy against pathogen-mediated diseases including the biowar pathogens like Anthrax, Clostridium, Brucella, etc. To accomplish this task, it is important to consider two aspects: 1) health risks associated with direct handling of biowar pathogens and 2) economic aspects towards the identification of drug targets using in vivo experiments. Therefore, to overcome these aspects, alternative strategies are immediately needed. One of the computational strategies associated with the drug discovery against harmful pathogens is subtractive genomics. This approach depends on two key elements: knowledge about the complete microbial genome sequences of pathogenic bacteria and the human genome. Subtractive genomic approach acts as a platform to effectively screen the human diseases by utilizing complete genome sequences of pathogens for distinct pathological pathways and novel virulence factors.

Brucella melitensis 16M is categorized under biowar pathogen list. It causes a disease known as brucellosis, which severely affects the livestock production and management people who are in close contact with domestic animals. Among the four species, B. melitensis 16M is highly pathogenic to humans. In the present study, 70 drug targets were clustered into 14 metabolic pathways, and these putative genes specific to pathogen and nonhomologous to humans in the genome of B. melitensis 16M were identified using subtractive genomic analysis. The present study pinpoints the utility of the subtractive genomic approach using large genomic databases for in silico systematic drug target identification in the postgenomic era. From the above 70 potential drug targets, 12 proteins lacking the crystal structures create a gap to understand the molecular features and host–pathogen interactions of B. melitensis 16M. Homology modeling of 12 identified proteins was structured based on the available templates through Modelar 9.2. The selected proteins were transcription termination factor Rho, malate synthase G, isocitrate lyase, carboxynorspermidine decarboxylase, urease accessory protein UReG, nicotinate phosphoribosyltransferase, 3-phosphoshikimate 1-carboxyvinyltransferase, 2,3,4,5-tetrahydropyridine-2-carboxylate N-succinyltransferase, iron (III)-binding periplasmic protein precursor, nitrate reductase beta chain, nitric oxide reductase subunit B, and hypothetical proteins. The modeled proteins were subjected to structure validation using Ramachandran plot and other tools such as SAVES, Verify 3D, and ProSA analysis. After validation of structures, another computational strategy using ligand-based drug discovery was performed to predict potential drug targets out of the identified 12 proteins. Accordingly, nine potential drug targets were selected for docking studies. Remaining three proteins, which include isocitrate lyase, iron (III)-binding periplasmic protein precursor, and nitrate reductase beta chain, lack the ligand molecule within the three-dimensional structures of protein; hence, we omitted docking studies for those proteins. For the nine qualified proteins, malate synthase G, carboxynorspermidine decarboxylase, urease accessory protein UReG, nicotinate phosphoribosyltransferase, 3-phosphoshikimate 1-carboxyvinyltransferase, 2,3,4,5-tetrahydropyridine-2-carboxylate N-succinyltransferase, nitric oxide reductase subunit B, transcription termination factor Rho, and hypothetical protein, virtual screens using the ZINC ligand database with 90% cutoff value give reliable ligand compounds. Virtual screening approach identified the 27 best potential lead molecules for potential drug targets, and the 27 lead molecules—ZINC ID: 79644962, 01678475, 79644960, 78283617, 06484028, 28570368, 04556820, 49803083, 72319544, 14988285, 26249851, 34510346, 59192952, 69482477, 78754973, 28710264, 28710270, 28710280, 04208896, 34496108, 59436378, 24934545, 36093420, 49020554, 00392000, 04578892, and 00967755—were tested through in silico PreADMET and molinspiration analysis and found that these molecules possesses the good druglike characteristics and desired physicochemical properties (molecular weight, logP, no. of rotatable bonds) and bioactivities, etc., for each ligand molecule. Molecular docking studies were performed using Autodock Vina on PyRx platform.

Chapter 1: Introduction to brucellosis

Jangampalli Adi Pradeepkiran a , b , M. Bhaskar b , K.V.L. Shrikanya c , P. Gopi Krishna d , M. Hanuma Reddy e , Ch Venkatrayulu e , and S.B. Sainath c       a Department of Internal Medicine, Texas Tech University of Health Science Centre, Lubbock, TX, United States      b Department of Zoology, Sri Venkateswara University, Tirupati, Andhra Pradesh, India      c Department of Biotechnology, Vikrama Simhapuri University, Nellore, Andhra Pradesh, India      d Department of Zoology, VSUPG Centre Kavali, Nellore, Andhra Pradesh, India      e Department of Marine Biology, Vikrama Simhapuri University, Nellore, Andhra Pradesh, India

Abstract

David Bruce identified the bacterium Brucella in the year 1887 and in the year 1918, Alice Evans, an American Microbiologist suggested the