Frontiers in Computational Chemistry: Volume 3 E-Book

• Herausgeber: Bentham Science Publishers
• Kategorie: Wissenschaft und neue Technologien
• Serie: Frontiers in Computational Chemistry
• Sprache: Englisch

Seitenzahl: 620

Veröffentlichungsjahr: 2017

Beschreibung

Frontiers in Computational Chemistry presents contemporary research on molecular modeling techniques used in drug discovery and the drug development process: computer aided molecular design, drug discovery and development, lead generation, lead optimization, database management, computer and molecular graphics, and the development of new computational methods or efficient algorithms for the simulation of chemical phenomena including analyses of biological activity.
The third volume of this series features four chapters covering in silico approaches to computer aided drug design, modeling of platinum and adjuvant anti-cancer drugs, allostery in proteins and studies on the theory of chemical space in electron systems.

Leseprobe

Table of Contents

Welcome

Table of Contents

Title Page

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PREFACE

List of Contributors

In Silico Approaches for Drug Discovery and Development

Abstract

1. INTRODUCTION

2. COMPUTER AIDED DRUG DESIGN STRATEGIES

2.1. Ligand Based Drug Discovery

2.2. Structure Based Drug Discovery

3. TOPICS IN CADD

3.1. Databases

3.1.1. Small Molecule Databases

3.1.2. Preparation of Ligand Libraries

3.1.3. Virtual Combinatorial libraries

3.1.4. Representation of Small Molecules

3.1.5. Molecular Descriptors/Features

3.2. Target Databases for Computer-Aided Drug Design

3.3. Similarity Searches

3.4. Quantitative Structure-Activity Relationship (QSAR)

3.4.1. Classical QSAR (1D/2D)

3.4.2. 3D-QSAR

3.4.3. Multidimensional QSAR

3.5. Pharmacophores

3.6. Comparative Modeling

3.7. Binding Site Detection and Characterization

3.8. Protein – Ligand Docking

3.8.1. Molecular Docking Methods

3.8.1.1. Search Methods

3.8.1.2. Scoring Functions

3.8.2. Protein Flexibility in Docking

4. MOLECULAR DYNAMICS SIMULATIONS IN DRUG DISCOVERY AND DESIGN

4.1. MD Simulations

4.2. Refinement of Homology Models

4.3. Combining Docking and MD Simulations

4.3.1. Receptor Conformation (Preparation of Receptor Structure)

4.3.2. Ensemble Generation

4.3.3. Refinement of Docked Complexes

4.4. Free Energy Calculations

5. ASSESSMENT OF ABSORPTION DISTRIBUTION METABOLISM EXCRETION AND TOXICITY PROPERTIES

5.1. Drug Attrition in the Drug Development Phase

5.2. Compound Library Filters

5.3. Drug Metabolism: Cytochrome P450

5.4. Prediction of Human Ether-A-Go-Go Related Gene Binding

6. PROTEIN – PROTEIN INTERACTIONS AS DRUG TRAGETS

6.1. Peptide and Peptidomimetics as ppi Inhibitors

CONFLICT OF INTEREST

ACKNOWLEDGEMENTS

REFERENCES

Computational Chemistry Assisted Design and Screening of Ligand-Solvent Systems for Metal Ion Separation

Abstract

1. INTRODUCTION

2. COMPUTATIONAL METHODOLOGY

2.1. Moller-Plesset Perturbation Theory

2.2. Couple Cluster Method

2.3. Density Functional Theory (DFT)

2.4. Local Density Approximation

2.5. Generalized Gradient Approximation

2.6. Conductor Like Screening Model (COSMO)

2.7. Basis Set Superposition Error (BSSE)

2.8. Present Approach of Design and Evaluation

2.8.1. Evaluation of Structural Parameters

2.8.2. Evaluation of Interaction Parameters

2.8.3. Evaluation of Thermodynamic Parameters

2.8.4. Calculation of Separation Parameters

3. STRUCTURES AND STRCTURAL PARAMETERS

3.1. Microsolvation of Metal Ions

3.2. Coordination Number and Radial Distribution Function

3.3. Macrocyclic Crown Ethers

3.4. Cavity Size of the Host Crown Ethers

3.5. Tuned Extended Crown Ethers

3.6. Conformation

3.7. Donors

3.8. Calix-Crown Ethers

3.9. Organophosphorus Ligands

3.10. Diglycolamide Ligands

3.11. Carbon Nanotube Functionalized Diglycolamic Acids

3.12. Ionic Liquids

4. INTERACTION PARAMETERS -BINDING ENEGY

4.1. Cavity Dependence

4.2. Conformer Dependence

4.3. Donor Atom Dependence

4.4. Binding Interaction towards Calix-Crown Ethers

4.5. Binding Interaction with Organophosphorous Ligands

4.6. Binding Interaction with Diglycolamide Ligands

4.7. Binding Interaction with CNT-DGA

5. THEMODYNAMIC PARAMETERS - ENTHALPY, ENTROPYAND FREE ENERGY

5.1. Free Energy of Extraction using Thermodynamical Cycle for Cs+

5.2. Free Energy of Extraction with TMDGA

5.3. Free Energy of Extraction using Thermodynamical Cycle with TMDGA

5.4. Free Energy of Extraction with CNT-DGA

5.5. Free Energy of Extraction using Thermodynamical Cycle

6. SEPARATION PARAMETERS -PARTITION COEFFICIENTS

6.1. Dual Mode of Extraction for Cs+ and Na+ Ions in Ionic Liquids

7. STRUCTURAL AND DYNAMICAL PROPERTIES AT LIQUID-LIQUID INTERFACE

7.1. Simulation Methodology

7.2. Hydration Structure of DB18C6/Li+ Complex in Water

7.3. Dynamic Behaviour of DB18C6/Li+ Complex in Water

7.4. Effect of Solvents on the Cation Shielding from Solvent and Relative Stabilities

7.5. Dynamics of Li+ and DB18C6 at Interface

CONCLUDING REMARKS

CONFLICT OF INTEREST

ACKNOWLEDGEMENTS

References

Molecular Mechanisms of Cellular Transport, Resistance and Cytotoxic Side Effects of Platinum and Adjuvant Anti-cancer Drugs – A Molecular Orbital Study

Abstract

OBJECTIVES

1. Introduction

1.1. Cytotoxic Side Effects

1.1.1. Factors that Determine Cytotoxic Side Effects

1.2. Resistance to Pt Drugs

1.3. Reversal of Resistance to Pt Drugs

1.4. Changes to Cell Membranes as a Basis for Reduced Accumulation of Pt in Resistant Cells

1.5. Combinatorial Chemotherapeutic Regimes

2. RESULTS AND DISCUSSION

2.1. Cytotoxic Side Effects

2.2. Resistance to Pt Drugs

2.3. Reversal of Resistance to Pt Drugs

2.4. Combinatorial Regimes and Adjuvant Drugs used with Pt Drugs

3. Computational molecular orbital methods

Conclusion

CONFLICT OF INTEREST

ACKNOWLEDGEMENTS

References

Elucidating Allosteric Communications in Proteins via Computational Methods

Abstract

1. INTRODUCTION

2. INDUCED FIT vs POPULATION SHIFT PARADIGMS

3. WHAT IS ALLOSTERICITY?

4. ELUCIDATING ALLOSTERICITY: COLLECTIVE MOTIONS vs. ENERGY TRANSPORT CHANNELS

4.1. Graph Theory

4.2. Elastic Network Models

4.3. Equilibrium and Non-equilibrium Simulations

4.4. MC/MD Perturbation Methods

4.5. Integration of Graph Theory Techniques with Simulation Based Methods

4.6. Statistical Coupling Analysis

CONCLUSION

CONFLICT OF INTEREST

ACKNOWLEDGEMENTS

ABBREVIATIONS

REFERENCES

Information-Theoretic Representation of the Chemical Space of Many Electron Systems

Abstract

1. Introduction

2. Information-theoretical measures

3. Information-theoretic chemical space for many electron systems

4. Chemical Space of selected Bacteriostatic Sulfonamides

5. Predominant Information Quality Scheme for the Essential Amino Acids

Conclusion

Disclosure

CONFLICT OF INTEREST

ACKNOWLEDGEMENTS

References

Frontiers in Computational

Chemistry

(Volume 3)

Edited by

Dr. Zaheer ul Haq

Panjwani Center for Molecular Medicine & Drug Research

International Center for Chemical & Biological Sciences

University of Karachi

Pakistan

&

Dr. Jeffry D. Madura

Department of Chemistry & Biochemistry

Center for Computational Sciences Duquesne University

Pittsburgh

USA

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PREFACE

The branch of chemistry that uses computers to study chemical questions is known as Computational Chemistry which is a very diverse field spanning from the development and application of linear free energy relationships (e.g. QSAR, QSPR), to electronic structure calculations, molecular dynamics simulations, and to solving coupled differential equations (e.g. drug metabolism). The focus of Frontiers in Computational Chemistry is to present material for the application of computational techniques used in biological processes. Topics falling under this umbrella include computer aided molecular design, drug discovery and development, lead generation, lead optimization, database management, computer and molecular graphics, and the development of new computational methods or efficient algorithms for the simulation of chemical phenomena including the analysis of biological activity. In this third volume, we have collected five different perspectives on the application of computational methods towards drug design.

Chapter 1 “In Silico Approaches for Drug Discovery and Development” reviews the main computational tools used in the drug discovery process. Joseph, et al. also presented the application of physics-based methods that are currently being developed and applied to the drug discovery process.

The removal of toxic metal ions from nuclear and chemical waste streams is an imperative and demanding problem. In Chapter 2 “Computational Chemistry Assisted Design and Screening of Ligand-Solvent Systems for Metal Ion Separation” Ali et al. review electronic structure methods to aid the design and development of new ligands that can be used to extract metal ions from the environment. The goal is to use electronic structure methods to identify a suitable ligand anchored on a solid matrix that can be used in a complex separation process.

One challenge in the biochemical field is understanding the side effects of anti-cancer drugs containing platinum. The authors of Chapter 3 “Molecular Mechanisms of Cellular Transport, Resistance and Cytotoxic Side Effects of Platinum and Adjuvant Anti-cancer Drugs ― A Molecular Orbital Study” present a review of the application of electronic structure methods to understand the side effects, acquired resistance, and combination of platinum drugs with adjuvant drugs in treating cancer.

In Chapter 4 “Elucidating Allosteric Communications in Proteins Via Computational Methods”, the authors present a review of the application of different normal mode analyses based on molecular dynamics methods to understanding allosteric communication in proteins. Alakent and Ince also present the application of graph theory, perturbation methods, and statistical methods to investigate allosteric mechanisms.

The authors of Chapter 5 “Information-theoretic chemical space for many electron systems: from atoms to biological and pharmacological molecules” review the utility of an information-theoretic three-dimensional (IT-3D) space to unveil the unique physical, chemical and biological aspects of a great diversity of many electron systems. These multiple electrons systems range from simple atomic systems to more complex systems such as amino acids. Esquivel et al. claim that “All chemical families recognized by the existing energy-based classifications are embraced by this entropic scheme”.

Zaheer Ul Haq Panjwani Center for Molecular Medicine & Drug Research International Center for Chemical & Biological Sciences University of Karachi Pakistan&Jeffry D. Madura Department of Chemistry & Biochemistry Center for Computational Sciences Duquesne University Pittsburgh USA

List of Contributors

Anil BodaChemical Engineering Division, Bhabha Atomic Research Centre, Mumbai, IndiaAshish Kumar Singha DebChemical Engineering Division, Bhabha Atomic Research Centre, Mumbai, IndiaBurak AlakentDepartment of Chemical Engineering, Bogazici University, Istanbul, TurkeyClifford W. FongEigenenergy, Adelaide, South Australia, AustraliaC. Soriano-CorreaQu´ımica Computacional, FES-Zaragoza, Universidad Nacional Aut´onoma de M´exico, 09230-Iztapalapa, M´exico, D.F, MexicoJ.C. AnguloInstituto Carlos I de F´ısica Te´orica y Computacional, Universidad de Granada, 18071-Granada, Spain Departamento de F´ısica At´omica, Molecular y Nuclear, Universidad de Granada, 18071-Granada, SpainJ.S. DehesaInstituto Carlos I de F´ısica Te´orica y Computacional, Universidad de Granada, 18071-Granada, Spain Departamento de F´ısica At´omica, Molecular y Nuclear, Universidad de Granada, 18071-Granada, SpainKalsankaTrivikram ShenoyChemical Engineering Division, Bhabha Atomic Research Centre, Mumbai, IndiaM. Molina-Esp´ırituDepartamento de Qu´ımica, Universidad Aut´onoma Metropolitana, 09340-M´exico, D.F., M´exicoPooja SahuChemical Engineering Division, Bhabha Atomic Research Centre, Mumbai, IndiaR.O. EsquivelDepartamento de Qu´ımica, Universidad Aut´onoma Metropolitana, 09340-M´exico, D.F., M´exico Instituto Carlos I de F´ısica Te´orica y Computacional, Universidad de Granada, 18071-Granada, SpainS. L´opez-RosaInstituto Carlos I de F´ısica Te´orica y Computacional, Universidad de Granada, 18071-Granada, Spain Departamento de F´ısica Aplicada II, Universidad de Sevilla, 41012-Sevilla, SpainSrinivasaraghavan KannanBioinformatics Institute, A STAR, Singapore 138671,Sk. Musharaf AliChemical Engineering Division, Bhabha Atomic Research Centre, Mumbai, IndiaThomas Leonard JosephBioinformatics Institute, A STAR, Singapore 138671,Vasanthanathan PoongavanamDepartment of Physics, Chemistry and Pharmacy, University of Southern Denmark, Campusvej 55, DK-5230, Odense M, DenmarkVigneshwaran NamasivayamDepartment of Life Science Informatics, B-IT, Rheinische Friedrich-Wilhelms-Universitaet, Dahlmannstr, 2, 53113 Bonn, GermanyZ. Nevin Gerek InceInstitute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, USA

In Silico Approaches for Drug Discovery and Development

Thomas Leonard Joseph1,Vigneshwaran Namasivayam2,Vasanthanathan Poongavanam3,Srinivasaraghavan Kannan1,*

1 Bioinformatics Institute, A*STAR, Singapore 138671, Singapore

2 Department of Life Science Informatics, B-IT, Rheinische Friedrich-Wilhelms-Universitaet, Dahlmannstr 53113 Bonn, Germany

3 Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Campusvej 55, DK-5230, Odense M, Denmark

Abstract

Discovery of new therapeutics is a very challenging, expensive and time-consuming process. With the number of approved drugs declining steadily combined with increasing costs, a rational approach is needed to facilitate, expedite and streamline the drug discovery process. In this regard computational methods are playing increasingly important roles, largely assisted by developments in algorithms and greatly increased computer power. With in silico methods playing key roles in the discovery of growing numbers of marketed drugs, nowadays use of computational tools has become an integral part of most drug discovery programs. Computational tools can be applied at different stages: from target selection through identification of hits to optimization. In this chapter we aim to provide an overview of major tools that have been developed and are routinely being used in the search of novel drug candidates. In addition, we present recent advances, especially in the application of physics-based simulation methodologies, in the drug discovery process for the development of improved therapeutics.

*Corresponding author Srinivasaraghavan Kannan:Bioinformatics Institute, A*STAR, Singapore 138671; E-mail: [email protected]