Drug Design Strategies: Computational Techniques and Applications: Volume 20 - Rilegato

Rilegato

ISBN 10: 1849731675 ISBN 13: 9781849731676

Casa editrice: Royal Society of Chemistry, 2012

Riassunto

This book documents the latest research into the theory and application of force-fields, semi-empirical molecular orbital, density functional and ab initio calculations, Quantum Mechanical (QM) based modelling, Atoms in Molecules (AIM) approach, and biomolecular dynamics. It also covers theory and application of 2D cheminformatics, QSAR/QSPR, ADME properties of drugs, drug docking/scoring protocols and approaches, topological methodology, and modelling accurate inhibition constants of enzymes. Finally, the book gives the theory and applications of multiscale modelling of proteins and biomolecular systems. The information need for a book in this area is due to the continuing rapid advance of firstly theoretical approaches, secondly software/hardware and lastly the successful application of the technology and this book fills a gap in the literature. The co-editors have extensive experience of teaching and researching in the field and the book includes contributions from cutting-edge academic and industrial researchers in their respective fields. It is essential reading for medicinal chemists, computational chemists and those in the pharmaceutical industry.

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Informazioni sugli autori

Dr Lee Banting BSc(Hons) PhD MRSC CChem, is at the Centre of Molecular Design, School of Pharmacy and Biomedical Sciences, University of Portsmouth, UK. He is a Senior Lecturer in Medicinal Chemistry and is on the Editorial Board of Natural Product Communications. Prof Dr Tim Clark is Professor in Computational Chemistry, and Head of the Centre of Molecular Design, School of Pharmacy and Biomedical Sciences, University of Portsmouth, UK and Technical Director, Computer-Chemie-Centrum, Universitat Erlangen-Nurnberg, Germany and Editor-in-Chief of the Journal of Molecular Modelling.

Dr Lee Banting BSc(Hons) PhD MRSC CChem, is at the Centre of Molecular Design, School of Pharmacy and Biomedical Sciences, University of Portsmouth, UK. He is a Senior Lecturer in Medicinal Chemistry and is on the Editorial Board of Natural Product Communications. Prof Dr Tim Clark is Professor in Computational Chemistry, and Head of the Centre of Molecular Design, School of Pharmacy and Biomedical Sciences, University of Portsmouth, UK and Technical Director, Computer-Chemie-Centrum, Universitõt Erlangen-N³rnberg, Germany and Editor-in-Chief of the Journal of Molecular Modelling.

Dalla quarta di copertina

Drug Design Strategies: Computational Techniques and Applications documents the latest research into the theory and application of force-fields, semi-empirical molecular orbital, density functional and ab initio calculations, Quantum Mechanical (QM) based modelling, Atoms in Molecules (AIM) approach, and biomolecular dynamics. It also covers theory and application of 2D cheminformatics, QSAR/QSPR, ADME properties of drugs, drug docking/scoring protocols and approaches, topological methodology, and modelling accurate inhibition constants of enzymes. Finally, the book gives the theory and applications of multiscale modelling of proteins and biomolecular systems. The information need for a book in this area is due to the continuing rapid advance of firstly theoretical approaches, secondly software/hardware and lastly the successful application of the technology and this book fills a gap in the literature. The co-editors have extensive experience of teaching and researching in the field and the book includes contributions from cutting-edge academic and industrial researchers in their respective fields. It is essential reading for medicinal chemists, computational chemists and those in the pharmaceutical industry.

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Drug Design Strategies

Computational Techniques and Applications

By Timothy Clark Lee Banting

The Royal Society of Chemistry

Copyright © 2012 Royal Society of Chemistry
All rights reserved.

ISBN: 978-1-84973-167-6

Contents

Chapter 1 Quantum Mechanical/Molecular Mechanical Approaches in Drug Design Tell Tuttle, 1, 
Chapter 2 Transition Metal Systems Robert J. Deeth, 27, 
Chapter 3 Modeling Protein–Protein Interactions by Rigid-body Docking David W. Ritchie, 56, 
Chapter 4 QM Based Modelling Timothy Clark, 87, 
Chapter 5 Semi-empirical Methods: Current Status and Future Directions Alexander A. Voityuk, 107, 
Chapter 6 Quantum Chemical Topology: on Descriptors, Potentials and Fragments Paul L. A. Popelier, 120, 
Chapter 7 Cheminformatics in Diverse Dimensions Thomas Engel, 164, 
Chapter 8 Analysing Molecular Surface Properties David C. Whitley, 184, 
Chapter 9 Atomistic Modelling of Drug Delivery Systems Paul Cox, 210, 
Subject Index, 232, 

CHAPTER 1

Quantum Mechanical/Molecular Mechanical Approaches in Drug Design

TELL TUTTLE

WestCHEM, Department of Pure and Applied Chemistry, University of Strathclyde, 295 Cathedral Street, Glasgow G1 1XL, UK

1.1 Introduction

Quantum mechanical/molecular mechanical (QM/MM) hybrid methods have their origins in the 1970s in the pioneering work of Warshell and Levitt. However, until the last two decades, the uptake of this methodology and its application to solving problems of biochemical interest was essentially non-existent. In the beginning of the 1990s the situation changed dramatically and since this time there has been an explosion in the number of publications that use QM/MM methods to study systems of biochemical interest. Several recent reviews in the literature have provided excellent coverage of the recent developments with a strong focus on the usefulness of QM/MM methods to studying enzyme reactions.

The dramatic uptake of QM/MM methodology has been driven by significant developments in the methodology accompanied by a rapid increase in computer power and decrease in computer cost. Moreover, along with the development in QM/MM methodology, the development of QM methods to deal with larger systems with ever increasing accuracy has allowed these high-level QM methods to be applied to biochemical systems. One can now find examples in the literature where enzyme-catalysed reactions have been modelled computationally to within chemical accuracy (ca. 1 kcal/mol) – a feat unimaginable only a few years previously. Despite these impressive strides in accuracy and efficiency, the application of QM/MM methods to the field of drug design has received far less attention until recently.

Computational drug discovery is a well-established discipline within the field of computational chemistry. Computational chemistry can be applied to the development of new drugs from both the perspective of the small molecule (drug-like compound) and the biological target (receptor). In cases where the drug target is unknown or a structure of the target receptor is unavailable, a quantitative–structure activity relationship (QSAR) can be developed by comparison of several ligands with varying biological responses (activities). One of the oldest and most popular 3D-QSAR methods is a Comparative Molecular Field Analysis (CoMFA), which uses an sp3-hybridized carbon with a charge of +1 to evaluate steric and electrostatic features of the compounds in the training set. By comparing the steric and electrostatic features of the compounds in the training set as a function of their activity a relationship between regions that benefit from having certain properties in relative molecular positions (e.g. decreased steric bulk next to a negatively charged region of the molecule) can be identified. While both ligand-based and receptor-based methods have seen significant successes throughout their application, in the current review the focus will be on receptor (structure) based methods.

Structure-based drug design (SBDD) stems from the lock-and-key principle originally proposed by Emil Fischer over 100 years ago. In the lock-and-key model the ligand and the receptor are both considered as rigid objects and the activity of a ligand results from the complementary matching of the steric and electrostatic features of the ligand (key) into the receptor's binding pocket (lock). This principle was subsequently revised in 1958 by Koshland with the proposal of the induced-fit model whereby both the ligand and the receptor are considered to behave dynamically and as such the 'lock' and the 'key' are both able to adapt to some extent to provide a better match where possible during the binding process. However, modelling the dynamic flexibility of a complete macromolecule (receptor) is still not computationally feasible when one wishes to screen a large database of potential drug-like molecules. As such, within modern docking methods a compromise between the lock-and-key method and the induced fit model is generally applied. These methods treat the ligand as a flexible molecule (or a series of different conformations of the ligand is used) and is docked into the rigid receptor, or semi rigid receptor (where flexibility of the side chains in the binding pocket is allowed). Irrespective of the approach used, the key requirement for SBDD is a reliable structure of the target macromolecule.

The SBDD approach usually involves multiple stages. These can be broadly classified as follows.

1. Generation of poses/conformations of the ligand in the active site – this process determines whether the ligand is able, and if so in which orientation, to physically fit into the active site.

2. Scoring and ranking the individual poses – in this process the static poses generated in the first step are given a score, usually based on electrostatic fit between the ligand and the residues that compose the binding site.

3. Refinement of the docked structures and their subsequent ranking – this third step is the most complicated and various different approaches exist to introduce ligand and binding site flexibility into the refinement of the structure (e.g. molecular dynamics simulations, random search methods, etc.).

Quantum mechanical/molecular mechanical (QM/MM) scoring refinement focuses on the third stage of the SBDD approach – the structural refinement and determination of the final ranking. This refinement process has several problems at a computational level. The methodology employed in the refinement needs to be efficient in order to deal with the large numbers of ligands and poses that need to be refined. Because of this the use of molecular mechanics (i.e. empirical force field based approaches) has been the method of choice. However, empirical methods are inherently limited in their accuracy to the quality/specificity of the parameterization. As a result, in repeated regular structures, such as proteins, which are combinations of only 20 amino acids, force field methods are remarkably successful (e.g. the CHARMM, AMBER, GROMOS, etc. force fields). However, in the case of ligands, which contain an infinite variety of chemical motifs, the parametrization of effective force fields is much more difficult. As such, docking and scoring programs generally rely on universal force fields for modelling both the ligand and the protein, which are much less successful, but offer a common level of accuracy between the protein and the ligand. In principle, the use of quantum mechanical (QM) methods in the place of molecular mechanical methods would solve these problems, offering a common and accurate description of both the ligand and peptide. However, the main drawback for QM methods is that their computational cost prohibits their application to such problems.

The middle ground between purely ab initio and purely empirical methods are the set of methods known as semi-empirical methods. These methods offer the chemical flexibility of the ab initio methods (though not necessarily the accuracy) but are orders of magnitude more efficient. Throughout the literature one is able to find several examples of the applications of semi-empirical methods in the context of QM/MM biochemical modelling. The latest semi-empirical methods (OMx, SCC-DFTB, PDDG/PM3, PM6-DH2, etc.) offer an accurate and efficient description of the structure and energetics of small molecules. Moreover, these methods have been successfully combined with biochemical force fields to provide an accurate description of the interactions in the binding site, between a ligand – treated at the QM level of theory – and the protein – treated at the MM level of theory.

The general strategy in QM/MM scoring refinement is to run a QM/MM minimization of the binding site (ligand + protein) to refine the structure and binding energies of the various poses obtained from the first two stages of the virtual screening. This approach simultaneously provides a more accurate modelling of both the ligand and binding site and, as such, should provide enhanced differentiation between the various ligands, as well as a more detailed understanding of the interactions that are present in the binding site. As such, this method should provide considerable benefit in lead optimization activities.

In Section 1.2 an outline of the main QM/MM methodology employed in QM/MM scoring refinement is provided. In Section 1.3 two illustrative examples of the application of this methodology to challenging scoring problems are provided. In the final section (1.4) the main difficulties associated with QM/MM scoring are addressed and the potential future directions of the field are discussed.

1.2 QM/MM Methods

The basic concept of a QM/MM method is to describe the chemically interesting region of the system (in this case the ligand and potentially the binding pocket) using a QM method and the remainder of the system, the environment, at the MM level of theory. In the most common case the ligand binds to the receptor non-covalently (Figure 1.1) and as such only the ligand is treated at the QM level of theory as the MM description of the protein (receptor) is reliable when appropriate biochemical force fields are chosen. The description of the ligand region using a QM method and the receptor using an MM method is trivial. However, the goal of any QM/MM method is to describe the interaction between the two regions – in other words, how do we best describe the coupling between QM and MM parts of the system? The coupling terms between the ligand and the receptor are exactly the interactions that will dictate the binding ability of the ligand and can be quite strong. Indeed, a balanced treatment of the three components, QM, MM and the coupling terms are equally important for any QM/MM scoring function to be successful.

1.2.1 Additive QM/MM Methods

There exist two families of QM/MM approaches: those based on the additive scheme and those based on the subtractive scheme (see Section 1.2.2). The additive scheme, initially proposed by Warshel and Levitt in 1976, is employed in the majority of QM/MM programs presently in use and generally contains three terms:

[MATHEMATICAL EXPRESSION OMITTED] (1.1)

where EQM/MM(Tot) is the total energy of the full system calculated at the QM/ MM level of theory, EQM(Lig) is the energy of the ligand calculated at the QM level of theory, EMM(Rec) is the energy of the receptor calculated at the MM level of theory and EQM–MM(Lig,Rec) is the explicit coupling term between the ligand and the receptor. As we shall see in the subtractive scheme there is no explicit coupling term present; however, the variation in how the coupling term is defined allows one to further differentiate between various additive QM/MM approaches. These approaches were described in detail by Bakowies and Thiel in 1996, and have recently been reviewed in the literature; as such, an account will not be provided here. The QM/MM approach discussed herein refers to the electrostatic embedding approach, unless otherwise stated. For details of other methods the reader is referred to the literature.

Where there are no bonds that cross the QM/MM boundary, as is the case for non-covalently bound ligands, the coupling term, in the electrostatic embedding approach, has two components:

[MATHEMATICAL EXPRESSION OMITTED] (1.2)

where EvdWQM–MM refers to the van der Waals (vdW) interactions between the ligand atoms and the atoms in the receptor; and EelecQM–MM refers to the electrostatic interactions between the ligand and receptor atoms. If a covalent bond exists between the QM and MM regions a boundary scheme needs to be chosen in order to properly describe the forces acting across the boundary and also to ensure appropriate 'capping' of the QM region. The various schemes for dealing with QM–MM boundary regions have been recently reviewed and the interested reader can find details of these from the reviews.

The vdW interactions are treated at the MM level of theory and as such the atoms in the ligand require vdW parameters to be assigned to them. This assignment is usually done by analogy with 'similar' atom types that exist in the force field. However, alternative, and more rigorous, approaches for assigning vdW parameters to the atoms treated at the QM level of theory have also been presented. The error introduced by the MM treatment of the vdW inter- actions between the ligand and the receptor regions is expected to be minimal due to the short-range nature of these interactions.

Due to its long-range nature, the predominant coupling term between the ligand and the receptor regions is the electrostatic term (EelecQM–MM). Within the electronic embedding approach, the electrostatic coupling between the ligand and receptor descriptions is achieved by performing the QM calculation in the presence of the point charges (taken as the partial charges assigned within the force field) of the receptor. This is achieved by including the partial charges as one-electron terms in the QM Hamiltonian, resulting in the additional term HelecQM–MM), defined in Equation (1.3).

[MATHEMATICAL EXPRESSION OMITTED] (1.3)

The electron positions are given by ri, the point charges, as defined in the force field, for the receptor are denoted qj and are located at Rj and Qk are the charges on the nuclei in the ligand located at Rk. The indices i, j and k correspond to the N electrons, the L point charges in the receptor and M nuclei in the ligand, respectively.

The inclusion of the point charges from the receptor in the Hamiltonian results in the polarization of the electronic structure of the ligand. If the charges in receptor change their positions during a geometry optimization or a molecular dynamics simulation, then the electronic structure is able to respond to these positional changes realistically as the electrostatic coupling is treated at the QM level of theory.

1.2.1.1 Extended Solvation

The long-range nature of the electrostatic interactions imbues them with a special significance in QM/MM calculations. The long-range nature implies that a significantly large proportion of the environment needs to be included in a calculation in order to correctly reproduce the electrostatic effects on the chemically interesting region of the system. However, increasing the size of the environment (receptor and solvent) carries two important pitfalls; (1) the larger the number of degrees of freedom in a system, the greater the chance of falling into localized, disconnected, minima; and (2) the larger the system, the more computationally intensive the calculation becomes. During the initial development of QM/MM methods this second factor was considered essentially irrelevant as the calculation of the QM component was expected to be the rate- limiting step. However, as modern QM methods have increased in both speed and accuracy this is no longer necessarily true, particularly when semi-empirical methods are employed for the QM calculation.

The concern of localized, disconnected minima is particularly troublesome in SBDD where the goal is to determine the relative binding energy of different ligands in the binding site and often the variation can be as little as several kcal/ mol across a series of competitive ligands. Given this small window of energy differences, spurious variations in ranking that can occur from changes in the environment (e.g. alterations in the H-bonding network of the solvent) or in distant parts of the receptor (e.g. rotation of an amino acid on the protein surface causing a stronger or weaker salt-bridge to be formed) are unacceptable. One approach that has been commonly employed to deal with this problem has been to freeze atoms in the solvent and receptor that are farther than a pre-determined distance (typically 10–15 Å) away from the ligand atoms. This procedure reduces the number of degrees of freedom in the QM/MM simulation and treats the 'frozen' part of the system as a fixed external potential that interacts with the mobile inner 'core' of the system. Despite the fixed nature of the outer region, this approach still requires the computation of all interactions between the frozen atoms in the system in addition to the interaction between every atom in the frozen region with every atom within the inner core region. Thus, freezing the more distant components of a system does not alleviate the second pitfall – i.e. the computational cost associated with extended, solvated systems.

(Continues...)

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Dettagli dell'edizione

• Editore: Royal Society of Chemistry
• Data di pubblicazione: 2012
• Lingua: Inglese
• ISBN 10: 1849731675
• ISBN 13: 9781849731676
• Rilegatura: Copertina rigida
• Numero di pagine: 239
• Redattore: Banting Lee, Clark Timothy
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile