Molecular modeling methods. Basic techniques and challenging problems (original) (raw)
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Molecular Dynamics and Related Computational Methods with Applications to Drug Discovery
2016
The main objective of this review chapter is to give the reader a practical toolbox for applications in quantitative biology and computational drug discovery. The computational technique of molecular dynamics is discussed, with special attention to force fields for protein simulations and methods for the calculation of solvation free energies. Additionally, computational methods aimed at characterizing and identifying ligand binding pockets on protein surfaces are discussed. Practical information about available databases and software of use in drug design and discovery is provided.
Molecular Dynamics: Survey of Methods for Simulating the Activity of Proteins
Chemical Reviews, 2006
time-dependent (i.e., kinetic) phenomena. This enables an understanding to be developed of various dynamic aspects of biomolecular structure, recognition, and function. However, when used alone, MD is of limited utility. An MD trajectory (i.e., the progress of simulated structure with respect to time) generally provides data only at the level of atomic positions, velocities, and single-point energies. To obtain the macroscopic properties in which one is usually interested requires the application of statistical mechanics, which connects microscopic simulations and macroscopic observables. Statistical mechanics provides a rigorous framework of mathematical expressions that relate the distributions and motions of atoms and molecules to macroscopic observables such as pressure, heat capacity, and free energies. 17,18 Extraction of these macroscopic observables is therefore possible from the microscopic data, and one can predict, for instance, changes in the binding free energy of a particular drug candidate or the mechanisms and energetic consequences of conformational change in a particular protein. Specific aspects of biomolecular structure, kinetics, and thermodynamics that may be investigated via MD include, for example, macromolecular stability, 19 conformational and allosteric properties, 20 the role of dynamics in enzyme activity, 21,22 molecular recognition and the properties of complexes, 21,23 ion and small molecule transport, 24,25 protein association, 26 protein folding, 27,16 and protein hydration. 28 MD, therefore, provides the opportunity to perform a variety of studies including molecular design (drug design 29 and protein design 30) and structure determination and refinement (Xray,31 NMR, 32 and modeling 33). 3. Molecular Dynamics Methods and Theory Given the structure of a biomolecular system, that is, the relative coordinates of the constituent atoms, there are various computational methods that can be used to investigate and study the dynamics of that system. In the present section, a number of such methods are described and discussed. The majority of important dynamics methodologies are highly dependent upon the availability of a suitable potential-energy function to describe the energy landscape of the system with respect to the aforementioned atomic coordinates. This critical aspect is, therefore, introduced first. 3.1. Potential Functions and the Energy Landscape Choice of an appropriate energy function for describing the intermolecular and intramolecular interactions is critical to a successful (i.e., valid yet tractable) molecular dynamics simulation. In conventional MD simulations, the energy function for nonbonded interactions tends to be a simple pairwise additive function (for computational reasons) of nuclear coordinates only. This use of a single nuclear coordinate to represent atoms is justified in terms of the Born-Oppenheimer approximation. 34 For bonded groups of atoms, that is those that form covalent bonds, bond angles, or dihedral angles, simple two-body, three-body, and four-body terms are used, as described below.
Computations of standard binding free energies with molecular dynamics simulations
The Journal of Physical Chemistry B, 2009
An increasing number of studies have reported computations of the absolute binding free energy of small ligands to proteins using molecular dynamics (MD) simulations with results that are in good agreement with experiments. This encouraging progress suggests that physics-based approaches hold the promise of making important contributions to the process of drug discovery and optimization in the near future. Two types of approaches are principally used to compute binding free energies with MD simulations. The most widely known are based on alchemical free energy methods, in which the interaction of the ligand with its surrounding are progressively switched off. An alternative method is to use a potential of mean force (PMF), in which the ligand is physically separated from the protein receptor. For both of these computational approaches, restraining potentials affecting the translational, rotational and conformational freedom of the ligand and protein may be activated and released during the simulations to aid convergence and improve the sampling. Such restraining potentials add bias to the simulations, but their effects can be rigorously removed to yield a binding free energy that is properly unbiased with respect to the standard state. A review of recent results is presented. Examples of computations with T4lysozyme mutants, FKBP12, SH2 domain, and cytochrome P450 are discussed and compared. Differences in computational methods are discussed and remaining difficulties and challenges are highlighted.
Calculation of ligand binding free energies from molecular dynamics simulations
International Journal of Quantum Chemistry, 1998
A recently developed method for predicting binding affinities in ligand᎐receptor complexes, based on interaction energy averaging and conformational sampling by molecular dynamics simulation, is presented. Polar and nonpolar contributions to the binding free energy are approximated by a linear scaling of the corresponding terms in the average intermolecular interaction energy for the bound and free states of the ligand. While the method originally assumed the validity of electrostatic linear response, we show that incorporation of systematic deviations from linear response derived from free energy perturbation calculations enhances the accuracy of the approach. The method is applied to complexes of wild-type and mutant human dihydrofolate reductases with 2,4-diaminopteridine and 2,4-diaminoquinazoline inhibitors. It is shown that a binding energy accuracy of about 1 kcalrmol is attainable even for multiply ionized compounds, such as methotrexate, for which electrostatic interactions energies are very large.
Current approaches and tools for binding energy prediction in computer- aided drug design
Journal of Chemical and Pharmaceutical Science, 2017
Computer methods can now be used on almost every stage of drug development, but the most common areas of computers application are virtual screening and lead generation/optimization stages. Accurate prediction of the protein-ligand binding affinities is a crucial step in the structure-based drug design approach. Current algorithms and tools for binding energy calculation that are used upon the development of new drug candidates with an emphasize on underlying principles, advantages and limitations, software and general considerations in the selection of specific methods are discussed in the paper. Four main classes of currently available physics-based computer methods (molecular docking, end point / approximate free energy, relative binding free energy, and absolute binding free energy) are reviewed in details. Molecular docking approaches are the method of choice to filter out compounds-nonbinders, but they are not accurate enough to predict binding affinity. The end point methods are more physically rigorous and closer to real free energy calculations, but they are more computationally-intensive and not predictive for some types of proteins. Relative binding free energy methods take into account conformational and entropic contributions, thus offering more accurate predictions. However, they have high computational requirements and can be used only to compare related ligands or receptors. The extremely computational-dependent method of absolute binding free energy calculation is the most powerful approach, giving predictions with good correlations to experimental binding affinities. 1. INTRODUCTION Rational structure-based computer-aided modeling of protein-ligand interactions is now a key component in modern drug discovery paradigm (Charifson,1997). It is widely accepted that computational methods have played an extremely important role in the design process for a growing number of marketed drugs, and in the development of new drug candidates (Mobley & Dill 2010). Moreover, by the aid of computer-aided drug design (CADD), the cost of drug development could be reduced by up to 50% (Tan, 2010). Computer methods can now be used on almost every stage of drug development, but the most common areas of computers application are virtual screening and lead generation/optimization stages (Xiang, 2012). Virtual screening methods, which are designed for searching large libraries of compounds in silico, are widely used within the drug R&D industry and play an indispensable role in modern CADD efforts. These methods usually give a much higher hit rate than the traditional high throughput screening (HTS) (Tang, 2006) and the hits from VS appear more drug-like than the ones from HTS (Shekhar, 2008). At the same time, there is concern that VS methods may have reached a limit in effectiveness (Schneider, 2010). Current virtual screening methods are not very effective in selecting molecules that are actually active against the selected target molecule, although they are undoubtedly useful in eliminating some inactive compounds (Chodera, 2012). Limitations of the VS methods come from a variety of approximations used to allow large numbers of compounds to be screened quickly, often neglecting statistical mechanical and chemical effects for computational efficiency (Chodera, 2012), thus leading to the inaccuracies in the estimation of protein-ligand binding energy. Lead optimization is another crucially important step (Keseru & Makara, 2006) among all of the stages of drug discovery process. From the computational side, the key step in lead optimization process is an accurate prediction of the protein-ligand binding affinities (Jorgensen, 2009), since it is currently accepted that the biological activity of a compound is closely related to the affinity of the compound to macromolecular receptor (Gohlke & Klebe, 2002). Unfortunately, available methods for binding affinity estimation do not possess enough balance between calculation efficiency and reliability, and in a typical situation the most accurate methods are the most time consuming, while the fastest algorithms usually are not very rigorous and accurate (Xiang, 2012). In this review we are going to discuss current approaches and tools for binding energy calculation that are used upon the development of new drug candidates with an emphasize on underlying principles, advantages and limitations, software and general considerations on the selection of specific methods for different users. Computational Approaches to Binding Energy Prediction: Currently available physics-based computer methods can be grouped in at least four different classes. Below are listed from the fastest to slowest, and from the least
A statistical mechanics handbook for protein-ligand binding simulation
Frontiers in Bioscience, 2013
Introduction 3. Basics 3.1. Fixed energy systems 3.2. Microscopic versus thermodynamic description 3.3. Fixed temperature systems 3.3.1. Consequences of the Boltzmann distribution 3.3.2. Time evolution in the canonical ensemble 3.4. From microscopic quantities to macroscopic observables 3.5. Complementary material to section 3 3.5.1. Derivation of Boltzmann distribution for the canonical ensemble 3.5.2. The Fokker-Plank equation 4. Tools and concepts for the description of the binding process 4.1. Role of the free energy and of the internal constraint 4.1.1. Obstacles to absolute free energy calculation 4.1.2. Free energy differences calculation 4.2. Free energy profiles and reaction paths 4.3. The definition of bound and unbound states and the reaction coordinate 4.4. Potential versus free energy surface 4.5. A didactic example 4.6. Volumetric effect on the unbound state 4.7. Complementary material to section 4 4.7.1. The zero temperature limit for free energy 5. Conclusions 6. Acknowledgements 7. References
Molecular dynamics simulations: from structure function relationships to drug discovery
In Silico Pharmacology, 2014
Molecular dynamics (MD) simulation is an emerging in silico technique with potential applications in diverse areas of pharmacology. Over the past three decades MD has evolved as an area of importance for understanding the atomic basis of complex phenomena such as molecular recognition, protein folding, and the transport of ions and small molecules across membranes. The application of MD simulations in isolation and in conjunction with experimental approaches have provided an increased understanding of protein structure-function relationships and demonstrated promise in drug discovery.
Journal of Chemical Information and Modeling, 1997
Structure-based design is the application of ligand-receptor modeling to predict the activity of a series of molecules that bind to a common receptor for which the molecular geometry is available. Successful structurebased design requires an accurate receptor model which can be economically employed in the design calculations. One goal of the work reported here has been to reduce the size of a model structure of a macromolecular receptor to allow multiple ligand-receptor molecular dynamic (MD) simulations to be computationally economical yet still provide meaningful binding thermodynamic data. A scaled-down 10 Å receptor model of the enzyme renin, when subjected to an alternate atomic mass constraint, maintains the structural integrity of the composite parent crystal structure. A second goal of the work has been to develop schemes to explore and characterize the protonation states of receptors and ligand-receptor systems. Application of the charge state characterization schemes to the hydroxyethylene and statine transition state inhibitors of renin in the training set suggests a monoprotonation state of the two active-site aspartate residues, where the lone proton resides on the outer carboxylate oxygen of Asp226 is most likely. For the reduced amide transition state inhibitors an active site consisting of both aspartates in the totally ionized state, and the ligand carrying a net +1.0 charge, is most stable and consistent with experimental data.
Drug design: Insights from atomistic simulations
2009
Computer simulations have become a widely used and powerful tool to study the behaviour of many-particle and many-interaction systems and processes such as nucleic acid dynamics, drug-DNA interactions, enzymatic processes, membrane, antibiotics. The increased reliability of computational techniques has made possible to plane a bottom-up approach in drug design, i.e. designing molecules with improved properties starting from the knowledge of the molecular mechanisms. However, the in silico techniques have to face the fact that the number of degrees of freedom involved in biological systems is very large while the time scale of several biological processes is not accessible to standard simulations. Algorithms and methods have been developed and are still under construction to bridge these gaps. Here we review the activities of our group focussed on the time-scale bottleneck and, in particular, on the use of the metadynamics scheme that allows the investigation of rare events in reasonable computer time without reducing the accuracy of the calculation. In particular, we have devoted particular attention to the characterization at microscopic level of translocation of antibiotics through membrane pores, aiming at the identification of structural and dynamical features helpful for a rational drug design.