Constructing Protein Models for Ligand-Receptor Binding Thermodynamic Simulations: An Application to a Set of Peptidometic Renin Inhibitors (original) (raw)
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Computational Drug Design Accommodating Receptor Flexibility: The Relaxed Complex Scheme
Computational structure-based drug design is a multidisciplinary research area and is still challenging in many respects. While ligand flexibility has been incorporated in many docking schemes, most programs still treat the receptors as rigid objects. 1 In general ligands may bind to conformations of the receptor that occur infrequently in the unliganded receptor; therefore, this rigid body assumption will fail to find correct ligand-receptor binding modes. Inspired by two recent successful experimental methods for the rapid discovery of ligands that bind strongly to a receptor, namely the ''SAR by NMR" method 2 and the "tether" method, 3 here we present a novel computational approach, called the "relaxed-complex" method, which incorporates receptor flexibility.
Journal of Chemical Information and Computer Sciences, 1997
A methodology is presented and applied in which the accurate estimation of ligand-receptor binding thermodynamics is achieved by formulating the calculation as a QSAR problem. When the receptor geometry is known, the free energy force field (FEFF) ligand-receptor binding energy terms can be calculated and used as independent variables in constructing FEFF 3D-QSARs. The FEFF 3D-QSAR analysis of a series of transition state inhibitors of renin was carried out. From a statistical analysis of the free energy contributions to the binding process, FEFF 3D-QSARs were constructed that reveal the change in solvation free energy upon binding and the intramolecular vacuum internal energy of the ligand in the unbound state are the most significant FEFF terms in determining the binding free energy, ∆G. Other terms, such as ligand stretching, bending, and torsion energy changes, the intermolecular van der Waals interaction energy, and change in ligand conformational entropy upon binding, are also found to make significant contributions in some FEFF 3D-QSAR ∆G models and in ∆H and ∆S binding models. Overall, a relatively small number of the thermodynamic contributions to the ligand-receptor binding process dominates the thermodynamics of binding in a given model.
Journal of Computer-aided Molecular Design, 2008
Quantitative Structure-Activity Relationships (QSAR) are being used since decades for prediction of biological activity, lead optimization, classification, identification and explanation of the mechanisms of drug action, and prediction of novel structural leads in drug discovery. Though the technique has lived up to its expectations in many aspects, much work still needs to be done in relation to problems related to the rational design of peptides. Peptides are the drugs of choice in many situations, however, designing them rationally is a complicated task and the complexity increases with the length of their sequence. In order to deal with the problem of peptide optimization, one of our recently developed QSAR formalisms CoRIA (Comparative Residue Interaction Analysis) is being expanded and modified as: reverse-CoRIA (rCoRIA) and mixed-CoRIA (mCoRIA) approaches. In these methodologies, the peptide is fragmented into individual units and the interaction energies (van der Waals, Coulombic and hydrophobic) of each amino acid in the peptide with the receptor as a whole (rCoRIA) and with individual active site residues in the receptor (mCoRIA) are calculated, which along with other thermodynamic descriptors, are used as independent variables that are correlated to the biological activity by chemometric methods. As a test case, the three CoRIA methodologies have been validated on a dataset of diverse nonamer peptides that bind to the Class I major histocompatibility complex molecule HLA-A*0201, and for which some structure activity relationships have already been reported. The different models developed, and validated both internally as well as externally, were found to be robust with statistically significant values of r 2 (correlation coefficient) and r 2pred (predictive r 2). These models were able to identify all the structure activity relationships known for this class of peptides, as well uncover some new relationships. This means that these methodologies will perform well for other peptide datasets too. The major advantage of these approaches is that they explicitly utilize the 3D structures of small molecules or peptides as well as their macromolecular targets, to extract position-specific information about important interactions between the ligand and receptor, which can assist the medicinal and computational chemists in designing new molecules, and biologists in studying the influence of mutations in the target receptor on ligand binding.
Journal of Medicinal Chemistry, 2004
We present a novel receptor-modeling approach (software Raptor) based on multidimensional quantitative structure-activity relationships (QSARs). To accurately predict relative free energies of ligand binding, it is of utmost importance to simulate induced fit. In Raptor, we explicitly and anisotropically allow for this phenomenon by a dual-shell representation of the receptor surrogate. In our concept, induced fit is not limited to steric aspects but includes the variation of the physicochemical fields along with it. The underlying scoring function for evaluating ligand-receptor interactions includes directional terms for hydrogen bonding and hydrophobicity and thereby treats solvation effects implicitly. This makes the approach independent from a partial-charge model and, as a consequence, allows one to smoothly model ligand molecules binding to the receptor with different net charges. We have applied the new concept toward the estimation of ligand-binding energies associated with the chemokine receptor-3 (50 ligands: r 2 ) 0.965; p 2 ) 0.932), the bradykinin B 2 receptor (52 ligands: r 2 ) 0.949; p 2 ) 0.859), and the estrogen receptor (116 ligands: r 2 ) 0.908; p 2 ) 0.907), respectively.
Journal of Computational Chemistry, 2013
Understanding binding mechanisms between enzymes and potential inhibitors and quantifying protein-ligand affinities in terms of binding free energy is of primary importance in drug design studies. In this respect, several approaches based on molecular dynamics simulations, often combined with docking techniques, have been exploited to investigate the physicochemical properties of complexes of pharmaceutical interest. Even if the geometric properties of a modeled proteinligand complex can be well predicted by computational methods, it is still challenging to rank with chemical accuracy a series of ligand analogues in a consistent way. In this article, we face this issue calculating relative binding free energies of a focal adhesion kinase, an important target for the development of anticancer drugs, with pyrrolopyrimidine-based ligands having different inhibitory power. To this aim, we employ steered molecular dynamics simulations combined with nonequilibrium work theorems for free energy calculations. This technique proves very powerful when a series of ligand analogues is considered, allowing one to tackle estimation of protein-ligand relative binding free energies in a reasonable time. In our cases, the calculated binding affinities are comparable with those recovered from experiments by exploiting the Michaelis-Menten mechanism with a competitive inhibitor. V
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 molecular mechanics/grid method for evaluation of ligand-receptor interactions
Journal of Computational Chemistry, 1995
We present a computational method for prediction of the conformation of a ligand when bound to a macromolecular receptor. The method is intended for use in systems in which the approximate location of the binding site is known and no large-scale rearrangements of the receptor are expected upon formation of the complex. The ligand is initially placed in the vicinity of the binding site and the atomic motions of the ligand and binding site are explicitly simulated, with solvent represented by an implicit solvation model and using a grid representation for the bulk of the receptor protein. These two approximations make the method computationally efficient and yet maintain accuracy close to that of an all-atom calculation. For the benzamidine/trypsin system, we ran 100 independent simulations, in many of which the ligand settled into the low-energy conformation observed in the crystal structure of the complex. The energy of these conformations was lower than and well-separated from that of others sampled. Extensions of this method are also discussed.
Recent years have witnessed rapid developments of computer-aided drug design methods, which have reached accuracy that allows their routine practical applications in drug discovery campaigns. Protein structure-based methods are useful for the prediction of binding modes of small molecules and their relative affinity. The high-throughput docking of up to 10 6 small molecules followed by scoring based on implicitsolvent force field can robustly identify micromolar binders using a rigid protein target. Molecular dynamics with explicit solvent is a low-throughput technique for the characterization of flexible binding sites and accurate evaluation of binding pathways, kinetics, and thermodynamics. In this review we highlight recent advancements in applications of ligand docking tools and molecular dynamics simulations to ligand identification and optimization.
Modern drug design approaches are based on accurate prediction of the protein-ligand binding interface and binding properties/modes of the ligand, even if experimentally determined (through X-ray or NMR) protein-ligand complex model is not available. The knowledge of the structure and physicochemical determinants of protein-substrate recognition and binding is of fundamental importance in structure-based drug design. What is highlighted herein is the procedure which makes use of tools of bioinformatics in order to test the binding properties of a ligand to a biomacromolecule. Since our research activities oriented in the quest of bioactive compounds as inhibitors towards zinc metallopeptidases, such us Angiotensin-I Converting Enzyme (ACE) [1] and Anthrax Lethal Factor (ALF) [2], we are studying the enzyme's catalytic sites and/or inhibitors conformational characteristics through Nuclear Magnetic Spectroscopy (NMR). Furthermore, we exploit the acquired structural data in an attempt to screen various compounds according their binding affinity in silico, through docking simulations methodology. Possible lead compounds will be optimized, synthesized and their binding properties would be then determined experimentally (using Xray or NMR). In this procedure application of docking simulations approaches is a prerequisite and the evaluation of the binding modes of a ligand to a protein target should be performed. To this effect, we implement docking simulations to the study of enzyme-inhibitor complexes and the results are compared to already known enzyme-inhibitor crystal structures. The potential for a docking algorithm to be used as a virtual screening [3][4]tool is based on both speed and accuracy .
A study of the binding of the antibacterial agent trimethoprim to Escherichia coli dihydrofolate reductase was carried out using energy minimization techniques with both a full, all-atom valence force field and a united atom force field. Convergence criteria ensured that no significant structural or energetic changes would occur with further minimization. Root-mean-square (RMS) deviations of both minimized structures with the experimental structure were calculated for selected regions of the protein. In the active site, the all-atom minimized structure fit the experimental structure much better than did the united atom structure. To ascertain what constitutes a good fit, the RMS deviations between crystal structures of the same enzyme either from different species or in different crystal environments were compared. The differences between the active site of the all-atom minimized structure and the experimental structure are similar to differences observed between crystal structures of the same protein.