Conformational energy penalties of protein-bound ligands

The conformational energies required for ligands to adopt their bioactive conformations were calculated for 33 ligand–protein complexes including 28 different ligands. In order to monitor the force field dependence of the results, two force fields, MM3 and AMBER, were employed for the calculations. Conformational analyses were performed in vacuo and in aqueous solution by using the generalized Born/solvent accessible surface (GB/SA) solvation model. The protein-bound conformations were relaxed by using flat-bottomed Cartesian constraints. For about 70% of the ligand–protein complexes studied, the conformational energies of the bioactive conformations were calculated to be 3 kcal/mol. It is demonstrated that the aqueous conformational ensemble for the unbound ligand must be used as a reference state in this type of calculations. The calculations for the ligand–protein complexes with conformational energy penalties of the ligand calculated to be larger than 3 kcal/mol suffer from uncertainties in the interpretation of the experimental data or limitations of the computational methods. For example, in the case of long-chain flexible ligands (e.g. fatty acids), it is demonstrated that several conformations may be found which are very similar to the conformation determined by X-ray crystallography and which display significantly lower conformational energy penalties for binding than obtained by using the experimental conformation. For strongly polar molecules, e.g. amino acids, the results indicate that further developments of the force fields and of the dielectric continuum solvation model are required for reliable calculations on the conformational properties of this type of compounds.     

QXP: Powerful, rapid computer algorithms for structure-based drug design

New methods for docking, template fitting and building pseudo-receptors are described. Full conformational searches are carried out for flexible cyclic and acyclic molecules. QXP (quick explore) search algorithms are derived from the method of Monte Carlo perturbation with energy minimization in Cartesian space. An additional fast search step is introduced between the initial perturbation and energy minimization. The fast search produces approximate low-energy structures, which are likely to minimize to a low energy. For template fitting, QXP uses a superposition force field which automatically assigns short-range attractive forces to similar atoms in different molecules. The docking algorithms were evaluated using X-ray data for 12 protein–ligand complexes. The ligands had up to 24 rotatable bonds and ranged from highly polar to mostly nonpolar. Docking searches of the randomly disordered ligands gave rms differences between the lowest energy docked structure and the energy-minimized X-ray structure, of less than 0.76 Å for 10 of the ligands. For all the ligands, the rms difference between the energy-minimized X-ray structure and the closest docked structure was less than 0.4 Å, when parts of one of the molecules which are in the solvent were excluded from the rms calculation. Template fitting was tested using four ACE inhibitors. Three ACE templates have been previously published. A single run using QXP generated a series of templates which contained examples of each of the three. A pseudo-receptor, complementary to an ACE template, was built out of small molecules, such as pyrrole, cyclopentanone and propane. When individually energy minimized in the pseudo-receptor, each of the four ACE inhibitors moved with an rms of less than 0.25 Å. After random perturbation, the inhibitors were docked into the pseudo-receptor. Each lowest energy docked structure matched the energy-minimized geometry with an rms of less than 0.08 Å. Thus, the pseudo-receptor shows steric and chemical complementarity to all four molecules. The QXP program is reliable, easy to use and sufficiently rapid for routine application in structure-based drug design.     

Toward accurate relative energy predictions of the bioactive conformation of drugs

Quantifying the relative energy of a ligand in its target-bound state (i.e. the bioactive conformation) is essential to understand the process of molecular recognition, to optimize the potency of bioactive molecules and to increase the accuracy of structure-based drug design methods. This is, nevertheless, seriously hampered by two interrelated issues, namely the difficulty in carrying out an exhaustive sampling of the conformational space and the shortcomings of the energy functions, usually based on parametric methods of limited accuracy. Matters are further complicated by the experimental uncertainty on the atomic coordinates, which precludes a univocal definition of the bioactive conformation. In this article we investigate the relative energy of bioactive conformations introducing two major improvements over previous studies: the use sophisticated QM-based methods to take into account both the internal energy of the ligand and the solvation effect, and the application of physically meaningful constraints to refine the bioactive conformation. On a set of 99 drug-like molecules, we find that, contrary to previous observations, two thirds of bioactive conformations lie within 0.5 kcal mol(-1) of a local minimum, with penalties above 2.0 kcal mol(-1) being generally attributable to structural determination inaccuracies. The methodology herein described opens the door to obtain quantitative estimates of the energy of bioactive conformations and can be used both as an aid in refining crystallographic structures and as a tool in drug discovery.     

A fast new approach to pharmacophore mapping and its application to dopaminergic and benzodiazepine agonists

Summary In the absence of a 3D structure of the target biomolecule, to propose the 3D requirements for a small molecule to exhibit a particular bioactivity, one must supply both a bioactive conformation and a superposition rule for every active compound. Our strategy identifies both simultaneously. We first generate and optimize all low-energy conformations by any suitable method. For each conformation we then use ALAD-DIN to calculate the location of points to be considered as part of the superposition. These points include atoms in the molecule and projections from the molecule to hydrogen-bond donors and acceptors or charged groups in the binding site. These positions and the relative energy of each conformation are the input to our new program DISCO. It uses a clique-detection method to find superpositions that contain a least one conformation of each molecule and user-specified numbers of point types and chirality. DISCO is fast; for example, it takes about 1 min CPU to propose pharmacophores from 21 conformations of seven molecules. We typically run DISCO several times to compare alternative pharmacophore maps. For D₂ dopamine agonists DISCO shows that the newer 2-aminothiazoles fit the traditional pharmacophore. Using site points correctly identifies the bioactive enantiomers of indoles to compare with catechols whereas using only ligand points leads to selecting the inactive enantiomer for the pharmacophore map. In addition, DISCO reproduces pharmacophore maps of benzodiazepines in the literature and proposes subtle improvements. Our experience suggests that clique-detection methods will find many applications in computational chemistry and computer-assisted molecular design.     

FLUFF-BALL, a template-based grid-independent superposition and QSAR technique: validation using a benchmark steroid data set

The Flexible Ligand Unified Force Field (FLUFF) is a molecular mechanistic superposition algorithm utilizing a template structure, on top of which the ligand(s) are superimposed. FLUFF enables a flexible semiautomatic superimposition in which the ligand and the template are allowed to seek the best common conformation, which can then be used to predict the biological activity by Boundless Adaptive Localized Ligand (BALL). In BALL, the similarity of the electrostatic and van der Waals volumes of the template and ligand is evaluated using the template-based coordinate system which makes the FLUFF-BALL invariant as to the rotations and translations of the global coordinate system. When tested using the CBG (corticosteroid binding globulin) affinities of 31 benchmark steroids, the FLUFF-BALL technique produced results comparable to standard 3D-QSAR methods. Supplementary test calculations were performed with five additional data sets. Due to its high level of automation and high throughput, the FLUFF-BALL is highly suitable for use in drug design and in scanning of large molecular libraries.     

Cross-docking study on InhA inhibitors: a combination of Autodock Vina and PM6-DH2 simulations to retrieve bio-active conformations

InhA, the NADH-dependent enoyl-acyl carrier protein reductase from Mycobacterium tuberculosis (Mtb) is the proposed main target of the first-line antituberculosis drug isoniazid (INH). INH activity is dependent on activation by the catalase peroxidase KatG, a Mtb enzyme whose mutations are linked to clinical resistance to INH. Other inhibitors of InhA that do not require any preliminary activation are known. The design of such direct potent inhibitors represents a promising approach to circumvent this resistance mechanism. An ensemble-docking process with four known InhA X-ray crystal structures and employing the Autodock Vina software was performed. Five InhA inhibitors whose bioactive conformations are known were sequentially docked in the substrate cavity of each protein. The efficiency of the docking was assessed and validated by comparing the calculated conformations to the crystallographic structures. For a same inhibitor, the docking results differed from one InhA conformation to another; however, docking poses that matched correctly or were very close to the expected bioactive conformations could be identified. The expected conformations were not systematically well ranked by the Autodock Vina scoring function. A post-docking optimization was carried out on all the docked conformations with the AMMP force field implemented on the VEGAZZ software, followed by a single point calculation of the interaction energy, using the MOPAC PM6-DH2 semi-empirical quantum chemistry method. The conformations were subsequently submitted to a PM6-DH2 optimization in partially flexible cavities. The resulting interaction energies combined with the multiple receptor conformations approach allowed us to retrieve the bioactive conformation of each ligand.     

A robust force field based method for calculating conformational energies of charged drug-like molecules

The binding affinity of a drug-like molecule depends among other things on the availability of the bioactive conformation. If the bioactive conformation has a significantly higher energy than the global minimum energy conformation, then the molecule is unlikely to bind to its target. Determination of the global minimum energy conformation and calculation of conformational penalties of binding is a prerequisite for prediction of reliable binding affinities. Here, we present a simple and computationally efficient procedure to estimate the global energy minimum for a wide variety of structurally diverse molecules, including polar and charged compounds. Identifying global energy minimum conformations of such compounds with force field methods is problematic due to the exaggeration of intramolecular electrostatic interactions. We demonstrate that the global energy minimum conformations of zwitterionic compounds generated by conformational analysis with modified electrostatics are good approximations of the conformational distributions predicted by experimental data and with molecular dynamics performed in explicit solvent. Finally the method is used to calculate conformational penalties for zwitterionic GluA2 agonists and to filter false positives from a docking study.     

Docking small ligands in flexible binding sites

A novel procedure for docking ligands in a flexible binding site is presented. It relies on conjugate gradient minimization, during which nonbonded interactions are gradually switched on. Short Monte Carlo minimization runs are performed on the most promising candidates. Solvation is implicitly taken into account in the evaluation of structures with a continuum model. It is shown that the method is very accurate and can model induced fit in the ligand and the binding site. The docking procedure has been successfully applied to three systems. The first two are the binding of progesterone and 5β-androstane-3,17-dione to the antigen binding fragment of a steroid binding antibody. A comparison of the crystal structures of the free and the two complexed forms reveals that any attempt to model binding must take protein rearrangements into account. Furthermore, the two ligands bind in two different orientations, posing an additional challenge. The third test case is the docking of N^α-(2-naphthyl-sulfonyl-glycyl)-D -para-amidino-phenyl-alanyl-piperidine (NAPAP) to human α-thrombin. In contrast to steroids, NAPAP is a very flexible ligand, and no information of its conformation in the binding site is used. All docking calculations are started from X-ray conformations of proteins with the uncomplexed binding site. For all three systems the best minima in terms of free energy have a root mean square deviation from the X-ray structure smaller than 1.5 Å for the ligand atoms. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 21–37, 1998     

Docking flexible molecules: A case study of three proteins

A genetic algorithm (GA) conformation search method is used to dock a series of flexible molecules into one of three proteins. The proteins examined are thermolysin (tmn), carboxypeptidase A (cpa), and dihydrofolate reductase (dfr). In the latter two proteins, the crystal ligand was redocked. For thermolysin, we docked eight ligands into a protein conformation derived from a single crystal structure. The bound conformations of the other ligands in tmn are known. In the cpa and dfr cases, and in seven of the eight tmn ligands, the GA docking method found conformations within 1.6 Å root mean square (rms) of the relaxed crystal conformation. © 1995 John Wiley & Sons, Inc.     

Complexes of a Zn‐metalloenzyme binding site with hydroxamate‐containing ligands. A case for detailed benchmarkings of polarizable molecular mechanics/dynamics potentials when the experimental binding structure is unknown

Zn‐metalloproteins are a major class of targets for drug design. They constitute a demanding testing ground for polarizable molecular mechanics/dynamics aimed at extending the realm of quantum chemistry (QC) to very long‐duration molecular dynamics (MD). The reliability of such procedures needs to be demonstrated upon comparing the relative stabilities of competing candidate complexes of inhibitors with the recognition site stabilized in the course of MD. This could be necessary when no information is available regarding the experimental structure of the inhibitor–protein complex. Thus, this study bears on the phosphomannose isomerase (PMI) enzyme, considered as a potential therapeutic target for the treatment of several bacterial and parasitic diseases. We consider its complexes with 5‐phospho‐d ‐arabinonohydroxamate and three analog ligands differing by the number and location of their hydroxyl groups. We evaluate the energy accuracy expectable from a polarizable molecular mechanics procedure, SIBFA. This is done by comparisons with ab initio quantum‐chemistry (QC) calculations in the following cases: (a) the complexes of the four ligands in three distinct structures extracted from the entire PMI‐ligand energy‐minimized structures, and totaling up to 264 atoms; (b) the solvation energies of several energy‐minimized complexes of each ligand with a shell of 64 water molecules; (c) the conformational energy differences of each ligand in different conformations characterized in the course of energy‐minimizations; and (d) the continuum solvation energies of the ligands in different conformations. The agreements with the QC results appear convincing. On these bases, we discuss the prospects of applying the procedure to ligand‐macromolecule recognition problems. © 2016 Wiley Periodicals, Inc.     

Torsion angle preference and energetics of small-molecule ligands bound to proteins

Small organic molecules can assume conformations in the protein-bound state that are significantly different from those in solution. We have analyzed the conformations of 21 common torsion motifs of small molecules extracted from crystal structures of protein-ligand complexes and compared them with their torsion potentials calculated by an ab initio DFT method. We find a good correlation between the potential energy of the torsion motifs and their conformational distribution in the protein-bound state: The most probable conformations of the torsion motifs agree well with the calculated global energy minima, and the lowest torsion-energy state becomes increasingly dominant as the torsion barrier height increases. The torsion motifs can be divided into 3 groups based on torsion barrier heights: high (>4 kcal/mol), medium (2-4 kcal/mol), and low (<2 kcal/mol). The calculated torsion energy profiles are predictive for the most preferred bound conformation for the high and medium barrier groups, the latter group common in druglike molecules. In the high-barrier group of druglike ligands, >95% of conformational torsions occur in the energy region <4 kcal/mol. The conformations of the torsion motifs in the protein-bound state can be modeled by a Boltzmann distribution with a temperature factor much higher than room temperature. This high-temperature factor, derived by fitting the theoretical model to the experimentally observed conformation occurrence of torsions, can be interpreted as the perturbation that proteins inflict on the conformation of the bound ligand. Using this model, it is calculated that the average strain energy of a torsion motif in ligands bound to proteins is approximately 0.6 kcal/mol, a result which can be related to the lower binding efficiency of larger ligands with more rotatable bonds. The above results indicate that torsion potentials play an important role in dictating ligand conformations in both the free and the bound states.     

Time-efficient flexible superposition of medium-sized molecules

We present an efficient algorithm for the structural alignment of medium-sized organic molecules. The algorithm has been developed for applications in 3D QSAR and in receptor modeling. The method assumes one of the molecules, the reference ligand, to be presented in the conformation that it adopts inside the receptor pocket. The second molecule, the test ligand, is considered to be flexible, and is assumed to be given in an arbitrary low-energy conformation. Ligand flexibility is modeled by decomposing the test ligand into molecular fragments, such that ring systems are completely contained in a single fragment. Conformations of fragments and torsional angles of single bonds are taken from a small finite set, which depends on the fragment and bond, respectively. The algorithm superimposes a distinguished base fragment of the test ligand onto a suitable region of the reference ligand and then attaches the remaining fragments of the test ligand in a step-by-step fashion. During this process, a scoring function is optimized that encompasses bonding terms and terms accounting for steric overlap as well as for similarity of chemical properties of both ligands. The algorithm has been implemented in the FLEXS system. To validate the quality of the produced results, we have selected a number of examples for which the mutual superposition of two ligands is experimentally given by the comparison of the binding geometries known from the crystal structures of their corresponding protein–ligand complexes. On more than two-thirds of the test examples the algorithm produces rms deviations of the predicted versus the observed conformation of the test ligand below 1.5 Å. The run time of the algorithm on a single problem instance is a few minutes on a common-day workstation. The overall goal of this research is to drastically reduce run times, while limiting the inaccuracies of the model and the computation to a tolerable level.     

Geometric accuracy of three-dimensional molecular overlays

This study examines the dependence of molecular alignment accuracy on a variety of factors including the choice of molecular template, alignment method, conformational flexibility, and type of protein target. We used eight test systems for which X-ray data on 145 ligand-protein complexes were available. The use of X-ray structures allowed an unambiguous assignment of bioactive overlays for each compound set. The alignment accuracy depended on multiple factors and ranged from 6% for flexible overlays to 73% for X-ray rigid overlays, when the conformation of the template ligand came from X-ray structures. The dependence of the overlay accuracy on the choice of templates and molecules to be aligned was found to be the most significant factor in six and seven of the eight ligand-protein complex data sets, respectively. While finding little preference for the overlay method, we observed that the introduction of molecule flexibility resulted in a decrease of overlay accuracy in 50% of the cases. We derived rules to maximize the accuracy of alignment, leading to a more than 2-fold improvement in accuracy (from 19% to 48%). The rules also allowed the identification of compounds with a low (<5%) chance to be correctly aligned. Last, the accuracy of the alignment derived without any utilization of X-ray conformers varied from <1% for the human immunodeficiency virus data set to 53% for the trypsin data set. We found that the accuracy was directly proportional to the product of the overlay accuracy from the templates in their bioactive conformations and the chance of obtaining the correct bioactive conformation of the templates. This study generates a much needed benchmark for the expectations of molecular alignment accuracy and shows appropriate usages and best practices to maximize hypothesis generation success.     

Predicting bioactive conformations and binding modes of macrocycles

Macrocyclic compounds experience increasing interest in drug discovery. It is often thought that these large and chemically complex molecules provide promising candidates to address difficult targets and interfere with protein–protein interactions. From a computational viewpoint, these molecules are difficult to treat. For example, flexible docking of macrocyclic compounds is hindered by the limited ability of current docking approaches to optimize conformations of extended ring systems for pose prediction. Herein, we report predictions of bioactive conformations of macrocycles using conformational search and binding modes using docking. Conformational ensembles generated using specialized search technique of about 70 % of the tested macrocycles contained accurate bioactive conformations. However, these conformations were difficult to identify on the basis of conformational energies. Moreover, docking calculations with limited ligand flexibility starting from individual low energy conformations rarely yielded highly accurate binding modes. In about 40 % of the test cases, binding modes were approximated with reasonable accuracy. However, when conformational ensembles were subjected to rigid body docking, an increase in meaningful binding mode predictions to more than 50 % of the test cases was observed. Electrostatic effects did not contribute to these predictions in a positive or negative manner. Rather, achieving shape complementarity at macrocycle-target interfaces was a decisive factor. In summary, a combined computational protocol using pre-computed conformational ensembles of macrocycles as a starting point for docking shows promise in modeling binding modes of macrocyclic compounds.     

A fast and efficient method to generate biologically relevant conformations

Summary Mutual binding between a ligand of low molecular weight and its macromolecular receptor demands structural complementarity of both species at the recognition site. To predict binding properties of new molecules before synthesis, information about possible conformations of drug molecules at the active site is required, especially if the 3D structure of the receptor is not known. The statistical analysis of small-molecule crystal data allows one to elucidate conformational preferences of molecular fragments and accordingly to compile libraries of putative ligand conformations. A comparison of geometries adopted by corresponding fragments in ligands bound to proteins shows similar distributions in conformation space. We have developed an automatic procedure that generates different conformers of a given ligand. The entire molecule is decomposed into its individual ring and open-chain torsional fragments, each used in a variety of favorable conformations. The latter ones are produced according to the library information about conformational preferences. During this building process, an extensive energy ranking is applied. Conformers ranked as energetically favorable are subjected to an optimization in torsion angle space. During minimization, unfavorable van der Waals interactions are removed while keeping the open-chain torsion angles as close as possible to the experimentally most frequently observed values. In order to assess how well the generated conformers map conformation space, a comparison with experimental data has been performed. This comparison gives some confidence in the efficiency and completeness of this approach. For some ligands that had been structurally characterized by protein crystallography, the program was used to generate sets of some 10 to 100 conformers. Among these, geometries are found that fall convincingly close to the conformations actually adopted by these ligands at the binding site.     

The histamine H1-receptor antagonist binding site. Part I: Active conformation of cyproheptadine

Summary The active conformation of several histamine H₁-antagonists is investigated. As a template molecule we used the antagonist cyproheptadine, which consists of a piperidylene ring connected to a tricyclic system. The piperidylene moiety is shown to be flexible. The global minimum is a chair conformation but, additionally, a second chair and various boat conformations have to be considered, as their energies are less than 5 kcal/mol above the energy of the global minimum. Two semi-rigid histamine H₁-antagonists, phenindamine and triprolidine, were fitted onto the various conformations of cyproheptadine in order to derive the pharmacologically active conformation of cyproheptadine. At the same time, the active conformation of both phenindamine and triprolidine was derived. It is demonstrated that, within the receptor-bound conformation of cyproheptadine, the piperidylene ring most probably exists in a boat form.     

Systematic drug receptor mapping: A new approach to the analysis of conformational energy calculations of flexible molecules with application to dopaminergic and adrenergic agonists

The computer program PRODIS is used to find low energy conformations of flexible molecules by searching the potential energy surface(s) of one or more torsion angles via rigid rotation. The n-dimensional grid of energy versus torsion angles is then converted to a Boltzman probability distribution, with the probability being represented not as a function of torsion angle, but rather a distance between two atoms. These atoms are chosen by comparison with a known, active analogue in which certain atoms have previously been determined as requirements for drug activity. PRODIS produces a list of low energy conformations, their corresponding interatomic distances and the Boltzman probability for each distance ±0.125, as well as the total probability for each conformation. The user also specifies a target interatomic distance and range (usually derived from a more rigid analogue) for which PRODIS lists all conformations and their Boltzman probability that meet this distance.     

Molecular modelling prediction of ligand binding site flexibility

We have investigated the efficacy of generating multiple sidechain conformations using a rotamer library in order to find the experimentally observed ligand binding site conformation of a protein in the presence of a bound ligand. We made use of a recently published algorithm that performs an exhaustive conformational search using a rotamer library to enumerate all possible sidechain conformations in a binding site. This approach was applied to a dataset of proteins whose structures were determined by X-ray and NMR methods. All chosen proteins had two or more structures, generally involving different bound ligands. By taking one of these structures as a reference, we were able in most cases to successfully reproduce the experimentally determined conformations of the other structures, as well as to suggest alternative low-energy conformations of the binding site. In those few cases where this procedure failed, we observed that the bound ligand had induced a high-energy conformation of the binding site. These results suggest that for most proteins that exhibit limited backbone motion, ligands tend to bind to low energy conformations of their binding sites. Our results also reveal that it is possible in most cases to use a rotamer search-based approach to predict alternative low-energy protein binding site conformations that can be used by different ligands. This opens the possibility of incorporating alternative binding site conformations to improve the efficacy of docking and structure-based drug design algorithms.