Rescoring docking hit lists for model cavity sites: predictions and experimental testing

Molecular docking computationally screens thousands to millions of organic molecules against protein structures, looking for those with complementary fits. Many approximations are made, often resulting in low “hit rates.” A strategy to overcome these approximations is to rescore top-ranked docked molecules using a better but slower method. One such is afforded by molecular mechanics-generalized Born surface area (MM-GBSA) techniques. These more physically realistic methods have improved models for solvation and electrostatic interactions and conformational change compared to most docking programs. To investigate MM-GBSA rescoring, we re-ranked docking hit lists in three small buried sites: a hydrophobic cavity that binds apolar ligands, a slightly polar cavity that binds aryl and hydrogen-bonding ligands, and an anionic cavity that binds cationic ligands. These sites are simple; consequently, incorrect predictions can be attributed to particular errors in the method, and many likely ligands may actually be tested. In retrospective calculations, MM-GBSA techniques with binding-site minimization better distinguished the known ligands for each cavity from the known decoys compared to the docking calculation alone. This encouraged us to test rescoring prospectively on molecules that ranked poorly by docking but that ranked well when rescored by MM-GBSA. A total of 33 molecules highly ranked by MM-GBSA for the three cavities were tested experimentally. Of these, 23 were observed to bind—these are docking false negatives rescued by rescoring. The 10 remaining molecules are true negatives by docking and false positives by MM-GBSA. X-ray crystal structures were determined for 21 of these 23 molecules. In many cases, the geometry prediction by MM-GBSA improved the initial docking pose and more closely resembled the crystallographic result; yet in several cases, the rescored geometry failed to capture large conformational changes in the protein. Intriguingly, rescoring not only rescued docking false positives, but also introduced several new false positives into the top-ranking molecules. We consider the origins of the successes and failures in MM-GBSA rescoring in these model cavity sites and the prospects for rescoring in biologically relevant targets.     

Testing a flexible-receptor docking algorithm in a model binding site

Sampling receptor flexibility is challenging for database docking. We consider a method that treats multiple flexible regions of the binding site independently, recombining them to generate different discrete conformations. This algorithm scales linearly rather than exponentially with the receptor's degrees of freedom. The method was first evaluated for its ability to identify known ligands of a hydrophobic cavity mutant of T4 lysozyme (L99A). Some 200000 molecules of the Available Chemical Directory (ACD) were docked against an ensemble of cavity conformations. Surprisingly, the enrichment of known ligands from among a much larger number of decoys in the ACD was worse than simply docking to the apo conformation alone. Large decoys, accommodated in the larger cavity conformations sampled in the ensemble, were ranked better than known small ligands. The calculation was redone with an energy correction term that considered the cost of forming the larger cavity conformations. Enrichment improved, as did the balance between high-ranking large and small ligands. In a second retrospective test, the ACD was docked against a conformational ensemble of thymidylate synthase. Compared to docking against individual enzyme conformations, the flexible receptor docking approach improved enrichment of known ligands. Including a receptor conformational energy weighting term improved enrichment further. To test the method prospectively, the ACD database was docked against another cavity mutant of lysozyme (L99A/M102Q). A total of 18 new compounds predicted to bind this polar cavity and to change its conformation were tested experimentally; 14 were found to bind. The bound structures for seven ligands were determined by X-ray crystallography. The predicted geometries of these ligands all corresponded to the observed geometries to within 0.7A RMSD or better. Significant conformational changes of the cavity were observed in all seven complexes. In five structures, part of the observed accommodations were correctly predicted; in two structures, the receptor conformational changes were unanticipated and thus never sampled. These results suggest that although sampling receptor flexibility can lead to novel ligands that would have been missed when docking a rigid structure, it is also important to consider receptor conformational energy.     

A model binding site for testing scoring functions in molecular docking

Prediction of interaction energies between ligands and their receptors remains a major challenge for structure-based inhibitor discovery. Much effort has been devoted to developing scoring schemes that can successfully rank the affinities of a diverse set of possible ligands to a binding site for which the structure is known. To test these scoring functions, well-characterized experimental systems can be very useful. Here, mutation-created binding sites in T4 lysozyme were used to investigate how the quality of atomic charges and solvation energies affects molecular docking. Atomic charges and solvation energies were calculated for 172,118 molecules in the Available Chemicals Directory using a semi-empirical quantum mechanical approach by the program AMSOL. The database was first screened against the apolar cavity site created by the mutation Leu99Ala (L99A). Compared to the electronegativity-based charges that are widely used, the new charges and desolvation energies improved ranking of known apolar ligands, and better distinguished them from more polar isosteres that are not observed to bind. To investigate whether the new charges had predictive value, the non-polar residue Met102, which forms part of the binding site, was changed to the polar residue glutamine. The structure of the resulting Leu99Ala and Met102Gln double mutant of T4 lysozyme (L99A/M102Q) was determined and the docking calculation was repeated for the new site. Seven representative polar molecules that preferentially docked to the polar versus the apolar binding site were tested experimentally. All seven bind to the polar cavity (L99A/M102Q) but do not detectably bind to the apolar cavity (L99A). Five ligand-bound structures of L99A/M102Q were determined by X-ray crystallography. Docking predictions corresponded to the crystallographic results to within 0.4A RMSD. Improved treatment of partial atomic charges and desolvation energies in database docking appears feasible and leads to better distinction of true ligands. Simple model binding sites, such as L99A and its more polar variants, may find broad use in the development and testing of docking algorithms.     

Probing molecular docking in a charged model binding site

A model binding site was used to investigate charge-charge interactions in molecular docking. This simple site, a small (180A(3)) engineered cavity in cyctochrome c peroxidase (CCP), is negatively charged and completely buried from solvent, allowing us to explore the balance between electrostatic energy and ligand desolvation energy in a system where many of the common approximations in docking do not apply. A database with about 5300 molecules was docked into this cavity. Retrospective testing with known ligands and decoys showed that overall the balance between electrostatic interaction and desolvation energy was captured. More interesting were prospective docking scre"ens that looked for novel ligands, especially those that might reveal problems with the docking and energy methods. Based on screens of the 5300 compound database, both high-scoring and low-scoring molecules were acquired and tested for binding. Out of 16 new, high-scoring compounds tested, 15 were observed to bind. All of these were small heterocyclic cations. Binding constants were measured for a few of these, they ranged between 20microM and 60microM. Crystal structures were determined for ten of these ligands in complex with the protein. The observed ligand geometry corresponded closely to that predicted by docking. Several low-scoring alkyl amino cations were also tested and found to bind. The low docking score of these molecules owed to the relatively high charge density of the charged amino group and the corresponding high desolvation penalty. When the complex structures of those ligands were determined, a bound water molecule was observed interacting with the amino group and a backbone carbonyl group of the cavity. This water molecule mitigates the desolvation penalty and improves the interaction energy relative to that of the "naked" site used in the docking screen. Finally, six low-scoring neutral molecules were also tested, with a view to looking for false negative predictions. Whereas most of these did not bind, two did (phenol and 3-fluorocatechol). Crystal structures for these two ligands in complex with the cavity site suggest reasons for their binding. That these neutral molecules do, in fact bind, contradicts previous results in this site and, along with the alkyl amines, provides instructive false negatives that help identify weaknesses in our scoring functions. Several improvements of these are considered.     

Specificity in structure-based drug design: Identification of a novel,selective inhibitor of Pneumocystis carinii dihydrofolate reductase

Specificity is an important aspect of structure-based drug design. Distinguishing between related targets in different organisms is often the key to therapeutic success. Pneumocystis carinii is a fungal opportunist which causes a crippling pneumonia in immunocompromised individuals. We report the identification of novel inhibitors of P. cariniidihydrofolate reductase (DHFR) that are selective versus inhibition of human DHFR using computational molecular docking techniques. The Fine Chemicals Directory, a database of commercially available compounds, was screened with the DOCK program suite to produce a list of potential P. carinii DHFR inhibitors. We then used a postdocking refinement directed at discerning subtle structural and chemical features that might reflect species specificity. Of 40 compounds predicted to exhibit anti-PneumocystisDHFR activity, each of novel chemical framework, 13 (33%) show IC₅₀ values better than 150 μM in an enzyme assay. These inhibitors were further assayed against human DHFR: 10 of the 13 (77%) bind preferentially to the fungal enzyme. The most potent compound identified is a 7 μM inhibitor of P. carinii DHFR with 25-fold selectivity. The ability of molecular docking methods to locate selective inhibitors reinforces our view of structure-based drug discovery as a valuable strategy, not only for identifying lead compounds, but also for addressing receptor specificity. Proteins 29:59–67, 1997. © 1997 Wiley-Liss, Inc.     

Ligand discovery from a dopamine D3 receptor homology model and crystal structure

G protein-coupled receptors (GPCRs) are intensely studied as drug targets and for their role in signaling. With the determination of the first crystal structures, interest in structure-based ligand discovery increased. Unfortunately, for most GPCRs no experimental structures are available. The determination of the D(3) receptor structure and the challenge to the community to predict it enabled a fully prospective comparison of ligand discovery from a modeled structure versus that of the subsequently released crystal structure. Over 3.3 million molecules were docked against a homology model, and 26 of the highest ranking were tested for binding. Six had affinities ranging from 0.2 to 3.1 μM. Subsequently, the crystal structure was released and the docking screen repeated. Of the 25 compounds selected, five had affinities ranging from 0.3 to 3.0 μM. One of the new ligands from the homology model screen was optimized for affinity to 81 nM. The feasibility of docking screens against modeled GPCRs more generally is considered.     

Structure-based approach to pharmacophore identification, in silico screening, and three-dimensional quantitative structure-activity relationship studies for inhibitors of Trypanosoma cruzi dihydrofolate reductase function

We have employed a structure-based three-dimensional quantitative structure-activity relationship (3D-QSAR) approach to predict the biochemical activity for inhibitors of T. cruzi dihydrofolate reductase-thymidylate synthase (DHFR-TS). Crystal structures of complexes of the enzyme with eight different inhibitors of the DHFR activity together with the structure in the substrate-free state (DHFR domain) were used to validate and refine docking poses of ligands that constitute likely active conformations. Structural information from these complexes formed the basis for the structure-based alignment used as input for the QSAR study. Contrary to indirect ligand-based approaches the strategy described here employs a direct receptor-based approach. The goal is to generate a library of selective lead inhibitors for further development as antiparasitic agents. 3D-QSAR models were obtained for T. cruzi DHFR-TS (30 inhibitors in learning set) and human DHFR (36 inhibitors in learning set) that show a very good agreement between experimental and predicted enzyme inhibition data. For crossvalidation of the QSAR model(s), we have used the 10% leave-one-out method. The derived 3D-QSAR models were tested against a few selected compounds (a small test set of six inhibitors for each enzyme) with known activity, which were not part of the learning set, and the quality of prediction of the initial 3D-QSAR models demonstrated that such studies are feasible. Further refinement of the models through integration of additional activity data and optimization of reliable docking poses is expected to lead to an improved predictive ability.     

Virtual screening,docking, and dynamics of potential new inhibitors of dihydrofolate reductase from Yersinia pestis

In the present work, we propose to design drugs that target the enzyme dihydrofolate redutase (DHFR) as a means of a novel drug therapy against plague. Potential inhibitors of DHFR from Yersinia pestis (YpDHFR) were selected by virtual screening and subjected to docking, molecular dynamics (MD) simulations, and Poisson–Boltzmann surface area method, in order to evaluate their interactions in the active sites of YpDHFR and human DHFR (HssDHFR). The results suggested selectivity for three compounds that were further used to propose the structures of six new potential selective inhibitors for YpDHFR.     

In pursuit of virtual lead optimization: pruning ensembles of receptor structures for increased efficiency and accuracy during docking

Representing receptors as ensembles of protein conformations during docking is a powerful method to approximate protein flexibility and increase the accuracy of the resulting ranked list of compounds. Unfortunately, docking compounds against a large number of ensemble members can increase computational cost and time investment. In this article, we present an efficient method to evaluate and select the most contributive ensemble members prior to docking for targets with a conserved core of residues that bind a ligand moiety. We observed that ensemble members that preserve the geometry of the active site core are most likely to place ligands in the active site with a conserved orientation, generally rank ligands correctly and increase interactions with the receptor. A relative distance approach is used to quantify the preservation of the three-dimensional interatomic distances of the conserved ligand-binding atoms and prune large ensembles quickly. In this study, we investigate dihydrofolate reductase as an example of a protein with a conserved core; however, this method for accurately selecting relevant ensemble members a priori can be applied to any system with a conserved ligand-binding core, including HIV-1 protease, kinases, and acetylcholinesterase. Representing a drug target as a pruned ensemble during in silico screening should increase the accuracy and efficiency of high-throughput analyses of lead analogs.     

In pursuit of virtual lead optimization: the role of the receptor structure and ensembles in accurate docking

Accurate ranking during in silico lead optimization is critical to drive the generation of new ligands with higher affinity, yet it is especially difficult because of the subtle changes between analogs. In order to assess the role of the structure of the receptor in delivering accurate lead ranking results, we docked a set of forty related inhibitors to structures of one species of dihydrofolate reductase (DHFR) derived from crystallographic, NMR solution data, and homology models. In this study, the crystal structures yielded the superior results: the compounds were placed in the active site in the conserved orientation and the docking scores for 80% percent of the compounds clustered into the same bins as the measured affinity. Single receptor structures derived from NMR data or homology models did not serve as accurate docking receptors. To our knowledge, these are the first experiments that assess ranking of homologous lead compounds using a variety of receptor structures. We then extended the study to investigate whether ensembles, either computationally or experimentally derived, of all of the single starting structures aid, hinder or have no effect on the performance of the starting template. Impressively, when ensembles of receptor structures derived from NMR data or homology models were employed, docking accuracy improved to a level equal to that of the high resolution crystal structures. The same experiments using a second species of DHFR and set of ligands confirm the results. A comparison of the structures of the individual ensemble members to the starting structures shows that the effect of the ensembles can be ascribed to protein flexibility in addition to absorption of computational error.     

Hit discovery of 4-amino-N-(4-(3-(trifluoromethyl)phenoxy)pyrimidin-5-yl)benzamide: A novel EGFR inhibitor from a designed small library

Searching for hit compounds within the huge chemical space resembles the attempt to find a needle in a haystack. Cheminformatics-guided selection of few representative molecules of a rationally designed virtual combinatorial library is a powerful tool to confront this challenge, speed up hit identification and cut off costs. Herein, this approach has been applied to identify hit compounds with novel scaffolds able to inhibit EGFR kinase. From a generated virtual library, six 4-aryloxy-5-aminopyrimidine scaffold-derived compounds were selected, synthesized and evaluated as hit EGFR inhibitors. 4-Aryloxy-5-benzamidopyrimidines inhibited EGFR with IC₅₀ 1.05–5.37 μM. Cell-based assay of the most potent EGFR inhibitor hit (10ac) confirmed its cytotoxicity against different cancerous cells. In spite of no EGFR, HER2 or VEGFR1 inhibition was elicited by 4-aryloxy-5-(thio)ureidopyrimidine derivatives, cell-based evaluation suggested them as antiproliferative hits acting by other mechanism(s). Molecular docking study provided a plausible explanation of incapability of 4-aryloxy-5-(thio)ureidopyrimidines to inhibit EGFR and suggested a reasonable binding mode of 4-aryloxy-5-benzamidopyrimidines which provides a basis to develop more optimized ligands.     

Design,synthesis, biological evaluation and computational investigation of novel inhibitors of dihydrofolate reductase of opportunistic pathogens

The present work deals with design, synthesis and biological evaluation of novel, diverse compounds as potential inhibitors of dihydrofolate reductase (DHFR) from opportunistic microorganisms; Pneumocystis carinii (pc), Toxoplasma gondii (tg) and Mycobacterium avium (ma). A set of 14 structurally diverse compounds were designed with varying key pharmacophoric features of DHFR inhibitors, bulky distal substitutions and different bridges joining the distal part and 2,4-diaminopyrimidine nucleus. The designed compounds were synthesized and evaluated in enzyme assay against pc, tg and ma DHFR. The rat liver (rl) DHFR was used as mammalian standard. As the next logical step of the project, flexible molecular docking studies were carried out to predict the binding modes of these compounds in pcDHFR active site and the obtained docked poses were post processed using MM-GBSA protocol for prediction of relative binding affinity. The predicted binding modes were able to rationalize the experimental results in most cases. Of particular interest, both the docking scores and MM-GBSA predicted ΔG_bind were able to distinguish between the active and low active compounds. Furthermore, good correlation coefficient of 0.797 was obtained between the IC₅₀ values and MM-GBSA predicted ΔG_bind. Taken together, the current work provides not only a novel scaffold for further optimization of DHFR inhibitors but also an understanding of the specific interactions of inhibitors with DHFR and structural modifications that improve selectivity.     

Towards in silico lead optimization: scores from ensembles of protein/ligand conformations reliably correlate with biological activity

Accurately ranking protein/ligand interactions and distinguishing subtle differences between homologous compounds in a virtual focused library in silico is essential in a structure-based drug discovery program. In order to establish a predictive model to design novel inhibitors of dihydrofolate reductase (DHFR) from the parasitic protozoa, Cryptosporidium hominis, we docked a series of 30 DHFR inhibitors with measured inhibition constants against the crystal structure of the protein. By including protein flexibility and averaging the energies of the 25 lowest protein/ligand conformers we obtained more accurate total nonbonded energies from which we calculated a predicted biological activity. The calculated and measured biological activities showed reliable correlations of 72.9%. Additionally, visual analysis of the ensemble of protein/ligand conformations revealed alternative ligand binding pockets in the active site. Using the same principles we then created a homology model of DHFR from Toxoplasma gondii and docked 11 inhibitors. A correlation of 50.2% between docking score and activity validates both the method and the model. The correlations presented here are particularly compelling considering the high structural similarity of the ligands and the fact that we have used structures derived from crystallographic data and homology modeling. These docking principles may be useful in any lead optimization study where accurate ranking of similar compounds is desired.     

Limits of Ligand Selectivity from Docking to Models: In Silico Screening for A1 Adenosine Receptor Antagonists

G protein-coupled receptors (GPCRs) are attractive targets for pharmaceutical research. With the recent determination of several GPCR X-ray structures, the applicability of structure-based computational methods for ligand identification, such as docking, has increased. Yet, as only about 1% of GPCRs have a known structure, receptor homology modeling remains necessary. In order to investigate the usability of homology models and the inherent selectivity of a particular model in relation to close homologs, we constructed multiple homology models for the A₁ adenosine receptor (A₁AR) and docked ∼2.2 M lead-like compounds. High-ranking molecules were tested on the A₁AR as well as the close homologs A_2AAR and A₃AR. While the screen yielded numerous potent and novel ligands (hit rate 21% and highest affinity of 400 nM), it delivered few selective compounds. Moreover, most compounds appeared in the top ranks of only one model. These findings have implications for future screens.     

Molecular docking analysis of bioactive compounds from Mollugo cerviana (L.) SER with DHFR for antifungal activity

Fungal infections have been increasing in recent years due to growing number of high-risk patients particularly immuno compromised hosts. Candida is the third- or fourth-most-common isolate in nosocomial bloodstream infections. The increase of fungal resistance to classical drugs, the treatment costs, and the fact that most available antifungal drugs have only fungistatic activity, justify the search for new strategies. Identification of therapeutic compounds from plants has been the centre of attraction ever since they were discovered. It is of interest to document the molecular docking analysis of bioactive compounds present in Mollugo cerviana (L.) SER with the DHFR protein target for antifungal activity. We show the optimal binding features of several compounds from the extract with in vivo and in vitro activities. Results of this showed that all compounds showed good antimicrobial activity and a very good antifungal activity against the target DHFR protein. So, these compounds may act as potential drug molecules after the experimental validation.     

In silico hit optimization toward AKT inhibition: fragment-based approach,molecular docking and molecular dynamics study

Abstract

Protein kinase B also known as AKT is a cardinal node in different signaling pathways that regulates diverse cell processes. AKT has three isoforms that share high homology. Hyperactivation of each isoform is related with different types of cancer. This work describes the computational search for new inhibitors using a hit optimization process of the previously reported AKT pan inhibitor, a 2,4,6-trisubstituted pyridine. A database of new molecules was proposed using a variant of fragment-based docking methodology and previous reported considerations. Molecular docking followed by molecular dynamics studies were performed to select the best compounds and analyze their behavior. Protein–ligand complexes energy was calculated using molecular mechanics Poisson–Boltzmann surface area protocol. Further, proposed molecules were compared with the ChEMBL database of compounds assayed against AKT. Data analysis leads to determine the structural requirements necessary for a favorable interaction of the proposed ligands with the AKT pocket. Molecular dynamics data suggested that the pK_a of the ligands is important for the stability in the AKT pocket. Molecular similarity analysis shows that proposed ligands have not been previously reported. Thus, ligands with high docking scores and favorable behavior on molecular dynamics simulations are proposed as potential AKT inhibitors.     

Comparative docking of dual conformations in human fatty acid synthase thioesterase domain reveals potential binding cavity for virtual screening of ligands

Human fatty acid synthase (hFASN), a homo dimeric lipogenic enzyme with seven catalytic domains, is an important clinical target in cancer, metabolic syndrome and infections. Here, molecular modelling and docking methods were implemented to examine the inter-molecular interactions of thioesterase (TE) domain in hFASN with its physiological substrate, and to identify potential chemical inhibitors. TE catalyses the hydrolysis of thioester bond between palmitate and the 4’ phosphopantetheine of acyl carrier protein, releasing 16-carbon palmitate. The crystal structure of hFASN TE in two inhibitory conformations (A and B) were geometry-optimized and used for molecular docking with palmitate, orlistat (a known FASN inhibitor) and virtual screening against compounds from National Cancer Institute (NCI) database. Relatively, low binding affinity was observed during the complex formation of palmitate with A (?.164 kcal/mol) and B (?.332 kcal/mol) forms of TE, when compared with orlistat-docked TE (A form: ?5.872 kcal/mol and B form: ?5.484 kcal/mol), clearly indicating that the native inhibited conformation (crystal structure) was unfavourable for substrate binding. We used these orlistat dual binding modes as positive controls for prioritizing the ligands during virtual screening. From 2, 31,617 molecules in the NCI database, 916 high-scoring compounds (hit ligands) were obtained for A-form and 4582 for B-form of the TE-domain, which were then ranked according to glide docking score, XP H bond score, absorption, distribution, metabolism and excretion and binding free energy (Prime/MM-GBSA). Consequently, two top scoring ligands (NSC: 319661 and NSC: 153166) emerged as promising drug candidates that may be tested in FASN-over-expressing diseases.     

Roles for Ordered and Bulk Solvent in Ligand Recognition and Docking in Two Related Cavities

A key challenge in structure-based discovery is accounting for modulation of protein-ligand interactions by ordered and bulk solvent. To investigate this, we compared ligand binding to a buried cavity in Cytochrome c Peroxidase (CcP), where affinity is dominated by a single ionic interaction, versus a cavity variant partly opened to solvent by loop deletion. This opening had unexpected effects on ligand orientation, affinity, and ordered water structure. Some ligands lost over ten-fold in affinity and reoriented in the cavity, while others retained their geometries, formed new interactions with water networks, and improved affinity. To test our ability to discover new ligands against this opened site prospectively, a 534,000 fragment library was docked against the open cavity using two models of ligand solvation. Using an older solvation model that prioritized many neutral molecules, three such uncharged docking hits were tested, none of which was observed to bind; these molecules were not highly ranked by the new, context-dependent solvation score. Using this new method, another 15 highly-ranked molecules were tested for binding. In contrast to the previous result, 14 of these bound detectably, with affinities ranging from 8 µM to 2 mM. In crystal structures, four of these new ligands superposed well with the docking predictions but two did not, reflecting unanticipated interactions with newly ordered waters molecules. Comparing recognition between this open cavity and its buried analog begins to isolate the roles of ordered solvent in a system that lends itself readily to prospective testing and that may be broadly useful to the community.