Development and validation of a genetic algorithm for flexible docking

A quantum mechanics-based scoring function: study of zinc ion-mediated ligand binding

In this communication, we report the development of a novel quantum mechanics-based scoring function to predict free energy of ligand binding in the zinc metalloenzymes carbonic anhydrase (CA) and carboxypeptidase A (CPA). In particular, the AM1 method is used in conjunction with solvation modeling to predict the relative binding affinities of 18 CA and 5 CPA inhibitors. The effect of metal-ligand charge transfer is also discussed and shown to be different in CPA and CA, providing a further challenge to computing metalloenzyme binding affinities.

Molecular determinants of thapsigargin binding by SERCA Ca2+-ATPase: A computational docking study

Thapsigargin (TG) is a potent and commonly used inhibitor of the ion transport activity of sarco/endoplasmic reticulum Ca2+-ATPases (SERCA). Based on the recently published crystal structures of rabbit muscle SERCA1a in the Ca2+/E1 (E1) and TG/E2 (E2) conformations, we performed computational docking studies to characterize the molecular interactions that govern binding of TG and TG-analogs by the enzyme. Using the program GOLD (genetic optimization for ligand docking) in combination with the scoring function ChemScore, TG was docked into the binding site of the E1 and E2 conformations of SERCA1a. The docking results revealed a consensus ligand-binding mode consistent with the crystal structure and showed that hydrophobic interactions are the primary driving force of TG binding by SERCA. Moreover, it was shown that the conformational changes accompanying the E2 to E1 transition in the enzyme likely displace TG from its favored orientation in the binding site, thereby substantially reducing its binding affinity. This finding illustrates on the molecular level how TG may exert its inhibitory effect in binding tightly to the E2 form and preventing it from converting into its E1 form, a requirement for catalytic function. We also docked 9 TG analogs into the E2 conformation of the enzyme. Eight of the analogs adopted a binding mode very similar to that of TG, whereas one compound preferred a different orientation in the binding site. Analysis of the predicted binding affinities showed a good correlation with the experimentally observed inhibitory potencies of the analogs. Docking was also performed with several modeled mutants of SERCA1a, whose phenylalanine residue in position 256 (Phe256) had been modified. The experimentally observed declines in TG sensitivity in most of the Phe256 mutants was qualitatively accounted for and appears, at least in part, be due to a slightly altered TG-binding mode.

Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening

Glide's ability to identify active compounds in a database screen is characterized by applying Glide to a diverse set of nine protein receptors. In many cases, two, or even three, protein sites are employed to probe the sensitivity of the results to the site geometry. To make the database screens as realistic as possible, the screens use sets of "druglike" decoy ligands that have been selected to be representative of what we believe is likely to be found in the compound collection of a pharmaceutical or biotechnology company. Results are presented for releases 1.8, 2.0, and 2.5 of Glide. The comparisons show that average measures for both "early" and "global" enrichment for Glide 2.5 are 3 times higher than for Glide 1.8 and more than 2 times higher than for Glide 2.0 because of better results for the least well-handled screens. This improvement in enrichment stems largely from the better balance of the more widely parametrized GlideScore 2.5 function and the inclusion of terms that penalize ligand-protein interactions that violate established principles of physical chemistry, particularly as it concerns the exposure to solvent of charged protein and ligand groups. Comparisons to results for the thymidine kinase and estrogen receptors published by Rognan and co-workers (J. Med. Chem. 2000, 43, 4759-4767) show that Glide 2.5 performs better than GOLD 1.1, FlexX 1.8, or DOCK 4.01.

Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy

Unlike other methods for docking ligands to the rigid 3D structure of a known protein receptor, Glide approximates a complete systematic search of the conformational, orientational, and positional space of the docked ligand. In this search, an initial rough positioning and scoring phase that dramatically narrows the search space is followed by torsionally flexible energy optimization on an OPLS-AA nonbonded potential grid for a few hundred surviving candidate poses. The very best candidates are further refined via a Monte Carlo sampling of pose conformation; in some cases, this is crucial to obtaining an accurate docked pose. Selection of the best docked pose uses a model energy function that combines empirical and force-field-based terms. Docking accuracy is assessed by redocking ligands from 282 cocrystallized PDB complexes starting from conformationally optimized ligand geometries that bear no memory of the correctly docked pose. Errors in geometry for the top-ranked pose are less than 1 A in nearly half of the cases and are greater than 2 A in only about one-third of them. Comparisons to published data on rms deviations show that Glide is nearly twice as accurate as GOLD and more than twice as accurate as FlexX for ligands having up to 20 rotatable bonds. Glide is also found to be more accurate than the recently described Surflex method.

Prediction of Biological Targets Using Probabilistic Neural Networks and Atom-Type Descriptors

Prediction of biological targets for molecules from their chemical structures is beneficial for generating focused libraries, selecting compounds for screening, and annotating biological activities for those compounds whose activities are unknown. We studied the ability of a probabilistic neural network (PNN), a variant of normalized radial basis function (RBF) neural networks, to predict biological activities for a set of 799 compounds having activities against seven biological targets. The compounds were taken from the MDDR database, and they were carefully selected to comprise distinct biological activities and diverse structures. The structural characteristics of compounds were represented by a set of 24 atom-type descriptors defined by 2D topological chemical structures. The modeling was done in two ways: (1). compounds having one certain activity were discriminated from those not having that activity and (2). all compounds were classified into seven biological classes. In both cases, around 90% of the compounds were correctly classified. Further validation of the modeled PNNs was done with 26 317 compounds having biological activities against various targets except for the seven targets used for modeling, and 67-98% compounds were correctly classified depending upon the targets. A PNN trains much more quickly than widely used neural networks such as a feed-forward neural network with error back-propagation. Calculation of atom-type descriptors is easy even for a large-size chemical library. Combination of PNN and atom-type descriptors thus provides a powerful way to predict biological activities from structural information.

Assessment of Docking Poses: Interactions-Based Accuracy Classification (IBAC) versus Crystal Structure Deviations

Six docking programs (FlexX, GOLD, ICM, LigandFit, the Northwestern University version of DOCK, and QXP) were evaluated in terms of their ability to reproduce experimentally observed binding modes (poses) of small-molecule ligands to macromolecular targets. The accuracy of a pose was assessed in two ways: First, the RMS deviation of the predicted pose from the crystal structure was calculated. Second, the predicted pose was compared to the experimentally observed one regarding the presence of key interactions with the protein. The latter assessment is referred to as interactions-based accuracy classification (IBAC). In a number of cases significant discrepancies were found between IBAC and RMSD-based classifications. Despite being more subjective, the IBAC proved to be a more meaningful measure of docking accuracy in all these cases.

Virtual Screening Using Protein−Ligand Docking: Avoiding Artificial Enrichment

This study addresses a number of topical issues around the use of protein-ligand docking in virtual screening. We show that, for the validation of such methods, it is key to use focused libraries (containing compounds with one-dimensional properties, similar to the actives), rather than "random" or "drug-like" libraries to test the actives against. We also show that, to obtain good enrichments, the docking program needs to produce reliable binding modes. We demonstrate how pharmacophores can be used to guide the dockings and improve enrichments, and we compare the performance of three consensus-ranking protocols against ranking based on individual scoring functions. Finally, we show that protein-ligand docking can be an effective aid in the screening for weak, fragment-like binders, which has rapidly become a popular strategy for hit identification. All results presented are based on carefully constructed virtual screening experiments against four targets, using the protein-ligand docking program GOLD.

Ligand-protein docking using a quantum stochastic tunneling optimization method

A novel hybrid optimization method called quantum stochastic tunneling has been recently introduced. Here, we report its implementation within a new docking program called EasyDock and a validation with the CCDC/Astex data set of ligand-protein complexes using the PLP score to represent the ligand-protein potential energy surface and ScreenScore to score the ligand-protein binding energies. When taking the top energy-ranked ligand binding mode pose, we were able to predict the correct crystallographic ligand binding mode in up to 75% of the cases. By using this novel optimization method run times for typical docking simulations are significantly shortened.

Recovering the true targets of specific ligands by virtual screening of the protein data bank

The Protein Data Bank (PDB) has been processed to extract a screening protein library (sc-PDB) of 2148 entries. A knowledge-based detection algorithm has been applied to 18,000 PDB files to find regular expressions corresponding to either protein, ions, co-factors, solvent, or ligand atoms. The sc-PDB database comprises high-resolution X-ray structures of proteins for which (i) a well-defined active site exists, (ii) the bound-ligand is a small molecular weight molecule. The database has been screened by an inverse docking tool derived from the GOLD program to recover the known target of four unrelated ligands. Both the database and the inverse screening procedures are accurate enough to rank the true target of the four investigated ligands among the top 1% scorers, with 70-100 fold enrichment with respect to random screening. Applying the proposed screening procedure to a small-sized generic ligand was much less accurate suggesting that inverse screening shall be reserved to rather selective compounds.

A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance

A thorough evaluation of some of the most advanced docking and scoring methods currently available is described, and guidelines for the choice of an appropriate protocol for docking and virtual screening are defined. The generation of a large and highly curated test set of pharmaceutically relevant protein-ligand complexes with known binding affinities is described, and three highly regarded docking programs (Glide, GOLD, and ICM) are evaluated on the same set with respect to their ability to reproduce crystallographic binding orientations. Glide correctly identified the crystallographic pose within 2.0 A in 61% of the cases, versus 48% for GOLD and 45% for ICM. In general Glide appears to perform most consistently with respect to diversity of binding sites and ligand flexibility, while the performance of ICM and GOLD is more binding site-dependent and it is significantly poorer when binding is predominantly driven by hydrophobic interactions. The results also show that energy minimization and reranking of the top N poses can be an effective means to overcome some of the limitations of a given docking function. The same docking programs are evaluated in conjunction with three different scoring functions for their ability to discriminate actives from inactives in virtual screening. The evaluation, performed on three different systems (HIV-1 protease, IMPDH, and p38 MAP kinase), confirms that the relative performance of different docking and scoring methods is to some extent binding site-dependent. GlideScore appears to be an effective scoring function for database screening, with consistent performance across several types of binding sites, while ChemScore appears to be most useful in sterically demanding sites since it is more forgiving of repulsive interactions. Energy minimization of docked poses can significantly improve the enrichments in systems with sterically demanding binding sites. Overall Glide appears to be a safe general choice for docking, while the choice of the best scoring tool remains to a larger extent system-dependent and should be evaluated on a case-by-case basis.

OptiDock: Virtual HTS of Combinatorial Libraries by Efficient Sampling of Binding Modes in Product Space

Products from combinatorial libraries generally share a common core structure that can be exploited to improve the efficiency of virtual high-throughput screening (vHTS). In general, it is more efficient to find a method that scales with the total number of reagents (Sigma growth) rather with the number of products (Pi growth). The OptiDock methodology described herein entails selecting a diverse but representative subset of compounds that span the structural space encompassed by the full library. These compounds are docked individually using the FlexX program (Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. J. Mol. Biol. 1995, 251, 470-489) to define distinct docking modes in terms of reference placements for combinatorial core atoms. Thereafter, substituents in R-cores (consisting of the core structure substituted at a single variation site) are docked, keeping the core atoms fixed at the coordinates dictated by each reference placement. Interaction energies are calculated for each docked R-core with respect to the target protein, and energies for whole compounds are calculated by finding the reference core placement for which the sum of corresponding R-core energies is most negative. The use of diverse whole compounds to define binding modes is a key advantage of the protocol over other combinatorial docking programs. As a result, OptiDock returns better-scoring conformers than does serially applied FlexX. OptiDock is also better able to find a viable docked pose for each library member than are other combinatorial approaches.

The many roles of computation in drug discovery

An overview is given on the diverse uses of computational chemistry in drug discovery. Particular emphasis is placed on virtual screening, de novo design, evaluation of drug-likeness, and advanced methods for determining protein-ligand binding.

Conformational Analysis of Drug-Like Molecules Bound to Proteins: An Extensive Study of Ligand Reorganization upon Binding

This paper describes a large-scale study on the nature and the energetics of the conformational changes drug-like molecules experience upon binding. Ligand strain energies and conformational reorganization were analyzed with different computational methods on 150 crystal structures of pharmaceutically relevant protein-ligand complexes. The common knowledge that ligands rarely bind in their lowest calculated energy conformation was confirmed. Additionally, we found that over 60% of the ligands do not bind in a local minimum conformation. While approximately 60% of the ligands were calculated to bind with strain energies lower than 5 kcal/mol, strain energies over 9 kcal/mol were calculated in at least 10% of the cases regardless of the method used. A clear correlation was found between acceptable strain energy and ligand flexibility, while there was no correlation between strain energy and binding affinity, thus indicating that expensive conformational rearrangements can be tolerated in some cases without overly penalizing the tightness of binding. On the basis of the trends observed, thresholds for the acceptable strain energies of bioactive conformations were defined with consideration of the impact of ligand flexibility. An analysis of the degree of folding of the bound ligands confirmed the general tendency of small molecules to bind in an extended conformation. The results suggest that the unfolding of hydrophobic ligands during binding, which exposes hydrophobic surfaces to contact with protein residues, could be one of the factors accounting for high reorganization energies. Finally, different methods for conformational analysis were evaluated, and guidelines were defined to maximize the prevalence of bioactive conformations in computationally generated ensembles.

Virtual screening with flexible docking and COMBINE-based models. Application to a series of factor Xa inhibitors

A two-step, fully automatic virtual screening procedure consisting of flexible docking followed by activity prediction by COMparative BINding Energy (COMBINE) analysis is presented. This novel approach has been successfully applied, as an example with medicinal chemistry interest, to a recently reported series of 133 factor Xa (fXa)(1) inhibitors whose activities encompass 4 orders of magnitude. The docking algorithm is linked to the COMBINE analysis program and used to derive independent regression models of the 133 inhibitors docked within three different fXa structures (PDB entries 1fjs, 1f0r, and 1xka), so as to explore the effect of receptor conformation on the overall results. Reliable docking conformations and predictive regression models requiring eight latent variables could be derived for two of the fXa structures, with the best model achieving a Q(2) of 0.63 and a standard deviation of errors of prediction (SDEP) of 0.51 (leave-one-out). The two-step procedure was then employed to screen a designed virtual library of 112 ligands, containing both active and inactive compounds. While docking energies alone could show a good performance for selecting hits, including structurally diverse ones, inclusion of COMBINE analysis regression models provided improved rankings for the identification of structurally related molecules in external sets. In our best case, a recognition rate of approximately 80% of known binders at approximately 15% false positives rate was achieved, corresponding to an enrichment factor of approximately 450% over random.

Phosphotyrosyl peptides and analogues as substrates and inhibitors of purple acid phosphatases

Purple acid phosphatases are metal-containing hydrolases. While their precise biological role(s) is unknown, the mammalian enzyme has been linked in a variety of biological circumstances (e.g., osteoporosis) with increased bone resorption. Inhibition of the human enzyme is a possible strategy for the treatment of bone-resorptive diseases such as osteoporosis. Previously, we determined the crystal structure of pig purple acid phosphatase to 1.55A and we showed that it is a good model for the human enzyme. Here, a study of the pH dependence of its kinetic parameters showed that the pig enzyme is most efficient at pH values similar to those encountered in the osteoclast resorptive space. Based on the observation that phosphotyrosine-containing peptides are good substrates for pig purple acid phosphatase, peptides containing a range of phosphotyrosine mimetics were synthesized. Kinetic analysis showed that they act as potent inhibitors of mammalian and plant purple acid phosphatases, with the best inhibitors exhibiting low micromolar inhibition constants at pH 3-5. These compounds are thus the most potent organic inhibitors yet reported for the purple acid phosphatases.

Identification of a minimal subset of receptor conformations for improved multiple conformation docking and two-step scoring

Docking and scoring are critical issues in virtual drug screening methods. Fast and reliable methods are required for the prediction of binding affinity especially when applied to a large library of compounds. The implementation of receptor flexibility and refinement of scoring functions for this purpose are extremely challenging in terms of computational speed. Here we propose a knowledge-based multiple-conformation docking method that efficiently accommodates receptor flexibility thus permitting reliable virtual screening of large compound libraries. Starting with a small number of active compounds, a preliminary docking operation is conducted on a large ensemble of receptor conformations to select the minimal subset of receptor conformations that provides a strong correlation between the experimental binding affinity (e.g., Ki, IC50) and the docking score. Only this subset is used for subsequent multiple-conformation docking of the entire data set of library (test) compounds. In conjunction with the multiple-conformation docking procedure, a two-step scoring scheme is employed by which the optimal scoring geometries obtained from the multiple-conformation docking are re-scored by a molecular mechanics energy function including desolvation terms. To demonstrate the feasibility of this approach, we applied this integrated approach to the estrogen receptor alpha (ERalpha) system for which published binding affinity data were available for a series of structurally diverse chemicals. The statistical correlation between docking scores and experimental values was significantly improved from those of single-conformation dockings. This approach led to substantial enrichment of the virtual screening conducted on mixtures of active and inactive ERalpha compounds.

Three-dimensional models of wild-type and mutated forms of cytochrome P450 14alpha-sterol demethylases from Aspergillus fumigatus and Candida albicans provide insights into posaconazole binding

The cytochrome P450 sterol 14alpha-demethylase enzyme (CYP51) is the target of azole antifungals. Azoles block ergosterol synthesis, and thereby fungal growth, by binding in the active-site cavity of the enzyme and ligating the iron atom of the heme cofactor through a nitrogen atom of the azole. Mutations in and around the CYP51 active site have resulted in azole resistance. In this work, homology models of the CYP51 enzymes from Aspergillus fumigatus and Candida albicans were constructed based on the X-ray crystal structure of CYP51 from Mycobacterium tuberculosis. Using these models, binding modes for voriconazole (VOR), fluconazole (FLZ), itraconazole (ITZ), and posaconazole (POS) were predicted from docking calculations. Previous work had demonstrated that mutations in the vicinity of the heme cofactor had a greater impact on the binding of FLZ and VOR than on the binding of POS and ITZ. Our modeling data suggest that the long side chains of POS and ITZ occupy a specific channel within CYP51 and that this additional interaction, which is not available to VOR and FLZ, serves to stabilize the binding of these azoles to the mutated CYP51 proteins. The model also predicts that mutations that were previously shown to specifically impact POS susceptibility in A. fumigatus and C. albicans act by interfering with the binding of the long side chain.

Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions

Representing and understanding the three-dimensional (3D) structural information of protein-ligand complexes is a critical step in the rational drug discovery process. Traditional analysis methods are proving inadequate and inefficient in dealing with the massive amount of structural information being generated from X-ray crystallography, NMR, and in silico approaches such as structure-based docking experiments. Here, we present SIFt (structural interaction fingerprint), a novel method for representing and analyzing 3D protein-ligand binding interactions. Key to this approach is the generation of an interaction fingerprint that translates 3D structural binding information from a protein-ligand complex into a one-dimensional binary string. Each fingerprint represents the "structural interaction profile" of the complex that can be used to organize, analyze, and visualize the rich amount of information encoded in ligand-receptor complexes and also to assist database mining. We have applied SIFt to tackle three common tasks in structure-based drug design. The first involved the analysis and organization of a typical set of results generated from a docking study. Using SIFt, docking poses with similar binding modes were identified, clustered, and subsequently compared with conventional scoring function information. A second application of SIFt was to analyze approximately 90 known X-ray crystal structures of protein kinase-inhibitor complexes obtained from the Protein Databank. Using SIFt, we were able to organize the structures and reveal striking similarities and diversity between their small molecule binding interactions. Finally, we have shown how SIFt can be used as an effective molecular filter during the virtual chemical library screening process to select molecules with desirable binding mode(s) and/or desirable interaction patterns with the protein target. In summary, SIFt shows promise to fully leverage the wealth of information being generated in rational drug design.

A practical approach to docking of zinc metalloproteinase inhibitors

Forty zinc-dependent metalloproteinase/ligand complexes with known crystal structures were re-docked using five docking/scoring approaches (DOCK, FlexX, DrugScore, GOLD, and AutoDock). Correct geometry of the coordination bonds between the ligand's zinc binding group (ZBG) and the catalytic zinc is important for docking accuracy and scoring reliability. More than 75% of docked poses with RMSD less than 2A were found to have appropriate ZBG binding, but for poor ZBG binding, about 95% of poses failed to dock correctly. Elimination of poses with inappropriate zinc binding resulted in better binding energy predictions that were further improved by dividing the ligands into subsets according to the ZBG (carboxylates, hydroxamates, and phosphorus containing groups). After a subset re-scoring using the regression functions obtained for individual subsets, DrugScore was able to explain 77% and the consensus scoring scheme X-CSCORE even 88% of variance in binding energies. The approach combining ZBG-based pose selection and subset re-scoring improved the hit rate in virtual screening for metalloproteinase inhibitors for all tested methods by 4-16%.

Novel peptidomimetic inhibitors of signal transducer and activator of transcription 3 dimerization and biological activity

The critical role of signal transducer and activator of transcription 3 (Stat3) in the growth and survival of human tumor cells identifies it as a promising target for cancer drug discovery. We previously identified a Stat3 SH2 domain-binding phosphopeptide, PY*LKTK, and its tripeptide derivatives, PY*L and AY*L (where Y* represents phosphotyrosine), which inhibit Stat3 biochemical activity and biological function. Here, we report novel peptidomimetic compounds based on PY*L (or AY*L) with substitution of the Y-1 residue by benzyl, pyridyl, or pyrazinyl derivatives that are selective and greater than 5-fold more potent in disrupting Stat3 activity in vitro than lead tripeptides. The biological activities of these derivatives mirror that originally observed for peptides. In this context, the representative peptidomimetic ISS 610 with 4-cyanobenzoate substitution inhibits constitutive Stat3 activity in Src-transformed mouse fibroblasts and human breast and lung carcinoma cells. This effect is not evident with the non-phosphorylated counterpart, ISS 610NP, consistent with interaction of peptidomimetics with the SH2 domain of Stat3. Moreover, ISS 610 induces cell growth inhibition and apoptosis of Src-transformed fibroblasts that contain persistently active Stat3. We present the first report of a peptidomimetic approach to design of small-molecule inhibitors of Stat3 that are also among the first examples of disruptors of transcription factor dimerization with the potential for novel cancer therapy.

Effects of natural flavones and flavonols on the kinase activity of Cdk5

A number of natural and synthetic flavonoids have been assessed previously with regard to their effects on the activity of cyclin-dependent kinases (Cdk1 and -2) related to the inhibition of cell cycle progression. On the other hand, the Cdk5/p35 system is of major importance in neuronal migration phenomena and brain development, and its deregulation is implicated in neurodegenerative diseases, particularly Alzheimer's. Here we show that some natural flavonoids inhibit the activity of the Cdk5/p35 system in the micromolar range, while others are practically inactive. Ring B-unsubstituted and highly methoxylated flavones were inactive or gave irreproducible results, and 6-methoxyapigenin and 6-methoxyluteolin were the most potent Cdk5 complex inhibitors within this series, while the common flavonols kaempferol and quercetin showed intermediate behavior. The reported crystal structure of the Cdk5 complex with its activator p25 was used for docking studies, which also led to the identification of the two 6-methoxyflavones, kaempferol and quercetin, as well as the untested 6-methoxy derivatives of kaempferol and quercetin and the corresponding 6-hydroxy analogues as compounds exhibiting a good fit to the active site of the enzyme.

1-(2-Naphthyl)-1 H -pyrazole-5-carboxylamides as potent factor Xa inhibitors. Part 3: Design, synthesis and SAR of orally bioavailable benzamidine-P4 inhibitors

Using N,N-dialkylated benzamidines as the novel P4 motifs, we have designed and synthesized a class of 1-(2-naphthyl)-1H-pyrazole-5-carboxylamides as highly potent and selective fXa inhibitors with significantly improved hydrophilicity and in vitro anticoagulant activity. These benzamidine-P4 fXa inhibitors have displayed excellent oral bioavailability and long half-life.

Synthesis, biological activity and molecular modeling studies of novel COX-1 inhibitors

Synthesis of new potential COX-1 and/or COX-2 inhibitors, derivatives of 1,1-di-(3-carboxyphenyl)ethane, their biological activity, docking results on COX-1 enzyme and absorption, distribution, metabolism, excretion (ADME) properties are presented. In addition to known interactions between ketoprofen and ibuprofen, leading NSAID agents and COX-1 active site, the possibility of formation of additional interactions is explored. Interactions with Ala527, and with one of the water molecules situated within the active site are identified. Molecular mechanics and DFT calculations for studied compounds have revealed free rotation around two central bonds (C1-C3' and C1-C3"), making them flexible, thus easier to enter and adjust to the active site. Further modifications of core structure have been undertaken to optimize biological activity and ADME properties. As a result, two of the compounds are indicated as novel COX-1 inhibitors.

GEMDOCK: A generic evolutionary method for molecular docking

We have developed an evolutionary approach for flexible ligand docking. This approval, GEMDOCK, uses a Generic Evolutionary Method for molecular DOCKing and an empirical scoring function. The former combines both discrete and continuous global search strategies with local search strategies to speed up convergence, whereas the latter results in rapid recognition of potential ligands. GEMDOCK was tested on a diverse data set of 100 protein-ligand complexes from the Protein Data Bank. In 79% of these complexes, the docked lowest energy ligand structures had root-mean-square derivations (RMSDs) below 2.0 A with respect to the corresponding crystal structures. The success rate increased to 85% if the structure water molecules were retained. We evaluated GEMDOCK on two cross-docking experiments in which each ligand of a protein ensemble was docked into each protein of the ensemble. Seventy-six percent of the docked structures had RMSDs below 2.0 A when the ligands were docked into foreign structures. We analyzed and validated GEMDOCK with respect to various search spaces and scoring functions, and found that if the scoring function was perfect, then the predicted accuracy was also essentially perfect. This study suggests that GEMDOCK is a useful tool for molecular recognition and may be used to systematically evaluate and thus improve scoring functions.

Comparing the Quality and Predictiveness between 3D QSAR Models Obtained from Manual and Automated Alignment

A set of 113 flexible cyclic urea inhibitors of human immunodeficiency virus protease (HIV-1 PR) was used to compare the quality and predictive power of CoMFA and CoMSIA models for manually or automatically aligned inhibitor set. Inhibitors that were aligned automatically with molecular docking were in agreement with information obtained from existing X-ray structures. Both alignment methods produced statistically significant CoMFA and CoMSIA models, with the best q(2) value being 0.649 and the best predictive r(2) being 0.754. The manual alignment gave statistically higher values, whereas the automated alignment gave more robust models for predicting the activities of an external inhibitor set. Both models utilized similar amino acids in the HIV-1 PR active site, supporting the idea that hydrogen bonds form between an inhibitor and the backbone carbonyl oxygens of Gly48 and Gly48' and also the backbone NH group of Asp30, Gly48, Asp29', and Gly48' of the enzyme. These results suggest that an automated inhibitor alignment can yield predictive 3D QSAR models that are well comparable to manual methods. Thus, an automated alignment method in creating 3D QSAR models is encouragable when a well-characterized structure of the target protein is available.

HierVLS hierarchical docking protocol for virtual ligand screening of large-molecule databases

To provide practical means for rapidly scanning the extensive experimental combinatorial chemistry libraries now available for high-throughput screening (HTS), it is essential to establish computational virtual ligand screening (VLS) techniques to rapidly identify out of a large library all active compounds against a particular protein target. Toward this goal we developed HierVLS, a fast hierarchical docking approach that starts with a coarse grain conformational search over a large number of configurations filtered with a fast but crude energy function, followed by a succession of finer grain levels, using successively more accurate but more expensive descriptions of the ligand-protein-solvent interactions to filter successively fewer cases. The final step of this procedure optimizes one configuration of the ligand in the protein site using our most accurate energy expression and description of the solvent, which would be impractical for all conformations and sites sampled in the coarse level. HierVLS is based on the HierDock approach, but rather than allowing an hour or more to determine the best binding site and energy for each ligands (as in HierDock), we have adapted our procedure so that it can lead to reliable results while using only 4 min (866 MHz Pentium III processor) per ligand. To validate the accuracy for HierVLS to predict the experimentally observed binding conformation, we considered 37 cocrystal structures comprising 11 target proteins. We find that HierVLS identifies the correct binding mode for all 37 cocrystals. In addition, the calculated binding energies correlate well with available experimental binding constants. To validate how well HierVLS can identify the correct ligand in an extensive library of decoys, we considered a library of over 10 000 molecules. HierVLS identifies 26 out of the 37 cases in the top 2% ranked by binding affinity among the 10 037 molecules. The failures result from either metal-containing sites on the protein or water-mediated ligand-protein interactions, which we anticipate can be solved within the constraints of practical VLS. We then applied HierVLS to screen a 55000-compound virtual library against the target protein-tyrosine phosphatase 1B (ptp1b). The top 250 compounds by binding affinity included all six ptp1b cocrystal ligands added to the library plus three other experimentally confirmed binders. The best (top 1) binder is an experimentally confirmed positive. We conclude that HierVLS is useful for selecting leads for a particular target out of large combinatorial databases.

Multiple active site corrections for docking and virtual screening

Several docking programs are now available that can reproduce the bound conformation of a ligand in an active site, for a wide variety of experimentally determined complexes. However, these programs generally perform less well at ranking multiple possible ligands in one site. Since accurate identification of potential ligands is a prerequisite for many aspects of structure-based drug design, this is a serious limitation. We have tested the ability of two docking programs, FlexX and Gold, to match ligands and active sites for multiple complexes. We show that none of the docking scores from either program are able to match consistently ligands and active sites in our tests. We propose a simple statistical correction, the multiple active site correction (MASC), which greatly ameliorates this problem. We have also tested the correction method against an extended set of 63 cocrystals and in a virtual screening experiment. In all cases, MASC significantly improves the results of the docking experiments.

Differential inhibition of adenylyl cyclase isoforms and soluble guanylyl cyclase by purine and pyrimidine nucleotides

Mammals express nine membranous adenylyl cyclase isoforms (ACs 1-9), a structurally related soluble guanylyl cyclase (sGC) and a soluble AC (sAC). Moreover, Bacillus anthracis and Bacillus pertussis produce the AC toxins, edema factor (EF), and adenylyl cyclase toxin (ACT), respectively. 2'(3')-O-(N-methylanthraniloyl)-guanosine 5'-[gamma-thio]triphosphate is a potent competitive inhibitor of AC in S49 lymphoma cell membranes. These data prompted us to study systematically the effects of 24 nucleotides on AC in S49 and Sf9 insect cell membranes, ACs 1, 2, 5, and 6, expressed in Sf9 membranes and purified catalytic subunits of membranous ACs (C1 of AC5 and C2 of AC2), sAC, sGC, EF, and ACT in the presence of MnCl(2). N-Methylanthraniloyl (MANT)-GTP inhibited C1.C2 with a K(i) of 4.2 nm. Phe-889 and Ile-940 of C2 mediate hydrophobic interactions with the MANT group. MANT-inosine 5'-[gamma-thio]triphosphate potently inhibited C1.C2 and ACs 1, 5, and 6 but exhibited only low affinity for sGC, EF, ACT, and G-proteins. Inosine 5'-[gamma-thio]triphosphate and uridine 5'-[gamma-thio]triphosphate were mixed G-protein activators and AC inhibitors. AC5 was up to 15-fold more sensitive to inhibitors than AC2. EF and ACT exhibited unique inhibitor profiles. At sAC, 2',5'-dideoxyadenosine 3'-triphosphate was the most potent compound (IC(50), 690 nm). Several MANT-adenine and MANT-guanine nucleotides inhibited sGC with K(i) values in the 200-400 nm range. UTP and ATP exhibited similar affinities for sGC as GTP and were mixed sGC substrates and inhibitors. The exchange of MnCl(2) against MgCl(2) reduced inhibitor potencies at ACs and sGC 1.5-250-fold, depending on the nucleotide and cyclase studied. The omission of the NTP-regenerating system from cyclase reactions strongly reduced the potencies of MANT-ADP, indicative for phosphorylation to MANT-ATP by pyruvate kinase. Collectively, AC isoforms and sGC are differentially inhibited by purine and pyrimidine nucleotides.

Dimers of d-myo-Inositol 1,4,5-Trisphosphate: Design, Synthesis, and Interaction with Ins(1,4,5)P3 Receptors

The design and synthesis of dimeric versions of the intracellular signaling molecule d-myo-inositol 1,4,5-trisphosphate [Ins(1,4,5)P(3)] are reported. Ins(1,4,5)P(3) dimers in a range of sizes were constructed by conjugation of a partially protected 2-O-(2-aminoethyl)-Ins(1,4,5)P(3) intermediate with activated oligo- and poly(ethylene glycol) (PEG) tethers, to give benzyl-protected dimers with amide or carbamate linkages. After deprotection, the resulting water-soluble Ins(1,4,5)P(3) dimers were purified by ion-exchange chromatography. The interaction of the Ins(1,4,5)P(3) dimers with tetrameric Ins(1,4,5)P(3) receptors was explored, using equilibrium [(3)H]Ins(1,4,5)P(3)-binding to membranes from cerebellum, and (45)Ca(2+)-release from permeabilized hepatocytes. The results showed that dimers, even when they incorporate large PEG tethers, interact potently with Ins(1,4,5)P(3) receptors, and that the shorter dimers are more potent than Ins(1,4,5)P(3) itself. A very small dimer, consisting of two Ins(1,4,5)P(3) motifs joined by a short N,N'-diethylurea spacer, was synthesized. Preliminary studies of (45)Ca(2+) release from the intracellular stores of permeabilized hepatocytes showed this shortest dimer to be almost as potent as adenophostin A, the most potent Ins(1,4,5)P(3) receptor ligand known. Possible interpretations of this result are considered in relation to the recently disclosed X-ray crystal structure of the type 1 Ins(1,4,5)P(3) receptor core binding domain.

Active site mutations and substrate inhibition in human sulfotransferase 1A1 and 1A3

Human SULT1A1 is primarily responsible for sulfonation of xenobiotics, including the activation of promutagens, and it has been implicated in several forms of cancer. Human SULT1A3 has been shown to be the major sulfotransferase that sulfonates dopamine. These two enzymes shares 93% amino acid sequence identity and have distinct but overlapping substrate preferences. The resolution of the crystal structures of these two enzymes has enabled us to elucidate the mechanisms controlling their substrate preferences and inhibition. The presence of two p-nitrophenol (pNP) molecules in the crystal structure of SULT1A1 was postulated to explain cooperativity at low and inhibition at high substrate concentrations, respectively. In SULT1A1, substrate inhibition occurs with pNP as the substrate but not with dopamine. For SULT1A3, substrate inhibition is found for dopamine but not with pNP. We investigated how substrate inhibition occurs in these two enzymes using molecular modeling, site-directed mutagenesis, and kinetic analysis. The results show that residue Phe-247 of SULT1A1, which interacts with both p-nitrophenol molecules in the active site, is important for substrate inhibition. Mutation of phenylalanine to leucine at this position in SULT1A1 results in substrate inhibition by dopamine. We also propose, based on modeling and kinetic studies, that substrate inhibition by dopamine in SULT1A3 is caused by binding of two dopamine molecules in the active site.

Synthesis and Target Identification of Hymenialdisine Analogs

Hymenialdisine (HMD) is a sponge-derived natural product kinase inhibitor with nanomolar activity against CDKs, Mek1, GSK3beta, and CK1 and micromolar activity against Chk1. In order to explore the broader application of the pyrrolo[2,3-c]azepine skeleton of HMD as a general kinase inhibitory scaffold, we searched for additional protein targets using affinity chromatography in conjunction with the synthesis of diverse HMD analogs and profiled HMD against a panel of 60 recombinant enzymes. This effort has led to nanomolar to micromolar inhibitors of 11 new targets including p90RSK, KDR, c-Kit, Fes, MAPK1, PAK2, PDK1, PKCtheta, PKD2, Rsk1, and SGK. The synthesis of HMD analogs has resulted in the identification of compounds with enhanced and/or dramatically altered selectivities relative to HMD (28n) and in molecules with antiproliferative activities 30-fold higher than HMD (28p).

Improving structure-based virtual screening by multivariate analysis of scoring data

Three different multivariate statistical methods, PLS discriminant analysis, rule-based methods, and Bayesian classification, have been applied to multidimensional scoring data from four different target proteins: estrogen receptor alpha (ERalpha), matrix metalloprotease 3 (MMP3), factor Xa (fXa), and acetylcholine esterase (AChE). The purpose was to build classifiers able to discriminate between active and inactive compounds, given a structure-based virtual screen. Seven different scoring functions were used to generate the scoring matrices. The classifiers were compared to classical consensus scoring and single scoring functions. The classifiers show a superior performance, with rule-based methods being most effective. The precision of correctly predicting an active compound is about 90% for three of the targets and about 25% for acetylcholine esterase. On the basis of these results, a new two-stage approach is suggested for structure-based virtual screening where limited activity information is available.

Shape signatures: a new approach to computer-aided ligand- and receptor-based drug design

A unifying principle of rational drug design is the use of either shape similarity or complementarity to identify compounds expected to be active against a given target. Shape similarity is the underlying foundation of ligand-based methods, which seek compounds with structure similar to known actives, while shape complementarity is the basis of most receptor-based design, where the goal is to identify compounds complementary in shape to a given receptor. These approaches can be extended to include molecular descriptors in addition to shape, such as lipophilicity or electrostatic potential. Here we introduce a new technique, which we call shape signatures, for describing the shape of ligand molecules and of receptor sites. The method uses a technique akin to ray-tracing to explore the volume enclosed by a ligand molecule, or the volume exterior to the active site of a protein. Probability distributions are derived from the ray-trace, and can be based solely on the geometry of the reflecting ray, or may include joint dependence on properties, such as the molecular electrostatic potential, computed over the surface. Our shape signatures are just these probability distributions, stored as histograms. They converge rapidly with the length of the ray-trace, are independent of molecular orientation, and can be compared quickly using simple metrics. Shape signatures can be used to test for both shape similarity between compounds and for shape complementarity between compounds and receptors and thus can be applied to problems in both ligand- and receptor-based molecular design. We present results for comparisons between small molecules of biological interest and the NCI Database using shape signatures under two different metrics. Our results show that the method can reliably extract compounds of shape (and polarity) similar to the query molecules. We also present initial results for a receptor-based strategy using shape signatures, with application to the design of new inhibitors predicted to be active against HIV protease.

Evaluation of Docking Performance: Comparative Data on Docking Algorithms

Docking molecules into their respective 3D macromolecular targets is a widely used method for lead optimization. However, the best known docking algorithms often fail to position the ligand in an orientation close to the experimental binding mode. It was reported recently that consensus scoring enhances the hit rates in a virtual screening experiment. This methodology focused on the top-ranked pose, with the underlying assumption that the orientation/conformation of the docked compound is the most accurate. In an effort to eliminate the scoring function bias, and assess the ability of the docking algorithms to provide solutions similar to the crystallographic modes, we investigated the most known docking programs and evaluated all of the resultant poses. We present the results of an extensive computational study in which five docking programs (FlexX, DOCK, GOLD, LigandFit, Glide) were investigated against 14 protein families (69 targets). Our findings show that some algorithms perform consistently better than others, and a correspondence between the nature of the active site and the best docking algorithm can be found.

New fascaplysin-based CDK4-specific inhibitors: design, synthesis and biological activity

The first biologically active non-planar analogues of the toxic anti-cancer agent, fascaplysin, have been produced; we present the design, synthesis and biological activity of three tryptamine derivatives.

Predicting protein–ligand binding affinities: a low scoring game?

We have investigated the performance of five well known scoring functions in predicting the binding affinities of a diverse set of 205 protein-ligand complexes with known experimental binding constants, and also on subsets of mutually similar complexes. We have found that the overall performance of the scoring functions on the diverse set is disappointing, with none of the functions achieving r(2) values above 0.32 on the whole dataset. Performance on the subsets was mixed, with four of the five functions predicting fairly well the binding affinities of 35 proteinases, but none of the functions producing any useful correlation on a set of 38 aspartic proteinases. We consider two algorithms for producing consensus scoring functions, one based on a linear combination of scores from the five individual functions and the other on averaging the rankings produced by the five functions. We find that both algorithms produce consensus functions that generally perform slightly better than the best individual scoring function on a given dataset.

Characterization of the CYP2C8 Active Site by Homology Modeling

To compare the features of the active sites of CYP2C8, CYP2C9, and CYP2C19, homology modeling was performed based on the crystallographic coordinates of mammalian CYP2C5. It was found that CYP2C8 has a much larger pocket than the other forms due to the existence of an additional pocket. The approach to the additional pocket is comprised of Ile102, Ser114, Leu208, Val366, and Ile476, and the side chains of Ser114, Val366, and Ile476, which are smaller than the corresponding residues in the other CYPs, enable access to the pocket. The general features of the active site in the CYP2C8 model are similar to those of the previously constructed CYP3A4 model, which may account for the 2 CYPs sharing some of their substrates. The CYP2C8 model was validated by examining the bound orientation of paclitaxel and showing that it is consistent with the formation of the 6-beta hydroxylated derivative during metabolism. Docked paclitaxel was found to form a hydrogen bond with the side chain of Asn 99, which is a characteristic residue of CYP2C8 and is located in the additional pocket. Descriptors for CYP2C8 and CYP2C9 substrates were also examined with the molecular operating environment (MOE). The descriptor by which CYP2C8 and CYP2C9 substrates were classified most distinctly was found to be molar refractivity, which might be related to the longer shape and more polar nature of the active site of CYP2C8 in the CYP2C subfamily.

Fully quantum mechanical energy optimization for protein-ligand structure

We present a quantum mechanical approach to study protein-ligand binding structure with application to a Adipocyte lipid-binding protein complexed with Propanoic Acid. The present approach employs a recently develop molecular fractionation with a conjugate caps (MFCC) method to compute protein-ligand interaction energy and performs energy optimization using the quasi-Newton method. The MFCC method enables us to compute fully quantum mechanical ab initio protein-ligand interaction energy and its gradients that are used in energy minimization. This quantum optimization approach is applied to study the Adipocyte lipid-binding protein complexed with Propanoic Acid system, a complex system consisting of a 2057-atom protein and a 10-atom ligand. The MFCC calculation is carried out at the Hartree-Fock level with a 3-21G basis set. The quantum optimized structure of this complex is in good agreement with the experimental crystal structure. The quantum energy calculation is implemented in a parallel program that dramatically speeds up the MFCC calculation for the protein-ligand system. Similarly good agreement between MFCC optimized structure and the experimental structure is also obtained for the streptavidin-biotin complex. Due to heavy computational cost, the quantum energy minimization is carried out in a six-dimensional space that corresponds to the rigid-body protein-ligand interaction.

An efficient molecular docking using conformational space annealing

Molecular docking falls into the general category of global optimization problems because its main purpose is to find the most stable complex consisting of a receptor and its ligand. Conformational space annealing (CSA), a powerful global optimization method, is incorporated with the Tinker molecular modeling package to perform molecular docking simulations of six receptor-ligand complexes (3PTB, 1ULB, 2CPP, 1STP, 3CPA, and 1PPH) from the Protein Data Bank. In parallel, Monte Carlo with the minimization (MCM) method is also incorporated into the Tinker package for comparison. The energy function, consisting of electrostatic interactions, van der Waals interactions, and torsional energy terms, is calculated using the AMBER94 all-atom empirical force field. Rigid docking simulations for all six complexes and flexible docking simulations for three complexes (1STP, 3CPA, and 1PPH) are carried out using the CSA and the MCM methods. The simulation results show that the docking procedures using the CSA method generally find the most stable complexes as well as the native-like complexes more efficiently and accurately than those using the MCM, demonstrating that CSA is a promising search method for molecular docking problems.

Molecular modelling of fentanyl analogs

Fentanyl is a highly potent and clinically widely used narcotic analgesic. A large number of its analogs have been synthesized, some of which (sufentanil and alfentanyl) are also in clinical use. Theoretical studies, in recent years, afforded a better understanding of the structure-activity relationships of this class of opiates and allowed insight into the molecular mechanism of the interactions of fentanyl analogs with their receptors. An overview of the current computational techniques for modeling fentanyl analogs, their receptors and ligand-receptor interactions is presented in this paper.

Development and evaluation of a generic evolutionary method for protein-ligand docking

We have developed a generic evolutionary method with an empirical scoring function for the protein-ligand docking, which is a problem of paramount importance in structure-based drug design. This approach, referred to as the GEMDOCK (Generic Evolutionary Method for molecular DOCKing), combines both continuous and discrete search mechanisms. We tested our approach on seven protein-ligand complexes, and the docked lowest energy structures have root-mean-square derivations ranging from 0.32 to 0.99 A with respect to the corresponding crystal ligand structures. In addition, we evaluated GEMDOCK on crossdocking experiments, in which some complexes with an identical protein used for docking all crystallized ligands of these complexes. GEMDOCK yielded 98% docked structures with RMSD below 2.0 A when the ligands were docked into foreign protein structures. We have reported the validation and analysis of our approach on various search spaces and scoring functions. Experimental results show that our approach is robust, and the empirical scoring function is simple and fast to recognize compounds. We found that if GEMDOCK used the RMSD scoring function, then the prediction accuracy was 100% and the docked structures had RMSD below 0.1 A for each test system. These results suggest that GEMDOCK is a useful tool, and may systematically improve the forms and parameters of a scoring function, which is one of major bottlenecks for molecular recognition.

A pharmacophore docking algorithm and its application to the cross-docking of 18 HIV-NNRTI's in their binding pockets

The docking of small molecules into the binding site of a target protein is an important but difficult step in structure-based drug design. The performance of a docking algorithm is usually evaluated by re-docking ligands into their native binding sites. We have explored the cross-docking of 18 HIV-NNRTIs (non-nucleoside inhibitors of HIV reverse transcriptase) of which the ligand-protein structure has been determined: each of the 18 ligands was docked into each of the 18 binding sites. The docking algorithms studied are an energy-based simulated annealing algorithm and a novel pharmacophore docking algorithm. It turns out that the energy-based docking of the ligands into non-native pockets is far less successful than the docking into their native pockets. The results can be improved by using explicit pharmacophore information, and by docking a ligand into a panel of protein structures and selecting the ligand-protein combination with the lowest interaction energy as the final result.

Design, Synthesis, Molecular Modeling Studies, and Calpain Inhibitory Activity of Novel α-Ketoamides Incorporating Polar Residues at the P1‘-Position

A series of novel alpha-ketoamides incorporating stereoisomeric residues with different electronic properties at the P(1)'-position were synthesized to study the electronic requirements for inhibitor binding to the S(1)'-subsite of calpain I. The results of the study suggested the presence of an acidic amino acid residue at the S(1)'-subsite of calpain I. For example, ester 1a (Cbz-l-Leu-l-Phe-CO-d-Phe-OMe) was over 450-fold more potent than its carboxylic acid derivative 2a (Cbz-l-Leu-l-Phe-CO-d-Phe-OH). Additionally, amidino derivative 3a (Cbz-l-Leu-l-Phe-CONH-d-CH[C(NH)NH(2)]Bn) was about 6000-fold more potent than 2a. Furthermore, 4a (Cbz-l-Leu-l-Phe-CONHCH(2)Bn) was 12-fold less potent than its aza analogue 4b (Cbz-l-Leu-l-Phe-CONHNHBn). The results are consistent with the presence of an acidic amino acid residue at the S(1)'-subsite of calpain I. The acidic amino acid residue was found to be Glu261 via molecular modeling studies.

Lessons in Molecular Recognition: The Effects of Ligand and Protein Flexibility on Molecular Docking Accuracy

The key to success for computational tools used in structure-based drug design is the ability to accurately place or "dock" a ligand in the binding pocket of the target of interest. In this report we examine the effect of several factors on docking accuracy, including ligand and protein flexibility. To examine ligand flexibility in an unbiased fashion, a test set of 41 ligand-protein cocomplex X-ray structures were assembled that represent a diversity of size, flexibility, and polarity with respect to the ligands. Four docking algorithms, DOCK, FlexX, GOLD, and CDOCKER, were applied to the test set, and the results were examined in terms of the ability to reproduce X-ray ligand positions within 2.0A heavy atom root-mean-square deviation. Overall, each method performed well (>50% accuracy) but for all methods it was found that docking accuracy decreased substantially for ligands with eight or more rotatable bonds. Only CDOCKER was able to accurately dock most of those ligands with eight or more rotatable bonds (71% accuracy rate). A second test set of structures was gathered to examine how protein flexibility influences docking accuracy. CDOCKER was applied to X-ray structures of trypsin, thrombin, and HIV-1-protease, using protein structures bound to several ligands and also the unbound (apo) form. Docking experiments of each ligand to one "average" structure and to the apo form were carried out, and the results were compared to docking each ligand back to its originating structure. The results show that docking accuracy falls off dramatically if one uses an average or apo structure. In fact, it is shown that the drop in docking accuracy mirrors the degree to which the protein moves upon ligand binding.

Parallel Solid-Phase Synthesis and Evaluation of Inhibitors of Streptomyces coelicolor Type II Dehydroquinase

A series of 1-substituted and 4-substituted benzyl analogues of the known inhibitor (1S,3R,4R)-1,3,4-trihydroxy-5-cyclohexene-1-carboxylic acid has been synthesized and tested as inhibitors of Streptomyces coelicolor type II dehydroquinase. The solid-phase syntheses of 18 new analogues are reported. The most potent inhibitor, 2-nitrobenzyloxy analogue 5i, has K(i) of 8 microM, more than 30 times lower than the K(M) of the substrate and approximately 4 times more potent than the original inhibitor. The binding modes of the synthesized analogues in the active site were studied by molecular docking with GOLD 2.0.