Comparison of the inhibition of Escherichia coli and Lactobacillus casei dihydrofolate reductase by 2,4-diamino-5-(substituted-benzyl)pyrimidines: quantitative structure-activity relationships, X-ray crystallography, and computer graphics in structure-activity analysis

Rational Design of Novel Allosteric Dihydrofolate Reductase Inhibitors Showing Antibacterial Effects on Drug-Resistant Escherichia coli Escape Variants

In drug discovery, systematic variations of substituents on a common scaffold and bioisosteric replacements are often used to generate diversity and obtain molecules with better biological effects. However, this could saturate the small-molecule diversity pool resulting in drug resistance. On the other hand, conventional drug discovery relies on targeting known pockets on protein surfaces leading to drug resistance by mutations of critical pocket residues. Here, we present a two-pronged strategy of designing novel drugs that target unique pockets on a protein's surface to overcome the above problems. Dihydrofolate reductase, DHFR, is a critical enzyme involved in thymidine and purine nucleotide biosynthesis. Several classes of compounds that are structural analogues of the substrate dihydrofolate have been explored for their antifolate activity. Here, we describe 10 novel small-molecule inhibitors of Escherichia coli DHFR, EcDHFR, belonging to the stilbenoid, deoxybenzoin, and chalcone family of compounds discovered by a combination of pocket-based virtual ligand screening and systematic scaffold hopping. These inhibitors show a unique uncompetitive or noncompetitive inhibition mechanism, distinct from those reported for all known inhibitors of DHFR, indicative of binding to a unique pocket distinct from either substrate or cofactor-binding pockets. Furthermore, we demonstrate that rescue mutants of EcDHFR, with reduced affinity to all known classes of DHFR inhibitors, are inhibited at the same concentration as the wild-type. These compounds also exhibit antibacterial activity against E. coli harboring the drug-resistant variant of DHFR. This discovery is the first report on a novel class of inhibitors targeting a unique pocket on EcDHFR.

Ligand binding studies, preliminary structure-activity relationship and detailed mechanistic characterization of 1-phenyl-6,6-dimethyl-1,3,5-triazine-2,4-diamine derivatives as inhibitors of Escherichia coli dihydrofolate reductase

Gram-negative bacteria are implicated in the causation of life-threatening hospital-acquired infections. They acquire rapid resistance to multiple drugs and available antibiotics. Hence, there is the need to discover new antibacterial agents with novel scaffolds. For the first time, this study explores the 1,3,5-triazine-2,4-diamine and 1,2,4-triazine-2,4-diamine group of compounds as potential inhibitors of Escherichia coli DHFR, a pivotal enzyme in the thymidine and purine synthesis pathway. Using differential scanning fluorimetry, DSF, fifteen compounds with various substitutions on either the 3rd or 4th positions on the benzene group of 6,6-dimethyl-1-(benzene)-1,3,5-triazine-2,4-diamine were shown to bind to the enzyme with varying affinities. Then, the dose dependence of inhibition by these compounds was determined. Preliminary quantitative structure-activity relationship analysis and docking studies implicate the alkyl linker group and the sulfonyl fluoride group in increasing the potency of inhibition. 4-[4-[3-(4,6-diamino-2,2-dimethyl-1,3,5-triazin-1-yl)phenyl]butyl]benzenesulfonyl fluoride (NSC120927), the best hit from the study and a molecule with no reported inhibition of E. coli DHFR, potently inhibits the enzyme with a Ki value of 42.50 ± 5.34 nM, followed by 4-[6-[4-(4,6-diamino-2,2-dimethyl-1,3,5-triazin-1-yl)phenyl]hexyl]benzenesulfonyl fluoride (NSC132279), with a Ki value of 100.9 ± 12.7 nM. Detailed kinetic characterization of the inhibition brought about by five small-molecule hits shows that these inhibitors bind to the dihydrofolate binding site with preferential binding to the NADPH-bound binary form of the enzyme. Furthermore, in search of novel diaminotriazine scaffolds, it is shown that lamotrigine, a 1,2,4-triazine-3,5-diamine and a sodium-ion channel blocker class of antiepileptic drug, also inhibits E. coli DHFR. This is the first comprehensive study on the binding and inhibition brought about by diaminotriazines of a gram-negative prokaryotic enzyme and provides valuable insights into the SAR as an aid to the discovery of novel antibiotics.

CoMFA analysis of tgDHFR and rlDHFR based on antifolates with 6-5 fused ring system using the all-orientation search (AOS) routine and a modified cross-validated r(2)-guided region selection (q(2)-GRS) routine and its initial application

We report the development of CoMFA analysis models that correlate the 3D chemical structures of 80 compounds with 6-5 fused ring system synthesized in our laboratory and their inhibitory potencies against tgDHFR and rlDHFR. In addition to conventional CoMFA analysis, we used two routines available in the literature aimed at the optimization of CoMFA: all-orientation search (AOS) and cross-validated r(2)-guided region selection (q(2)-GRS) to further optimize the models. During this process, we identified a problem associated with q(2)-GRS routine and modified using two strategies. Thus, for the inhibitory activity against each enzyme (tgDHFR and rlDHFR), five CoMFA models were developed using the conventional CoMFA, AOS optimized CoMFA, the original q(2)-GRS optimized CoMFA and the modified q(2)-GRS optimized CoMFA using the first and the second strategy. In this study, we demonstrate that the modified q(2)-GRS routines are superior to the original routine. On the basis of the steric contour maps of the models, we designed four new compounds in the 2,4-diamino-5-methyl-6-phenylsulfanyl-substituted pyrrolo[2,3-d]pyrimidine series. As predicted, the new compounds were potent and selective inhibitors of tgDHFR. One of them, 2,4-diamino-5-methyl-6-(2',6'-dimethylphenylthio)pyrrolo[2,3-d]pyrimidine, is the first 6-5 fused ring system compound with nanomolar tgDHFR inhibitory activity. The HCl salt of this compound was also prepared to increase solubility. Both forms of the drug were tested in vivo in a Toxoplasma gondii infection mouse model. The results indicate that both forms were active with the HCl salt significantly more potent than the free base.

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.

Identifying Biologically Active Compound Classes Using Phenotypic Screening Data and Sampling Statistics

Scoring the activity of compounds in phenotypic high-throughput assays presents a unique challenge because of the limited resolution and inherent measurement error of these assays. Techniques that leverage the structural similarity of compounds within an assay can be used to improve the hit-recovery rate from screening data. A technique is presented that uses clustering and sampling statistics to predict likely compound activity by scoring entire structural classes. A set of phenotypic assays performed against a commercially available compound library was used as a test set. Using the class-scoring technique, the resultant activity prediction scores were more reproducible than individual assay measurements, and class scoring recovered known active compounds more efficiently than individual assay measurements because class scoring had fewer false positives. Known biologically active compounds were recovered 87% of the time using class scores, suggesting a low false-negative rate that compared well to individual assay measurements. In addition, many weak and potentially novel classes of active compounds, overlooked by individual assay measurements, were suggested.

Electrophilicity as a possible descriptor for toxicity prediction

Electrophilicity is one of the cardinal chemical reactivity descriptors successfully employed in various molecular reactivity studies within a structure-activity relationship parlance. The applications of this quantity in the modeling of toxicological properties have inspired us to perform a more exhaustive study in order to test and/or to validate the application of electrophilicity in assessing its chemical and toxicological potential. For this reason the toxicity of a large data set of molecules comprising 252 aliphatic compounds on the Tetrahymena pyriformis is studied. A quantitative structure-activity relationship analysis enabled us to model toxicity in terms of global and local electrophilicities, which provide a reasonably good prediction of aliphatic toxicity. It is heartening to note that the global and local electrophilicity values together can explain the toxicity of a large variety of aliphatic compounds nicely without resorting to any other descriptor or other microscopic/macroscopic physicochemical properties as is the situation in all other QSAR studies.

CoMFA and CoMSIA Analyses of Pneumocystis carinii Dihydrofolate Reductase, Toxoplasma gondii Dihydrofolate Reductase, and Rat Liver Dihydrofolate Reductase

In a continuing effort to develop potent and selective dihydrofolate reductase (DHFR) inhibitors against opportunistic pathogens, we developed three-dimensional quantitative structure-activity relationship (3D QSAR) models for the inhibitory activity against Pneumocystis carinii (pc) DHFR, Toxoplasma gondii (tg) DHFR, and rat liver DHFR, using a data set of 179 structurally diverse compounds. To ensure a balanced distribution of more potent and less potent drugs in the training set, three different 90-compound training sets taken from the main data set were used, one for each enzyme, while the remaining 89 compounds in the main data set in each case were used as the test set. Three methods, namely, conventional CoMFA, all orientation search (AOS) CoMFA, and CoMSIA were applied to the training sets. While the AOS CoMFA models gave the best internal predictions (cross-validated r(2) values from the training sets), which are satisfactory, CoMSIA models gave the best external predictions (predictive r(2) values from the test sets). Both AOS CoMFA and CoMSIA analyses were used to construct stdev*coefficient contour maps which can be used to design new compounds in an interactive fashion.

A comparison of methods for modeling quantitative structure-activity relationships

A large number of methods are available for modeling quantitative structure-activity relationships (QSAR). We examine the predictive accuracy of several methods applied to data sets of inhibitors for angiotensin converting enzyme, acetylcholinesterase, benzodiazepine receptor, cyclooxygenase-2, dihydrofolate reductase, glycogen phosphorylase b, thermolysin, and thrombin. Descriptors calculated with CoMFA, CoMSIA, EVA, HQSAR, and traditional 2D and 2.5D descriptors were used for developing models with partial least squares (PLS). In addition, the genetic function approximation algorithm, genetic PLS, and back-propagation neural networks were used for deriving models from 2.5D descriptors (i.e., 2D descriptors and 3D descriptors calculated from CORINA structures and Gasteiger-Marsili charges). Predictive accuracy was assessed using designed test sets. It was found that HQSAR generally performs as well as CoMFA and CoMSIA; other descriptor sets performed less well. When 2.5D descriptors were used, only neural network ensembles were found to be similarly or more predictive than PLS models. In addition, we show that many cross-validation procedures yield similar estimates of the interpolative accuracy of methods. However, the lack of correspondence between cross-validated and test set predictive accuracy for four sets underscores the benefit of using designed test sets.

Development of neural network QSPR models for Hansch substituent constants. 2. Applications in QSAR studies of HIV-1 reverse transcriptase and dihydrofolate reductase inhibitors

In this paper, the applications of a Hansch substituent constant predictor(1) to Quantitative Structure-Activity Relationships (QSAR) studies of E. coli dihydrofolate reductase (DHFR) inhibitors 2,4-diamino-5-(substituted-benzyl)pyrimidines as well as HIV-1 reverse transcriptase (RT) inhibitors 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT) derivatives are demonstrated. Both data sets contain functional groups for which the substituent constants (pi, MR, F and R) could not be found in standard substituent constant tables. The substituent constant predictor allowed us to derive predicted pi, MR, F and R values for all substituents in both data sets, thus enabling the generation of easily interpretable QSAR models of comparable or better predictivity than previous models.

Prediction of dihydrofolate reductase inhibition and selectivity using computational neural networks and linear discriminant analysis

A data set of 345 dihydrofolate reductase inhibitors was used to build QSAR models that correlate chemical structure and inhibition potency for three types of dihydrofolate reductase (DHFR): rat liver (rl), Pneumocystis carinii (pc), and Toxoplasma gondii (tg). Quantitative models were built using subsets of molecular structure descriptors being analyzed by computational neural networks. Neural network models were able to accurately predict log IC(50) values for the three types of DHFR to within +/-0.65 log units (data sets ranged approximately 5.5 log units) of the experimentally determined values. Classification models were also constructed using linear discriminant analysis to identify compounds as selective or nonselective inhibitors of bacterial DHFR (pcDHFR and tgDHFR) relative to mammalian DHFR (rlDHFR). A leave-N-out training procedure was used to add robustness to the models and to prove that consistent results could be obtained using different training and prediction set splits. The best linear discriminant analysis (LDA) models were able to correctly predict DHFR selectivity for approximately 70% of the external prediction set compounds. A set of new nitrogen and oxygen-specific descriptors were developed especially for this data set to better encode structural features, which are believed to directly influence DHFR inhibition and selectivity.

Validation of DAPPER for 3D QSAR: conformational search and chirality metric

Adequate conformational searching of small molecules and inclusion of a chirality identifier are necessary features of any current technique for quantitative structure-activity relationships (QSAR). However, implementation of these features can be difficult and computationally expensive, and some techniques can still lead to insufficient treatment of molecular conformation. We select the standard systematic conformational search as the default search method for our recent 3D QSAR program, DAPPER, and develop a novel chirality metric for use in QSAR. These techniques are implemented in DAPPER and validated on standard data sets.

The discovery of indicator variables for QSAR using inductive logic programming

A central problem in forming accurate regression equations in QSAR studies is the selection of appropriate descriptors for the compounds under study. We describe a novel procedure for using inductive logic programming (ILP) to discover new indicator variables (attributes) for QSAR problems, and show that these improve the accuracy of the derived regression equations. ILP techniques have previously been shown to work well on drug design problems where there is a large structural component or where clear comprehensible rules are required. However, ILP techniques have had the disadvantage of only being able to make qualitative predictions (e.g. active, inactive) and not to predict real numbers (regression). We unify ILP and linear regression techniques to give a QSAR method that has the strength of ILP at describing steric structure, with the familiarity and power of linear regression. We evaluated the utility of this new QSAR technique by examining the prediction of biological activity with and without the addition of new structural indicator variables formed by ILP. In three out of five datasets examined the addition of ILP variables produced statistically better results (P < 0.01) over the original description. The new ILP variables did not increase the overall complexity of the derived QSAR equations and added insight into possible mechanisms of action. We conclude that ILP can aid in the process of drug design.

Validation of EGSITE2, a mixed integer program for deducing objective site models for experimental binding data

EGSITE2 represents a substantial advance in a long series of methods for calculating receptor site models given only specific binding data. Compared to our most recently reported technique, EGSITE [Schnitker et al. J. Comput.-Aided Mol. Des. 1997, 11, 93-110] the user no longer has to simplify the structures of the molecules in the training set by clustering the atoms into a few superatoms. The only remaining source of subjectivity is the user's choice of compounds for the training set, which can be surprisingly few in number. Then EGSITE2 automatically produces typically several different models that explain the observed binding without outliers. The models are remarkably simple but have substantial predictive power for any sort of test compound, with an estimation of the uncertainty of the prediction. Validation of the method is reported for four standard test cases: triazines and pyrimidines binding to dihydrofolate reductase, steroids binding to corticosteroid-binding globulin and to testosterone-binding globulin, and peptides binding to angiotensin-converting enzyme.

Nonlinear quantitative structure-activity relationship for the inhibition of dihydrofolate reductase by pyrimidines

A novel method for quantitative structure-activity relationship (QSAR) analysis is presented. The method, which does not assume any particular functional form for the QSAR, develops nonlinear relationships between parameters describing a set of molecules and the activity of the molecules. For the QSAR of the inhibition of Escherichia coli dihydrofolate reductase by 2,4-diamino-5-(substituted benzyl)pyrimidines, the method compares favorably to other nonlinear methods. Cross-validation trials demonstrate that the predictive ability is as accurate as other methods, and the method is simpler and faster than neural network and machine-learning methods. Consequently, its implementation is much easier, and interpretation of the generated QSAR is more straightforward.

Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines

Neural networks and inductive logic programming (ILP) have been compared to linear regression for modelling the QSAR of the inhibition of E. coli dihydrofolate reductase (DHFR) by 2,4-diamino-5-(substituted benzyl)pyrimidines, and, in the subsequent paper [Hirst, J.D., King, R.D. and Sternberg, M.J.E. J. Comput.-Aided Mol. Design, 8 (1994) 421], the inhibition of rodent DHFR by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazines. Cross-validation trials provide a statistically rigorous assessment of the predictive capabilities of the methods, with training and testing data selected randomly and all the methods developed using identical training data. For the ILP analysis, molecules are represented by attributes other than Hansch parameters. Neural networks and ILP perform better than linear regression using the attribute representation, but the difference is not statistically significant. The major benefit from the ILP analysis is the formulation of understandable rules relating the activity of the inhibitors to their chemical structure.

Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase

The machine learning program GOLEM from the field of inductive logic programming was applied to the drug design problem of modeling structure-activity relationships. The training data for the program were 44 trimethoprim analogues and their observed inhibition of Escherichia coli dihydrofolate reductase. A further 11 compounds were used as unseen test data. GOLEM obtained rules that were statistically more accurate on the training data and also better on the test data than a Hansch linear regression model. Importantly machine learning yields understandable rules that characterized the chemistry of favored inhibitors in terms of polarity, flexibility, and hydrogen-bonding character. These rules agree with the stereochemistry of the interaction observed crystallographically.

A method for fast energy estimation and visualization of protein-ligand interaction

A new computational and graphical method for facilitating ligand-protein docking studies is developed on a three-dimensional computer graphics display. Various physical and chemical properties inside the ligand binding pocket of a receptor protein, whose structure is elucidated by X-ray crystal analysis, are calculated on three-dimensional grid points and are stored in advance. By utilizing those tabulated data, it is possible to estimate the non-bonded and electrostatic interaction energy and the number of possible hydrogen bonds between protein and ligand molecules in real time during an interactive docking operation. The method also provides a comprehensive visualization of the local environment inside the binding pocket. With this method, it becomes easier to find a roughly stable geometry of ligand molecules, and one can therefore make a rapid survey of the binding capability of many drug candidates. The method will be useful for drug design as well as for the examination of protein-ligand interactions.

An interactive computer graphics study of thermolysin-catalyzed peptide cleavage and inhibition by N-carboxymethyl dipeptides

Interactive computer graphics was used as a tool in studying the cleavage mechanism of the model substrate Z-Phe-Phe-Leu-Trp by the zinc endopeptidase thermolysin. Two Michaelis complexes and three binding orientations of the tetrahedral intermediate to the crystal structure of thermolysin were investigated. Our results indicate that a Michaelis complex, which does not involve coordination of the scissile peptide to the zinc, is consistent with available experimental data and the most plausible of the two complexes. A tetrahedral intermediate complex wherein the two oxygens of the hydrated scissile peptide straddle the zinc in a bidentate fashion results in the most favorable interactions with the active site. The preferred tetrahedral intermediate and Michaelis complex provide a rationalization for the published substrate data. A trajectory for proceeding from the Michaelis complex to the tetrahedral intermediate is proposed. This trajectory involves a simultaneous activation of the zinc-bound water molecule concurrent with attack on the scissile peptide. A detailed ordered product release mechanism is also presented. These studies suggest some modifications and a number of extensions to the mechanism proposed earlier [Kester, W. R., & Matthews, B. W. (1977) Biochemistry 16, 2506; Holmes, M. A., & Matthews, B. W. (1981) Biochemistry 20, 6912]. The binding mode of the thermolysin inhibitor N-(1-carboxy-3-phenylpropyl)-L-leucyl-L-tryptophan [Monzingo, A. F., & Matthews, B. W. (1984) Biochemistry (preceding paper in this issue)] is compared with that of the preferred tetrahedral intermediate, providing insight into this inhibitor design.

Solvent-accessible surfaces of proteins and nucleic acids

A method is presented for analytically calculating a smooth, three-dimensional contour about a molecule. The molecular surface envelope may be drawn on either color raster computer displays or real-time vector computer graphics systems. Molecular areas and volumes may be computed analytically from this surface representation. Unlike most previous computer graphics representations of molecules, which imitate wire models or space-filling plastic spheres, this surface shows only the atoms that are accessible to solvent. This analytical method extends the earlier dot surface numerical algorithm, which has been applied in enzymology, rational drug design, immunology, and understanding DNA base sequence recognition.