Crystallography, quantitative structure-activity relationships, and molecular graphics in a comparative analysis of the inhibition of dihydrofolate reductase from chicken liver and Lactobacillus casei by 4,6-diamino-1,2-dihydro-2,2-dimethyl-1-(substituted-phenyl)-s-triazine s

Chemical space of Escherichia coli dihydrofolate reductase inhibitors: New approaches for discovering novel drugs for old bugs

Escherichia coli Dihydrofolate reductase is an important enzyme that is essential for the survival of the Gram-negative microorganism. Inhibitors designed against this enzyme have demonstrated application as antibiotics. However, either because of poor bioavailability of the small-molecules resulting from their inability to cross the double membrane in Gram-negative bacteria or because the microorganism develops resistance to the antibiotics by mutating the DHFR target, discovery of new antibiotics against the enzyme is mandatory to overcome drug-resistance. This review summarizes the field of DHFR inhibition with special focus on recent efforts to effectively interface computational and experimental efforts to discover novel classes of inhibitors that target allosteric and active-sites in drug-resistant variants of 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.

Structure-activity relationship and comparative docking studies for cycloguanil analogs as PfDHFR-TS inhibitors

Drug resistance acquired by Plasmodium falciparum (Pf) is a major problem in the treatment and control of malaria. One of the major examples of drug resistance is that caused by mutations in the active site of dihydrofolate reductase (DHFR) of Pf (PfDHFR-TS). A double mutation, A16V+S108T, is specific for resistance to the marketed drug cycloguanil. In this study, we used 58 cycloguanil (2,4-diamino-1,6-dihydro-1,3,5-triazine) derivatives to explore the relationship between various physicochemical properties and reported binding affinity data on wild-type and mutant-type A16V+S108T. Using the Hansch 2D-quantitative structure-activity relationship method, we obtained a parabolic relationship of hydrophobicity of substituents at the N1-phenyl ring with the wild-type binding affinity data. Hydrophobicity being a key property for wild-type binding affinity data, we found steric factors to be crucial for A16V+S108T mutant resistance. We investigated FlexX, GOLD, Glide and Molegro virtual docking programs and 13 different scoring functions on 10 of the cycloguanil derivatives to evaluate which program was best for reproducing the experimental binding mode and correlating the docking scores with the reported binding affinity data. We identified GOLD, using its GoldScore fitness function, as the most accurate docking program for predicting binding affinity data of cycloguanil derivatives to DHFR and Molegro virtual docker, with its template docking algorithm and MolDock [GRID] scoring function, as most accurate for reproducing the experimental binding mode of a reference ligand that is structurally similar to the cycloguanil derivatives studied. We also report an interaction index which best describes the structure-activity relationships exhibited by these analogs in terms of PfDHFR-TS active site interactions.

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.

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.

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.

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.

Effects of variable selection on CoMFA coefficient contour maps in a set of triazines inhibiting DHFR

An example of a CoMFA study is described with the aim to discuss one of the major problems of this 3D QSAR method: lack of variable selection. It is shown that the use of nonrelevant energy parameters might produce CoMFA contour maps which poorly reflect the actual nature of the binding site and are in part statistical artefacts. The data set employed in our analysis comprises triazine inhibitors of dihydrofolate reductase (DHFR), isolated from chicken liver, which have already been the object of a QSAR study by other authors. Since three-dimensional structures of triazine-DHFR complexes are known, it was possible not only to reduce ambiguities in the superimposition of the ligands, but also to compare the resulting CoMFA contour maps with the enzyme active site.

Ab initio studies of 2,4-diamino triazine and its complexes with ligands: a model for inhibitor-active site interactions of dihydrofolate reductase

The protonation energies of 2,4-diamino triazine, an inhibitor of the therapeutic target dihydrofolate reductase, has been calculated using ab initio (Hartree-Fock) calculations. It is found that N1 (see Fig. 1) exhibits the highest proton affinity (261.6 kcal/mol) by comparison with other inhibitor protonation sites. The energies of binding of the formate ion and formamide (as models for the amino acid residues in the active site of dihydrofolate reductase) to neutral and protonated 2,4-diamino triazine are also obtained. The highest binding energies are featured by the complex formed from a formate attached to the N4 and N1 protonated forms of the triazine. However, as N4 has a comparatively low proton affinity (195.0 kcal/mol), it is unlikely that an interaction of this nature would prevail. On the other hand, the formate-protonated N1 interaction is similar to the structures identified by X-ray crystallography of enzyme-triazine complexes.

A molecular shape analysis and quantitative structure-activity relationship investigation of some triazine-antifolate inhibitors of Leishmania dihydrofolate reductase

Molecular shape analysis (MSA) is used to develop quantitative structure-activity relationships (QSARs) for a set of 45 4,6-diamino-1,2-dihydro-2,2-dimethyl-1-(3-substituted-phenyl)-s-triazine inhibitors of Leishmania major dihydrofolate reductase (DHFR). The MSA-QSARs are equally significant to a QSAR developed by R. G. Booth et al. [1987) J. Med. Chem. 30, 1218) using linear free energy descriptors. However, the MSA-QSARs have the same general form as all other QSARs developed for DHFR inhibitors using MSA. Molecular shape, as represented by common overlap steric volume of each inhibitor with a shape reference standard triazine from the set of 45 compounds, and relative lipophilicity account for the large majority of the variance in inhibition potency as a function of substituent choice. A general method of evaluating the impact of different conformational states of flexible substituents upon the form and significance of MSA-QSARs is developed. The results of applying this method to the 45 triazines indicate that the MSA-QSARs are relatively independent of the type of conformation assigned to the large flexible substituents. It is important to note, however, that the types of substituent conformations used in this analysis cannot necessarily be related to an "active" substituent conformation.