Search

The optimization of compounds with multiple targets is a difficult multidimensional problem in the drug discovery cycle. Here, we present a systematic, multidisciplinary approach to the development of selective antiparasitic compounds. Computational fragment-based design of novel pteridine derivatives along with iterations of crystallographic structure determination allowed for the derivation of a structure-activity relationship for multitarget inhibition. The approach yielded compounds showing apparent picomolar inhibition of T. brucei pteridine reductase 1 (PTR1), nanomolar inhibition of L. major PTR1, and selective submicromolar inhibition of parasite dihydrofolate reductase (DHFR) versus human DHFR. Moreover, by combining design for polypharmacology with a property-based on-parasite optimization, we found three compounds that exhibited micromolar EC 50 values against T. brucei brucei while retaining their target inhibition. Our results provide a basis for the further development of pteridine-based compounds, and we expect our multitarget approach to be generally applicable to the design and optimization of anti-infective agents.

Flavonoids represent a potential source of new antitrypanosomatidic leads. Starting from a library of natural products, we combined target-based screening on pteridine reductase 1 with phenotypic screening on Trypanosoma brucei for hit identification. Flavonols were identified as hits, and a library of 16 derivatives was synthesized. Twelve compounds showed EC50 values against T. brucei below 10 μM. Four X-ray crystal structures and docking studies explained the observed structure-activity relationships. Compound 2 (3,6-dihydroxy-2-(3-hydroxyphenyl)-4H-chromen-4-one) was selected for pharmacokinetic studies. Encapsulation of compound 2 in PLGA nanoparticles or cyclodextrins resulted in lower in vitro toxicity when compared to the free compound. Combination studies with methotrexate revealed that compound 13 (3-hydroxy-6-methoxy-2-(4-methoxyphenyl)-4H-chromen-4-one) has the highest synergistic effect at concentration of 1.3 μM, 11.7-fold dose reduction index and no toxicity toward host cells. Our results provide the basis for further chemical modifications aimed at identifying novel antitrypanosomatidic agents showing higher potency toward PTR1 and increased metabolic stability.

Human thymidylate synthase (hTS), a target for antiproliferative drugs, is an obligate homodimer. Single-point mutations to alanine at the monomer-monomer interface may enable the identification of specific residues that delineate sites for drugs aimed at perturbing the protein-protein interactions critical for activity. We computationally identified putative hotspot residues at the interface and designed mutants to perturb the intersubunit interaction. Dimer dissociation constants measured by a FRET-based assay range from 60 nM for wild-type hTS up to about 1 mM for single-point mutants and agree with computational predictions of the effects of these mutations. Mutations that are remote from the active site retain full or partial activity, although the substrate KM values were generally higher and the dimer was less stable. The lower dimer stability of the mutants can facilitate access to the dimer interface by small molecules and thereby aid the design of inhibitors that bind at the dimer interface.

Folate analogue inhibitors of Leishmania major pteridine reductase (PTR1) are potential antiparasitic drug candidates for combined therapy with dihydrofolate reductase (DHFR) inhibitors. To identify new molecules with specificity for PTR1, we carried out a virtual screening of the Available Chemicals Directory (ACD) database to select compounds that could interact with L. major PTR1 but not with human DHFR. Through two rounds of drug discovery, we successfully identified eighteen drug-like molecules with low micromolar affinities and high in vitro specificity profiles. Their efficacy against Leishmania species was studied in cultured cells of the promastigote stage, using the compounds both alone and in combination with 1 (pyrimethamine; 5-(4-chlorophenyl)-6-ethylpyrimidine-2,4-diamine). Six compounds showed efficacy only in combination. In toxicity tests against human fibroblasts, several compounds showed low toxicity. One compound, 5c (riluzole; 6-(trifluoromethoxy)-1,3-benzothiazol-2-ylamine), a known drug approved for CNS pathologies, was active in combination and is suitable for early preclinical evaluation of its potential for label extension as a PTR1 inhibitor and antiparasitic drug candidate.

The periplasmic oligopeptide binding component (OppA) of the oligopeptide permease found in Gram-negative bacteria acts as a receptor for peptide transport across the cell membrane and is a potential target for antibacterial drug design. OppA exhibits broad specificity, binding to diverse peptides of 2-5 amino acid residues length. Crystallographic and calorimetric measurements have been carried out by Tame et al. of the binding of 28 peptides of sequence K-X-K to OppA, where X is a natural or nonnatural amino acid. Despite this extensive experimental characterization, a clear relationship between structural and thermodynamic parameters could not be readily identified, with a complicating factor being the observation of varying numbers of water molecules at the binding interface in the different complexes. Consequently, we have applied COMparative BINding Energy (COMBINE) analysis to derive quantitative structure-activity relationships (QSARs) for these 28 OppA-tripeptide complexes. This is the first application of COMBINE analysis to predict binding enthalpies and entropies, and predictive QSAR models were obtained for these quantities as well as for binding free energies. These QSAR models highlight several protein residues and bound water molecules in the binding site, as well as the electrostatic desolvation energies of the protein and the peptides, as responsible for most of the differences in binding thermodynamics between the peptides studied. The QSAR models aid rationalization of the determinants of binding affinity of the OppA:peptide complexes and provide guides for further ligand design. This study also points to the general applicability of COMBINE analysis to estimating thermodynamic parameters for protein-peptide complexes.

Neuraminidase is a surface glycoprotein of influenza viruses that cleaves terminal sialic acids from carbohydrates. It is critical for viral release from infected cells and facilitates viral spread in the respiratory tract. The catalytic active site of neuraminidase is highly conserved in all type A and B influenza viruses, making it an excellent target for antiinfluenza drug design. Indeed, neuraminidase inhibitors have recently become available in the clinic for the treatment of influenza. Here, we describe the use of 3D structures of neuraminidase-inhibitor complexes to derive quantitative structure-activity relationships (QSARs) to aid understanding of the mechanism of inhibition and the discovery of new inhibitors. Crystal structures of neuraminidase-inhibitor complexes were used alongside modeled complexes to derive QSAR models by COMparative BINding Energy (COMBINE) analysis (Ortiz, A. R.; Pisabarro, M. T.; Gago, F.; Wade, R. C. J. Med. Chem. 1995, 38, 2681-2691). The neuraminidase proteins studied include type A subtypes N2 and N9 (which have ca. 50% sequence identity) and an active site mutant of the N9 subtype. The inhibitors include sialic acid and benzoic acid analogues with diverse frameworks and substitution groups. By considering the contributions of the protein residues and a key water molecule to the electrostatic and van der Waals intermolecular interaction energies, a predictive and robust QSAR model for binding to type A neuraminidase was obtained. In this QSAR model, 12 protein residues and 1 bound water molecule are highlighted as particularly important for inhibitory activity. This QSAR model provides guidelines for structural modification of current inhibitors and the design of novel inhibitors in order to optimize inhibitory activity.

Specific binding of transcription factors to DNA is crucial for gene regulation. We derived models for the binding specificity of transcription factors of the nuclear receptor family to DNA using two QSAR methods: a Free-Wilson-like method and COMparative BINding Energy (COMBINE) analysis. The analysis is based on experimental data for the interaction of 20 mutant glucocorticoid receptor DNA-binding domains with 16 different response elements in a total of 320 complexes (Zilliacus, J.; Wright, A. P.; Carlstedt-Duke, J.; Nilsson, L.; Gustafsson, J. A. Proteins 1995, 21, 57-67). The predictive abilities of the models obtained by the two methods are similar. The COMBINE analysis indicates that the most important properties for determining binding specificity for this dataset are the changes upon binding of the solvation free energies of the bases that are mutated in the dataset and the electrostatic interactions of the mutated nucleotides with certain charged amino acids. Further important descriptors are the changes of solvation free energy and surface area of the side chain of the mutated residue. It is clear, however, that there are additional features important for the specificity of binding that are not included in the models, such as differences in interfacial hydration of the complexes.

The effects of data pretreatment, data scaling, and variable selection on three-dimensional quantitative structure-activity relationships derived by comparative molecular field analysis (CoMFA) using the GRID energy function were studied in detail for a set of inhibitors of the human synovial fluid phospholipase A2 (HSF-PLA2). The quality of the models was evaluated for predictive power and ability to map the receptor binding site by (a) comparison of predicted and experimental activities using cross-validation and external validation sets and (b) comparison of the regions selected in space in the CoMFA models with a crystal structure of a HSF-PLA2-inhibitor complex, with optimized comparative binding energy analysis (COMBINE) models (Ortiz et al., 1995) and with structure-activity relationships derived previously for different sets of compounds. It is found that (1) data scaling and dielectric modeling strongly influence CoMFA results. Unscaled data and a uniform dielectric constant of 4 are well suited to GRID-CoMFA studies for the present compound set. (2) The GOLPE and Q2-GRS variable selection methods select variables in roughly the same regions in Cartesian space, but they produce different models in chemometric space and differ in their sensitivity to data scaling and pretreatment and their tendency to overfitting. (3) CoMFA models are consistent with COMBINE models in that they identify approximately the same intermolecular interactions as relevant for activity. Our study provides support for the qualitative receptor-mapping properties of CoMFA models and for the validity of variable selection when applied with care and also provides guidelines for how to evaluate the quality of CoMFA models.

A new computational method for deducing quantitative structure-activity relationships (QSARs) using structural data from ligand-macromolecule complexes is presented. First, the ligand-macromolecule interaction energy is computed for a set of ligands using molecular mechanics calculations. Then, by selecting and scaling components of the ligand-macromolecule interaction energy that show good predictive ability, a regression equation is obtained in which activity is correlated with the interaction energies of parts of the ligands and key regions of the macromolecule. Application to the interaction of the human synovial fluid phospholipase A2 with 26 inhibitors indicates that the derived QSAR has good predictive ability and provides insight into the mechanism of enzyme inhibition. The method, which we term comparative binding energy (COMBINE) analysis, is expected to be applicable to ligand-receptor interactions in a range of contexts including rational drug design, host-guest systems, and protein engineering.

The specificity of interactions between biological macromolecules and their ligands may be partially attributed to the directional properties of hydrogen bonds. We have now extended the GRID method (Goodford, P. J. J. Med. Chem. 1985, 28, 849. Boobbyer, D. N. A.; Goodford, P. J.; McWhinnie, P. M.; Wade, R. C. J. Med. Chem. 1989, 32, 1083), of determining energetically favorable ligand binding sites on molecules of known structure, in order to improve the treatment of groups which can make multiple hydrogen bonds. In this method, the interaction energy between a probe (a small chemical group that may be part of a larger ligand) and a target molecule is calculated using an energy function which includes a hydrogen bond term which is dependent on the length of the hydrogen bond, its orientation at the hydrogen-bonding atoms, and their chemical character. The methods described in the preceding paper (Wade, R. C.; Clark, K. J.; Goodford, P. J. J. Med. Chem., preceding paper in this issue) for probes capable of making two hydrogen bonds are here extended to the following probes which have the ability to make more than two hydrogen bonds: ammonium-NH3+, amine-NH2, sp3-hybridized hydroxyl, and water. Use of the improved GRID procedure is demonstrated by the determination of the conformation of an amino acid side chain at the subunit interface in hemoglobin and of the location of water binding sites in human lysozyme.

The directional properties of hydrogen bonds play a major role in determining the specificity of intermolecular interactions. An energy function which takes explicit account of these properties has been developed for use in the determination of energetically favorable ligand binding sites on molecules of known structure by the GRID method (Goodford, P.J.J. Med. Chem. 1985, 28, 849. Boobbyer, D.N.A.; Goodford, P.J.; McWhinnie, P.M.; Wade, R.C.J. Med. Chem. 1989, 32, 1083). In this method, the interaction energy between a target molecule and a small chemical group (a probe), which may be part of a larger ligand, was calculated using an energy function consisting of Lennard-Jones, electrostatic, and hydrogen bond terms. The latter term was a function of the length of the hydrogen bond, its orientation at the hydrogen-bonding atoms, and their chemical nature. We now describe hydrogen bond energy functions which take account of the spatial distribution of the hydrogen bonds made by probes with the ability to form two hydrogen bonds. These functions were designed so as to model the experimentally observed angular dependence of the hydrogen bonds. We also describe the procedure to locate the position and orientation of the probe at which the interaction energy is optimized. The use of this procedure is demonstrated by examples of biological and pharmacological interest which show that it can produce results that are consistent with other theoretical approaches and with experimental observations.

An empirical energy function designed to calculate the interaction energy of a chemical probe group, such as a carbonyl oxygen or an amine nitrogen atom, with a target molecule has been developed. This function is used to determine the sites where ligands, such as drugs, may bind to a chosen target molecule which may be a protein, a nucleic acid, a polysaccharide, or a small organic molecule. The energy function is composed of a Lennard-Jones, an electrostatic and a hydrogen-bonding term. The latter is dependent on the length and orientation of the hydrogen bond and also on the chemical nature of the hydrogen-bonding atoms. These terms have been formulated by fitting to experimental observations of hydrogen bonds in crystal structures. In the calculations, thermal motion of the hydrogen-bonding hydrogen atoms and lone-pair electrons may be taken into account. For example, in a alcoholic hydroxyl group, the hydrogen may rotate around the C-O bond at the observed tetrahedral angle. In a histidine residue, a hydrogen atom may be bonded to either of the two imidazole nitrogens and movement of this hydrogen will cause a redistribution of charge which is dependent on the nature of the probe group and the surrounding environment. The shape of some of the energy functions is demonstrated on molecules of pharmacological interest.