The NEWLEAD program: a new method for the design of candidate structures from pharmacophoric hypotheses.

We have developed a computer program, called NEWLEAD, for the automatic generation of candidate structures conforming to the requirements of a given pharmacophore. The treatment consists in connecting the pharmacophoric pieces with spacers assembled from small chemical entities (atoms, chains, or ring moieties). We have tested the program on several sets of input fragments, each comprising selected functional groups obtained from the bioactive conformations of reference molecules. In addition to the expected solutions, the program can generate new structures that are chemically unrelated to the reference molecules. This provides an unbiased starting point for the design of new generations of lead structures. The concept used in this approach is presented and discussed. The present possibilities of the program are illustrated by some examples. The treatment is very fast, because only a few bonds are created between building blocks already having ideal geometries. The ability to generate rapidly a variety of molecules conforming to a three-dimensional pharmacophoric model makes NEWLEAD a useful tool with wide applicability in rational drug design, including the areas of molecular mimicry and peptidomimetism.

Improving the oral bioavailability of the iron chelator HBED by breaking the symmetry of the intramolecular H-bond network.

Physicochemical analysis and Monte Carlo simulations were used to identify structural features which prevent oral absorption of HBED, a potent iron chelator. In water the dominant conformations of HBED involve the hydrophobic collapse of the two aromatic rings. These conformations are favored in polar media because they expose the polar phenolic hydroxy groups to the solvent and partially shield the nonpolar aromatic rings. In a less polar solvent such as chloroform, a symmetrical H-bond network between the carboxylates and the amines dominates the conformational space. This leads to the exposure of the phenolic hydroxy groups to the solvent, which is unfavorable for solvation. The low solubility of HBED in nonpolar solvents was confirmed experimentally by determination of the partition coefficients in octanol, chloroform, and cyclohexane and may explain the poor membrane permeability of this compound. The high conformational stability which disfavors partitioning into phospholipids is mainly due to the symmetrical H-bond network. Potentiometric titrations of a monoester of HBED in MeOH/water indicate that the protonation sequence was changed compared to that of the parent compound, suggesting that the symmetrical H-bond network was disrupted. Conformational analysis in chloroform confirmed that, in contrast to HBED, no symmetric interaction between the carboxylate and the nitrogen amines is possible in the half-ester and a variety of conformations which allow partial shielding of the polar phenolic OH groups are energetically possible. This theoretical model predicting a better solubility of the half-esters in nonpolar solvents was supported by the large increase in the partition coefficients in octanol, chloroform, and cyclohexane measured experimentally. The high absorbability predicted by physicochemical and computer simulation methods was corroborated by in vivo experiments in marmoset monkeys where the monoethyl ester derivative of HBED was well-absorbed orally while the parent compound was nearly ineffective in the same model.

Predicting binding affinities of protein ligands from three-dimensional models: application to peptide binding to class I major histocompatibility proteins.

A simple and fast free energy scoring function (Fresno) has been developed to predict the binding free energy of peptides to class I major histocompatibility (MHC) proteins. It differs from existing scoring functions mainly by the explicit treatment of ligand desolvation and of unfavorable protein-ligand contacts. Thus, it may be particularly useful in predicting binding affinities from three-dimensional models of protein-ligand complexes. The Fresno function was independently calibrated for two different training sets: (a) five HLA-A0201-peptide structures, which had been determined by X-ray crystallography, and (b) three-dimensional models of 37 H-2K(k)-peptide structures, which had been obtained by knowledge-based homology modeling. For both training sets, a good cross-validated fit to experimental binding free energies was obtained with predictive errors of 3-3.5 kJ/mol. As expected, lipophilic interactions were found to contribute the most to HLA-A0201-peptide interactions, whereas H-bonding predominates in H-2K(k) recognition. Both cross-validated models were afterward used to predict the binding affinity of a test set of 26 peptides to HLA-A0204 (an HLA allele closely related to HLA-A0201) and of a series of 16 peptides to H-2K(k). Predictions were more accurate for HLA-A2-binding peptides as the training set had been built from experimentally determined structures. The average error in predicting the binding free energy of the test peptides was 3.1 kJ/mol. For the homology model-derived equation, the average error in predicting the binding free energy of peptides to K(k) was significantly higher (5.4 kJ/mol) but still very acceptable. The present scoring function is thus able to predict with a good accuracy binding free energies from three-dimensional models, at the condition that the backbone coordinates of the MHC-bound peptide have first been determined with an accuracy of about 1-1.5 A. Furthermore, it may be easily recalibrated for any protein-ligand complex.