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Designed as a multivalent hydrogen bond acceptor, new receptors, Discopus 1a,b, were built from a triphenylene core surrounded by six (diaryl)phosphinate groups. An efficient synthesis was developed to prepare these elaborated structures in a high overall yield. The X-ray structure of receptor 1b showed strong cooperative hydrogen bonds with two water molecules and intermolecular CH-pi contacts. In chloroform, Discopus 1a,b displayed recognition properties toward dihydroxybenzenes, selectively forming complexes with catechol derivatives 4a-c in a 1:2 (host:guest) stoichiometry. According to NMR and microcalorimetry titrations, association constants were found in the 30-2837 M(-1) range, which were larger than those reported for curvated catechol receptors (14-120 M(-1)). Interestingly, Discopus present two distinct catechol binding sites. Weak hydrogen bonding between host phosphinates and guest hydroxyl groups was shown by infrared spectroscopy and (31)P NMR. Molecular dynamics simulations and recognition experiments suggested that a stronger hydrogen bond assisted by a pi-interaction between the Discopus core and one catechol molecule could exist within the 1:2 complex.

A rational design approach was followed to develop peptidomimetic analogues of a cytotoxic T-cell epitope capable of stimulating T-cell responses as strong as or stronger (heteroclytic) than those of parental antigenic peptides. The work described herein focused on structural alterations of the central amino acids of the melanoma tumor-associated antigenic peptide Melan-A/MART-1(26-35) using nonpeptidic units. A screening was first realized in silico to select altered peptides potentially capable of fitting at the interface between the major histocompatibilty complex (MHC) class-I HLA-A2 molecule and T-cell receptors (TCRs). Two compounds appeared to be high-affinity ligands to the HLA-A2 molecule and stimulated several Melan-A/MART-1 specific T-cell clones. Most remarkably, one of them even managed to amplify the response of one clone. Together, these results indicate that central TCR-contact residues of antigenic peptides can be replaced by nonpeptidic motifs without loss of binding affinity to MHC class-I molecules and T-cell triggering capacity.

A LigandFit shape-directed docking methodology was used to identify the best position at which the melanoma-derived MHC class-I HLA-A2-binding antigenic peptide ELAGIGILTV could be modified by attaching a small molecule capable of fitting at the interface of complementary determining regional (CDR) loops of a T-cell receptor (TCR) while triggering T-cell responses. The small molecule selected here for determining the feasibility of this alternative track to chemical alteration of antigenic peptides was the electrophilic quinone methide (+)-puupehenone (), a natural product that belongs to a family of marine metabolites capable of expressing immunomodulatory activities. A preliminary chemical reactivity model study revealed the efficacy of the thiol group of a cysteine (C) side-chain in its nucleophilic addition reaction with in a regio- and diastereoselective manner. The best TCR/HLA-A2 ligand [i.e., ELAGCGILTV-S-puupehenol ()] then identified by the LigandFit docking procedure was synthesized and used to pulse HLA-A2(+) T2 cells for T-cell stimulation. Among the ELAGIGILTV-specific T-cell clones we tested, five of them recognized the conjugate in spite of its low binding affinity for the HLA-A2 molecules. The resulting T-cell stimulation was determined through the intracytoplasmic secretion of IFN-gamma and the percentage of T-cells thus activated. These highly encouraging results indicate that small non-peptidic natural product-derived molecules attached onto the central part of an antigenic peptide can fit at the TCR/HLA-A2 interface with induction of T-cell responses.

A key problem in QSAR is the selection of appropriate descriptors to form accurate regression equations for the compounds under study. Inductive logic programming (ILP) algorithms are a class of machine-learning algorithms that have been successfully applied to a number of SAR problems. Unlike other QSAR methods, which use attributes to describe chemical structure, ILP uses relations. This gives ILP the advantages of not requiring explicit superimposition of individual compounds in a dataset, of dealing naturally with multiple conformations, and of using a language much closer to that used normally by chemists. We unify ILP and standard regression techniques to give a QSAR method that has the strength of ILP at describing steric structure with the familiarity and power of regression methods. Complex pharmacophores, correlating with activity, were identified and used as new indicator variables, along with the comparative molecular field analysis (CoMFA) prediction, to form predictive regression equations. We compared the formation of 3D-QSARs using standard CoMFA with the use of ILP on the well-studied thermolysin zinc protease inhibitor dataset and a glycogen phosphorylase inhibitor dataset. In each case the addition of ILP variables produced statistically better results (P < 0.01 for thermolysin and P < 0.05 for GP datasets) than the CoMFA analysis. Moreover, the new ILP variables were not found to increase the complexity of the final QSAR equations and gave possible insight into the binding mechanism of the ligand-protein complex under study.