Biomedical subjects

Jean-Yves Trosset

Publications and source records attributed to Jean-Yves Trosset.

6 recordsLinked to original sources

PubMed2006

Inhibition of protein-protein interactions: the discovery of druglike beta-catenin inhibitors by combining virtual and biophysical screening.

The interaction between beta-catenin and Tcf family members is crucial for the Wnt signal transduction pathway, which is commonly mutated in cancer. This interaction extends over a very large surface area (4800 A(2)), and inhibiting such interactions using low molecular weight inhibitors is a challenge. However, protein surfaces frequently contain "hot spots," small patches that are the main mediators of binding affinity. By making tight interactions with a hot spot, a small molecule can compete with a protein. The Tcf3/Tcf4-binding surface on beta-catenin contains a well-defined hot spot around residues K435 and R469. A 17,700 compounds subset of the Pharmacia corporate collection was docked to this hot spot with the QXP program; 22 of the best scoring compounds were put into a biophysical (NMR and ITC) screening funnel, where specific binding to beta-catenin, competition with Tcf4 and finally binding constants were determined. This process led to the discovery of three druglike, low molecular weight Tcf4-competitive compounds with the tightest binder having a K(D) of 450 nM. Our approach can be used in several situations (e.g., when selecting compounds from external collections, when no biochemical functional assay is available, or when no HTS is envisioned), and it may be generally applicable to the identification of inhibitors of protein-protein interactions.

Binding Sites↗

PubMed2005

Virtual screening to enrich a compound collection with CDK2 inhibitors using docking, scoring, and composite scoring models.

Docking programs can generate subsets of a compound collection with an increased percentage of actives against a target (enrichment) by predicting their binding mode (pose) and affinity (score), and retrieving those with the highest scores. Using the QXP and GOLD programs, we compared the ability of six single scoring functions (PLP, Ligscore, Ludi, Jain, ChemScore, PMF) and four composite scoring models (Mean Rank: MR, Rank-by-Vote: Vt, Bayesian Statistics: BS and PLS Discriminant Analysis: DA) to separate compounds that are active against CDK2 from inactives. We determined the enrichment for the entire set of actives (IC50 < 10 microM) and for three activity subsets. In all cases, the enrichment for each subset was lower than for the entire set of actives. QXP outperformed GOLD at pose prediction, but yielded only moderately better enrichments. Five to six scoring functions yielded good enrichments with GOLD poses, while typically only two worked well with QXP poses. For each program, two scoring functions generally performed better than the others (Ligscore2 and Ludi for GOLD; QXP and Jain for QXP). Composite scoring functions yielded better results than single scoring functions. The consensus approaches MR and Vt worked best when separating micromolar inhibitors from inactives. The statistical approaches BS and DA, which require training data, performed best when distinguishing between low and high nanomolar inhibitors. The key observation that all hit rate profiles for all four activity intervals for all scoring schemes for both programs are significantly better than random, is evidence that docking can be successfully applied to enrich compound collections.

Adenosine Triphosphate↗

PubMed2004

Catecholic flavonoids acting as telomerase inhibitors.

In recent years telomerase has been identified as a new promising target in oncology and consequently new telomerase inhibitors have been intensely explored as anticancer agents. Focused screening of several polyhydroxylated flavonoids has allowed us to identify 7,8,3',4'-tetrahydroxyflavone 1 as a new telomerase inhibitor with an interesting in vitro activity in a Flash-Plate assay (IC50 = 0.2 microM) that has been confirmed in the classical TRAP assay. Starting from this compound, we developed a medicinal chemistry program to optimize our lead, and in particular to replace one of the two catechols with potential bioisosteres. From this study, new structural analogues characterized by submicromolar potencies have been obtained. Their synthesis and biological activity are described.

Antineoplastic Agents↗

PubMed2003

Hot spots in Tcf4 for the interaction with beta-catenin.

The interaction of beta-catenin with T-cell factor (Tcf) 4 plays a central role in the Wnt signaling pathway and has been discussed as a possible site of intervention for the development of anti-cancer drugs. In this study, we performed Ala-scanning mutagenesis of all Tcf4 residues in the Tcf-beta-catenin interface and studied the binding energetics of these mutants using isothermal titration calorimetry. Binding of Tcf4 was found to be highly cooperative. Single site mutations of most Tcf4 residues resulted in a significant reduction in binding enthalpies but in similar binding constants as compared with wild type Tcf4. Interestingly, this was also true for residues that are disordered in the reported crystal structures. The mutation D16A caused the largest reduction in binding constant (50-fold) accompanied by a large unfavorable enthalpy change (DeltaDeltaHobs) of +8 kcal/mol at 25 degrees C. In contrast, the mutation of the Tcf residues Glu24 and Glu28, which have been proposed as an interaction hot spot due to their location in a field of strong positive electrostatic potential on the beta-catenin surface (charge button), resulted only in a significant reduction of binding enthalpies, which were largely compensated for by unfavorable entropic contributions to the binding. Other mutations that significantly reduced Tcf binding constants were D11A and alanine mutations of the hydrophobic residues Leu41, Val44, and Leu48. The measured thermodynamic data are discussed with the available structural information of Tcf-beta-catenin crystal structures and allow us to propose possible sites for development of Tcf antagonists.

Amino Acid Sequence↗

PubMedDate not supplied

Novel scoring functions comprising QXP, SASA, and protein side-chain entropy terms.

Novel scoring functions that predict the affinity of a ligand for its receptor have been developed. They were built with several statistical tools (partial least squares, genetic algorithms, neural networks) and trained on a data set of 100 crystal structures of receptor-ligand complexes, with affinities spanning 10 log units. The new scoring functions contain both descriptors generated by the QXP docking program and new descriptors that were developed in-house. These new descriptors are based on solvent accessible surface areas and account for conformational entropy changes and desolvation effects of both ligand and receptor upon binding. The predictive r(2) values for a test set of 24 complexes are in the 0.712-0.741 range and RMS prediction errors in the 1.09-1.16 log K(d) range. Inclusion of the new descriptors led to significant improvements in affinity prediction, compared to scoring functions based on QXP descriptors alone. However, the QXP descriptors by themselves perform better in binding mode prediction. The performance of the linear models is comparable to that of the neural networks. The new functions perform very well, but they still need to be validated as universal tools for the prediction of binding affinity.

Algorithms↗

PubMedDate not supplied

Assessment of docking poses: interactions-based accuracy classification (IBAC) versus crystal structure deviations.

Six docking programs (FlexX, GOLD, ICM, LigandFit, the Northwestern University version of DOCK, and QXP) were evaluated in terms of their ability to reproduce experimentally observed binding modes (poses) of small-molecule ligands to macromolecular targets. The accuracy of a pose was assessed in two ways: First, the RMS deviation of the predicted pose from the crystal structure was calculated. Second, the predicted pose was compared to the experimentally observed one regarding the presence of key interactions with the protein. The latter assessment is referred to as interactions-based accuracy classification (IBAC). In a number of cases significant discrepancies were found between IBAC and RMSD-based classifications. Despite being more subjective, the IBAC proved to be a more meaningful measure of docking accuracy in all these cases.

Crystallography, X-Ray↗