Biomedical subjects

Pieter F W Stouten

Publications and source records attributed to Pieter F W Stouten.

8 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

Quantitative determination of the topological propensities of amyloidogenic peptides.

One of the interesting puzzles of amyloid beta-peptide of Alzheimer's disease (Abeta) is that it appears to polymerize into amyloid fibrils in a parallel beta sheet topology, while smaller subsets of the peptide produce anti-parallel beta sheets. In order to target potential weak points of amyloid fibrils in a rational drug design effort, it would be helpful to understand the forces that drive this change. We have designed two peptides CHQKLVFFAEDYNGKDEAFFVLKQHW and CHQKLVFFAEDYNGKHQKLVFFAEDW that join the significant amyloidogenic Abeta (14-23) sequence HQKLVFFAED in parallel and anti-parallel topologies, respectively. (Here, the word "parallel" refers only to residue sequence and not backbone topology). The N-termini of the hairpins were labeled with the fluorescent dye 5-((((2-iodoacetyl)amino)ethyl)amino)naphthalene-1-sulfonic acid (IAEDANS), forming a fluorescence energy transfer donor-acceptor pair with the C-terminus tryptophan. Circular dichroism results show that the anti-parallel hairpin adopts a beta-sheet conformation, while the parallel hairpin is disordered. Fluorescent Resonance Energy Transfer (FRET) results show that the distance between the donor and the acceptor is significantly shorter in the anti-parallel topology than in the parallel topology. The fluorescence intensity of anti-parallel hairpin also displays a linear concentration dependence, indicating that the FRET observed in the anti-parallel hairpin is from intra-molecular interactions. The results thus provide a quantitative estimate of the relative topological propensities of amyloidogenic peptides. Our FRET and CD results show that beta sheets involving the essential Abeta (14-23) fragment, strongly prefer the anti-parallel topology. Moreover, we provide a quantitative estimate of the relative preference for these two topologies. Such analysis can be repeated for larger subsets of Abeta to determine quantitatively the relative degree of preference for parallel/anti-parallel topologies in given fragments of Abeta.

Amino Acid Sequence↗

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↗

PubMed2005

Understanding and modulating cyclin-dependent kinase inhibitor specificity: molecular modeling and biochemical evaluation of pyrazolopyrimidinones as CDK2/cyclin A and CDK4/cyclin D1 inhibitors.

Cyclin-dependent kinases (CDKs) play a key role in regulating the cell cycle. The cyclins, their activating agents, and endogenous CDK inhibitors are frequently mutated in human cancers, making CDKs interesting targets for cancer chemotherapy. Our aim is the discovery of selective CDK4/cyclin D1 inhibitors. An ATP-competitive pyrazolopyrimidinone CDK inhibitor was identified by HTS and docked into a CDK4 homology model. The resulting binding model was consistent with available SAR and was validated by a subsequent CDK2/inhibitor crystal structure. An iterative cycle of chemistry and modeling led to a 70-fold improvement in potency. Small substituent changes resulted in large CDK4/CDK2 selectivity changes. The modeling revealed that selectivity is largely due to hydrogen-bonded interactions with only two kinase residues. This demonstrates that small differences between enzymes can efficiently be exploited in the design of selective inhibitors.

Amino Acid Sequence↗

PubMed2004

Influence of molecular flexibility and polar surface area metrics on oral bioavailability in the rat.

The relationship of rotatable bond count (N(rot)) and polar surface area (PSA) with oral bioavailability in rats was examined for 434 Pharmacia compounds and compared with an earlier report from Veber et al. (J. Med. Chem. 2002, 45, 2615). N(rot) and PSA were calculated with QikProp or Cerius2. The resulting correlations depended on the calculation method and the therapeutic class within the data superset. These results underscore that such generalizations must be used with caution.

Administration, Oral↗

PubMedDate not supplied

Linear and nonlinear methods in modeling the aqueous solubility of organic compounds.

Solubility data for 930 diverse compounds have been analyzed using linear Partial Least Square (PLS) and nonlinear PLS methods, Continuum Regression (CR), and Neural Networks (NN). 1D and 2D descriptors from MOE package in combination with E-state or ISIS keys have been used. The best model was obtained using linear PLS for a combination between 22 MOE descriptors and 65 ISIS keys. It has a correlation coefficient (r2) of 0.935 and a root-mean-square error (RMSE) of 0.468 log molar solubility (log S(w)). The model validated on a test set of 177 compounds not included in the training set has r2 0.911 and RMSE 0.475 log S(w). The descriptors were ranked according to their importance, and at the top of the list have been found the 22 MOE descriptors. The CR model produced results as good as PLS, and because of the way in which cross-validation has been done it is expected to be a valuable tool in prediction besides PLS model. The statistics obtained using nonlinear methods did not surpass those got with linear ones. The good statistic obtained for linear PLS and CR recommends these models to be used in prediction when it is difficult or impossible to make experimental measurements, for virtual screening, combinatorial library design, and efficient leads optimization.

Journal Article↗

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↗