Biomedical subjects
Publications and source records attributed to Brett A Tounge.
We report the design, synthesis, and binding affinities of a family of cyclic RGD peptides attached to type VI beta-turn scaffolds. The analogues prepared exhibit interesting binding data to the isolated receptors alphavbeta3 and alphavbeta5. The results demonstrate the utility of these type VI beta-turn scaffolds for the constraint of biologically relevant peptides.
Computing the binding affinity of a protein-ligand complex is one of the most fundamental and difficult tasks in computer-aided drug design. Many approaches for computing binding affinities can be classified as linear interaction energy (LIE) models as they rely on some type of linear fit of computed interaction energies between ligand and protein. We have examined the computed interaction energies of a series of beta-secretase (BACE) inhibitors in terms of van der Waals, coulombic, and continuum-solvation contributions to ligand binding. We have also systematically examined the effect of different protonation states of the protein and ligands. We find that the binding affinities are relatively insensitive to the protonation state of the protein when neutral ligands are considered. Inclusion of charged ligands leads to large deviations in the coulomb, solvation, and even van der Waals terms. The latter is due to increased repulsive van der Waals interactions in the complex due to the strong coulomb attraction found between oppositely charged functional groups in the protein and ligand. In general, we find that the best models are obtained when the protein is judiciously charged (e.g. Asp32-, Arg235+) and the potentially charged ligands are treated as neutral.
Homology models based on available K+ channel structures have been used to construct a multiple state representation of the hERG cardiac K+ channel. These states are used to capture the flexibility of the channel. We show that this flexibility is essential in order to correctly model the binding affinity of a set of diverse ligands. Using this multiple state approach, a binding affinity model was constructed for set of known hERG channel binders. The predicted pIC50s are in good agreement with experiment (RMSD: 0.56 kcal/mol). In addition, these calculations provide structures for the bound ligands that are consistent with published mutation studies. These computed ligand bound complex structures can be used to guide synthesis of analogs with reduced hERG liability.
The synthesis and SAR of a new class of piperidine-based alphavbeta3/alphavbeta5 integrin antagonists is described. Replacement of an amide bond in a prototype isonipecotamide by a C-C isostere, and adjustment of the spacer length between the carboxylic acid and basic moieties, led to low nanomolar antagonists of alphavbeta3 and/or alphavbeta5 integrins with excellent selectivity versus alpha(IIb)beta3.
[structure: see text] We have prepared 7,9-diazabicyclo[4.2.2]dec-3-ene-8,10-dione (3) and 7,9-diazabicyclo[4.2.2]decane-8,10-dione (4), which differ by virtue of the degree of unsaturation in the bridging carbacyclic tether on a 2,5-diketopiperazine. Remarkably different self-assembly patterns were observed in the solid state for the two compounds, attributed to subtle variations in the conformational constraints imposed by the tether.
One of the key questions that must be addressed when implementing a chemoinformatics system is whether the tools will be designed for use by the expert user or by the "bench scientist." This decision can impact not only the style of tools that are rolled out, but is also a factor in terms of how these tools are delivered to the end users. The system that we outline here was designed for use by the non-expert user. As such, the tools that we discuss are in many cases simplified versions of some common algorithms used in chemoinformatics. In addition, the focus is on how to distribute these tools using a web-services interface, which greatly simplifies delivering new protocols to the end user.
It has been shown that the rate-limiting step in the production of beta-amyloid peptide (Abeta) is the proteolytric cleavage of the membrane-bound beta-amyloid precursor protein (APP) by beta-secretase (BACE). Since the accumulation of Abeta has been implicated as one of the key events in the progression of Alzheimer's disease, BACE has become an important therapeutic target. Recently, two crystal structures of BACE cocrystallized with the inhibitors OM99-2 and OM00-3 were published by Tang and co-workers. In addition, the Ghosh group has published binding data on a series of inhibitors based on their initial lead, OM99-2. Using this set as a basis, we have developed a model for the binding affinity of these ligands to BACE using the linear interaction energy method. The best binding affinity model for the full set of ligands had a RMSD of 1.10 kcal/mol. The best model excluding the two charged ligands had a RMSD of 0.87 kcal/mol.
Scaling is a difficult issue for any analysis of chemical properties or molecular topology when disparate descriptors are involved. To compare properties across different data sets, a common scale must be defined. Using several publicly available databases (ACD, CMC, MDDR, and NCI) as a basis, we propose to define chemically meaningful scales for a number of molecular properties and topology descriptors. These chemically derived scaling functions have several advantages. First, it is possible to define chemically relevant scales, greatly simplifying similarity and diversity analyses across data sets. Second, this approach provides a convenient method for setting descriptor boundaries that define chemically reasonable topology spaces. For example, descriptors can be scaled so that compounds with little potential for biological activity, bioavailability, or other drug-like characteristics are easily identified as outliers. We have compiled scaling values for 314 molecular descriptors. In addition the 10th and 90th percentile values for each descriptor have been calculated for use in outlier filtering.
We have developed a new method for assigning a drug-like score to reagents. This algorithm uses topological torsion (TT) 2D descriptors to compute the subsimilarity of any given reagent to a substructural element of any compound in the CMC. The utility of this approach is demonstrated by scoring a test set of reagents derived from the "Comprehensive Survey of Combinatorial Library Synthesis: 2000" (J. Comb. Chem.). R-groups were extracted from the most-active compounds found in each of the reviewed libraries, and the distribution of the subsimilarity scores for these monomers were compared to the ACD. This comparison showed a dramatic shift in the distribution of the JCC R-group subset toward higher subsimilarity scores in comparison to the entire ACD database. The ACD was also used to examine the relationship between molecular weight and various subsimilarity scoring algorithms. This analysis was used to derive a subsimilarity score that is less biased by molecular weight.