Megan L. Peach

Wealth of opportunity - the C1 domain as a target for drug development.

The diacylglycerol-responsive C1 domains of protein kinase C and of the related classes of signaling proteins represent highly attractive targets for drug development. The signaling functions that are regulated by C1 domains are central to cellular control, thereby impacting many pathological conditions. Our understanding of the diacylglycerol signaling pathways provides great confidence in the utility of intervention in these pathways for treatment of cancer and other conditions. Multiple compounds directed at these signaling proteins, including compounds directed at the C1 domains, are currently in clinical trials, providing strong validation for these targets. Extensive understanding of the structure and function of C1 domains, coupled with detailed insights into the molecular details of ligand - C1 domain interactions, provides a solid basis for rational and semi-rational drug design. Finally, the complexity of the factors contributing to ligand - C1 domain interactions affords abundant opportunities for manipulation of selectivity; indeed, substantially selective compounds have already been identified.

Molecular modeling, total synthesis, and biological evaluations of C9-deoxy bryostatin 1.

The bryostatins are a family of natural products of marine origin that display both intriguing structural complexity and a fascinating profile of biological activity.[1] These materials were isolated (from Bugula neritina) and their structures determined through the pioneering work of Pettit and coworkers.[2] Subsequently, a monumental large scale collection and isolation effort managed to yield some 18 grams of bryostatin 1, the most abundant and now most thoroughly investigated member of this family, from some 13,000 kg of the source organism.[3] This world’s supply of material has supported numerous biological investigations and roughly 80 clinical trials against various cancers.[4] Recently, a clinical trial against Alzheimer’s disease has also commenced.[5]

Effects on Ligand Interaction and Membrane Translocation of the Positively Charged Arginine Residues Situated along the C1 Domain Binding Cleft in the Atypical Protein Kinase C Isoforms

The C1 domain zinc finger structure is highly conserved among the protein kinase C (PKC) superfamily members. As the interaction site for the second messenger sn-1,2-diacylglycerol (DAG) and for the phorbol esters, the C1 domain has been an important target for developing selective ligands for different PKC isoforms. However, the C1 domains of the atypical PKC members are DAG/phorbol ester-insensitive. Compared with the DAG/phorbol ester-sensitive C1 domains, the rim of the binding cleft of the atypical PKC C1 domains possesses four additional positively charged arginine residues (at positions 7, 10, 11, and 20). In this study, we showed that mutation to arginines of the four corresponding sites in the C1b domain of PKCδ abolished its high potency for phorbol 12,13-dibutyrate in vitro, with only marginal remaining activity for phorbol 12-myristate 13-acetate in vivo. We also demonstrated both in vitro and in vivo that the loss of potency to ligands was cumulative with the introduction of the arginine residues along the rim of the binding cavity rather than the consequence of loss of a single, specific residue. Computer modeling reveals that these arginine residues reduce access of ligands to the binding cleft and change the electrostatic profile of the C1 domain surface, whereas the basic structure of the binding cleft is still maintained. Finally, mutation of the four arginine residues of the atypical PKC C1 domains to the corresponding residues in the δC1b domain conferred response to phorbol ester. We speculate that the arginine residues of the C1 domain of atypical PKCs may provide an opportunity for the design of ligands selective for the atypical PKCs.

Directed Discovery of Agents Targeting the Met Tyrosine Kinase Domain by Virtual Screening

Hepatocyte growth factor (HGF) is an important regulator of normal development and homeostasis, and dysregulated signaling through the HGF receptor, Met, contributes to tumorigenesis, tumor progression, and metastasis in numerous human malignancies. The development of selective small-molecule inhibitors of oncogenic tyrosine kinases (TK) has led to well-tolerated, targeted therapies for a growing number of cancer types. To identify selective Met TK inhibitors, we used a high-throughput virtual screen of the 13.5 million compound ChemNavigator database to find compounds most likely to bind to the Met ATP binding site and to form several critical interactions with binding site residues predicted to stabilize the kinase domain in its inactive conformation. Subsequent biological screening of 70 in silico hit structures using cell-free and intact cell assays identified three active compounds with micromolar IC(50) values. The predicted binding modes and target selectivity of these compounds are discussed and compared to other known Met TK inhibitors.

Combining docking with pharmacophore filtering for improved virtual screening

BackgroundVirtual screening is used to distinguish potential leads from inactive compounds in a database of chemical samples. One method for accomplishing this is by docking compounds into the structure of a receptor binding site in order to rank-order compounds by the quality of the interactions they form with the receptor. It is generally established that docking can be reasonably successful at generating good poses of a ligand in an active site. However, the scoring functions that are used with docking are typically not successful at correctly ranking ligands according to binding affinity or even distinguishing correct poses of a given ligand from incorrect ones.ResultsWe have developed a simple method for reducing the number of false positives in a virtual screen, meaning ligands which are scored highly by the docking program but do not bind well in reality. This method uses a docking program for pose generation without regard to scoring, followed by filtering with receptor-based pharmacophore searches. We applied it to three test-case targets: neuraminidase A, cyclin-dependent kinase 2, and the C1 domain of protein kinase C.ConclusionThe pharmacophore filtering method can perform better than more traditional docking + scoring methods, and allows the advantages of both docking-based and pharmacophore-based approaches to virtual screening to be fully realized.

QSAR modeling of imbalanced high-throughput screening data in PubChem.

Many of the structures in PubChem are annotated with activities determined in high-throughput screening (HTS) assays. Because of the nature of these assays, the activity data are typically strongly imbalanced, with a small number of active compounds contrasting with a very large number of inactive compounds. We have used several such imbalanced PubChem HTS assays to test and develop strategies to efficiently build robust QSAR models from imbalanced data sets. Different descriptor types [Quantitative Neighborhoods of Atoms (QNA) and “biological” descriptors] were used to generate a variety of QSAR models in the program GUSAR. The models obtained were compared using external test and validation sets. We also report on our efforts to incorporate the most predictive of our models in the publicly available NCI/CADD Group Web services (http://cactus.nci.nih.gov/chemical/apps/cap).

Synthesis of seco-B-ring bryostatin analogue WN-1 via C-C bond-forming hydrogenation: critical contribution of the B-ring in determining bryostatin-like and phorbol 12-myristate 13-acetate-like properties.

The seco-B-ring bryostatin analogue, macrodiolide WN-1, was prepared in 17 steps (longest linear sequence) and 30 total steps with three bonds formed via hydrogen-mediated C–C coupling. This synthetic route features a palladium-catalyzed alkoxycarbonylation of a C2-symmetric diol to form the C9-deoxygenated bryostatin A-ring. WN-1 binds to PKCα (Ki = 16.1 nM) and inhibits the growth of multiple leukemia cell lines. Although structural features of the WN-1 A-ring and C-ring are shared by analogues that display bryostatin-like behavior, WN-1 displays PMA-like behavior in U937 cell attachment and proliferation assays, as well as in K562 and MV-4-11 proliferation assays. Molecular modeling studies suggest the pattern of internal hydrogen bonds evident in bryostatin 1 is preserved in WN-1, and that upon docking WN-1 into the crystal structure of the C1b domain of PKCδ, the binding mode of bryostatin 1 is reproduced. The collective data emphasize the critical contribution of the B-ring to the function of the upper portion of the molecule in conferring a bryostatin-like pattern of biological activity.

Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software

Metabolism has been identified as a defining factor in drug development success or failure because of its impact on many aspects of drug pharmacology, including bioavailability, half-life and toxicity. In this article, we provide an outline and descriptions of the resources for metabolism-related property predictions that are currently either freely or commercially available to the public. These resources include databases with data on, and software for prediction of, several end points: metabolite formation, sites of metabolic transformation, binding to metabolizing enzymes and metabolic stability. We attempt to place each tool in historical context and describe, wherever possible, the data it was based on. For predictions of interactions with metabolizing enzymes, we show a typical set of results for a small test set of compounds. Our aim is to give a clear overview of the areas and aspects of metabolism prediction in which the currently available resources are useful and accurate, and the areas in which they are inadequate or missing entirely.

Computational tools and resources for metabolism-related property predictions. 2. Application to prediction of half-life time in human liver microsomes

BACKGROUND The most important factor affecting metabolic excretion of compounds from the body is their half-life time. This provides an indication of compound stability of, for example, drug molecules. We report on our efforts to develop QSAR models for metabolic stability of compounds, based on in vitro half-life assay data measured in human liver microsomes. METHOD A variety of QSAR models generated using different statistical methods and descriptor sets implemented in both open-source and commercial programs (KNIME, GUSAR and StarDrop) were analyzed. The models obtained were compared using four different external validation sets from public and commercial data sources, including two smaller sets of in vivo half-life data in humans. CONCLUSION In many cases, the accuracy of prediction achieved on one external test set did not correspond to the results achieved with another test set. The most predictive models were used for predicting the metabolic stability of compounds from the open NCI database, the results of which are publicly available on the NCI/CADD Group web server ( http://cactus.nci.nih.gov ).

A new approach to radial basis function approximation and its application to QSAR.

We describe a novel approach to RBF approximation, which combines two new elements: (1) linear radial basis functions and (2) weighting the model by each descriptor’s contribution. Linear radial basis functions allow one to achieve more accurate predictions for diverse data sets. Taking into account the contribution of each descriptor produces more accurate similarity values used for model development. The method was validated on 14 public data sets comprising nine physicochemical properties and five toxicity endpoints. We also compared the new method with five different QSAR methods implemented in the EPA T.E.S.T. program. Our approach, implemented in the program GUSAR, showed a reasonable accuracy of prediction and high coverage for all external test sets, providing more accurate prediction results than the comparison methods and even the consensus of these methods. Using our new method, we have created models for physicochemical and toxicity endpoints, which we have made freely available in the form of an online service at http://cactus.nci.nih.gov/chemical/apps/cap.