Biomedical subjects

William M Rockey

Publications and source records attributed to William M Rockey.

4 recordsLinked to original sources

PubMed2006

Structure selection for protein kinase docking and virtual screening: homology models or crystal structures?

There is currently far more sequence information than structural information available, and the ability to use homology models for virtual screening applications is desirable in many cases where structures have not yet been solved. This review focuses on the application of protein kinase homology models for virtual screening use. In addition to reviewing previous cases in which kinase homology models have been used in inhibitor design, we present new data - useful for template selection in homology modeling applications - indicating that the template structure with the highest sequence or structural similarity with the target structure may not always be the best choice. This new work explored the simple hypothesis that better results might be obtained for docking a ligand to a target receptor using a homology model of the target created from a different kinase template co-crystallized with the ligand, than from a crystal structure of the actual kinase target that is unliganded or bound to an unrelated ligand. This hypothesis was tested in docking studies of staurosporine with eight different kinases: AutoDock was used to dock staurosporine to homology models of each kinase created from staurosporine-bound template structures, and the results were compared with docking staurosporine to crystal structures of the target kinase that were obtained in complex with a non-staurosporine ligand or no ligand. It was found that the homology models performed as well as or better than the crystal structures, suggesting that using a homology model created from a template crystallized with a representative ligand may in some cases be a preferred approach, especially in virtual screening experiments that focus on enriching for members of a particular inhibitor class.

Crystallography, X-Ray↗

PubMed2006

Rapid computational identification of the targets of protein kinase inhibitors.

This review focuses primarily on computational methods for predicting the selectivity of small-molecule inhibitors of protein kinases. A detailed discussion is presented of two computational studies that have attempted to make inhibitor selectivity predictions on a kinome-wide scale, and studies describing other methodologies that might potentially be applied to this area are also outlined. As the availability of reliable experimental data measuring inhibitor binding affinities (as opposed to the degree of inhibition of the kinase reaction) is important for the development and validation of most computational methods, recent advances in the large-scale experimental acquisition of selectivity data are also discussed, and a comparison of recently published computational selectivity predictions against a large-scale experimental screen is provided.

Animals↗

PubMed2005

Rapid computational identification of the targets of protein kinase inhibitors.

We describe a method for rapidly computing the relative affinities of an inhibitor for all individual members of a family of homologous receptors. The approach, implemented in a new program, SCR, models inhibitor-receptor interactions in full atomic detail with an empirical energy function and includes an explicit account of flexibility in homology-modeled receptors through sampling of libraries of side chain rotamers. SCR's general utility was demonstrated by application to seven different protein kinase inhibitors: for each inhibitor, relative binding affinities with panels of approximately 20 protein kinases were computed and compared with experimental data. For five of the inhibitors (SB203580, purvalanol B, imatinib, H89, and hymenialdisine), SCR provided excellent reproduction of the experimental trends and, importantly, was capable of identifying the targets of inhibitors even when they belonged to different kinase families. The method's performance in a predictive setting was demonstrated by performing separate training and testing applications, and its key assumptions were tested by comparison with a number of alternative approaches employing the ligand-docking program AutoDock (Morris et al. J. Comput. Chem. 1998, 19, 1639-1662). These comparison tests included using AutoDock in nondocking and docking modes and performing energy minimizations of inhibitor-kinase complexes with the molecular mechanics code GROMACS (Berendsen et al. Comput. Phys. Commun. 1995, 91, 43-56). It was found that a surprisingly important aspect of SCR's approach is its assumption that the inhibitor be modeled in the same orientation for each kinase: although this assumption is in some respects unrealistic, calculations that used apparently more realistic approaches produced clearly inferior results. Finally, as a large-scale application of the method, SB203580, purvalanol B, and imatinib were screened against an almost full complement of 493 human protein kinases using SCR in order to identify potential new targets; the predicted targets of SB203580 were compared with those identified in recent proteomics-based experiments. These kinome-wide screens, performed within a day on a small cluster of PCs, indicate that explicit computation of inhibitor-receptor binding affinities has the potential to promote rapid discovery of new therapeutic targets for existing inhibitors.

Binding Sites↗

PubMed2002

Progress toward virtual screening for drug side effects.

The development and application of a computational protocol for conducting virtual screens of drug side interactions is described. A conventional drug-docking algorithm (AutoDock) is used to conduct two separate studies. First, a series of docking simulations is performed by using guanosine diphosphate and adenosine diphosphate as prototype drugs with the goal of successfully differentiating known receptors from a large number of bait receptors. Using the electrostatic energy of the purine ring as a basis for discrimination allows the correct identification of receptors in blind studies with 100% specificity and 94% sensitivity. In a second study, similar methodology is used to investigate the binding of clinically relevant inhibitors (Gleevec, purvalanol A, and hymenialdisine) to a variety of protein kinase targets. Overall, excellent agreement between experimental and predicted preferences for kinase targets is obtained. An important conclusion from the latter study is that homology-modeled structures of putative receptors may reasonably be used as targets for docking when directly solved crystal structures are not available. The prospects for routine application of the methodology as a means of identifying potential side interactions of candidate drugs are discussed.

Adenosine Diphosphate↗