Cyclic Peptides as Protein Kinase Inhibitors: Structure–Activity Relationship and Molecular Modeling

Under-expression or overexpression of protein kinases has been shown to be associated with unregulated cell signal transduction in cancer cells. Therefore, there is major interest in designing protein kinase inhibitors as anticancer agents. We have previously reported [WR]5, a peptide containing alternative arginine (R) and tryptophan (W) residues as a non-competitive c-Src tyrosine kinase inhibitor. A number of larger cyclic peptides containing alternative hydrophobic and positively charged residues [WR]x (x = 6–9) and hybrid cyclic-linear peptides, [R6K]W6 and [R5K]W7, containing R and W residues were evaluated for their protein kinase inhibitory potency. Among all the peptides, cyclic peptide [WR]9 was found to be the most potent tyrosine kinase inhibitor. [WR]9 showed higher inhibitory activity (IC50 = 0.21 μM) than [WR]5, [WR]6, [WR]7, and [WR]8 with IC50 values of 0.81, 0.57, 0.35, and 0.33 μM, respectively, against c-Src kinase as determined by a radioactive assay using [γ-33P]ATP. Consistent with the result above, [WR]9 inhibited other protein kinases such as Abl kinase activity with an IC50 value of 0.35 μM, showing 2.2-fold higher inhibition than [WR]5 (IC50 = 0.79 μM). [WR]9 also inhibited PKCa kinase activity with an IC50 value of 2.86 μM, approximately threefold higher inhibition than [WR]5 (IC50 = 8.52 μM). A similar pattern was observed against Braf, c-Src, Cdk2/cyclin A1, and Lck. [WR]9 exhibited IC50 values of 9 is consistently more potent than other cyclic peptides with a smaller ring size and hybrid cyclic-linear peptides [R6K]W6 and [R5K]W7 against selected protein kinases. Thus, the presence of R and W residues in the ring, ring size, and the number of amino acids in the structure of the cyclic peptide were found to be critical in protein kinase inhibitory potency. We identified three putative binding pockets through automated blind docking of cyclic peptides [WR](5–9). The most populated pocket is located between the SH2, SH3, and N-lobe domains on the opposite side of the ATP binding site. The second putative pocket is formed by the same domains and located on the ATP binding site side of the protein. Finally, a third pocket was identified between the SH2 and SH3 domains. These results are consistent with the non-competitive nature of the inhibition displayed by these molecules. Molecular dynamics simulations of the protein–peptide complexes indicate that the presence of either [WR]5 or [WR]9 affects the plasticity of the protein and in particular the volume of the ATP binding site pocket in different ways. These results suggest that the second pocket is most likely the site where these peptides bind and offer a plausible rationale for the increased affinity of [WR]9

Accelerating AutoDock4 with GPUs and Gradient-Based Local Search

AutoDock4 is a widely used program for docking small molecules to macromolecular targets. It describes ligand-receptor interactions using a physics-inspired scoring function that has been proven useful in a variety of drug discovery projects. However, compared to more modern and recent software, AutoDock4 has longer execution times, limiting its applicability to large scale dockings. To address this problem, we describe an OpenCL implementation of AutoDock4, called AutoDock-GPU, that leverages the highly parallel architecture of GPU hardware to reduce docking runtime by up to 350-fold with respect to a single-threaded process. Moreover, we introduce the gradient-based local search method ADADELTA, as well as an improved version of the Solis-Wets random optimizer from AutoDock4. These efficient local search algorithms significantly reduce the number of calls to the scoring function that are needed to produce good results. The improvements reported here, both in terms of docking throughput and search efficiency, facilitate the use of the AutoDock4 scoring function in large scale virtual screening

Integrating Biomolecular Analysis and Visual Programming: Flexibility and Interactivity in the Design of Bioinformatics Tools

One of the challenges in bio-computing is to enable the efficient use of a wide variety of rapidly evolving computational methods to simulate, analyze and understand complex interactions of molecular systems. Our laboratory is interested in the development of novel computational technologies and in the application of these technologies to the analysis and understanding of complex biological systems. We have been using the Python programming language as a platform to develop reusable and interoperable components dealing with different aspects of structural bioinformatics. These components are the basic building blocks from which several domain specific applications have been developed. In this paper we describe the integration of two applications developed in our laboratory: PMV and a visual-programming environment. PMV is a general purpose, command-driven molecular visualization and manipulation program built from reusable software components. The visual-programming environment enables a user to build interactively networks describing novel combinations of computational methods. We describe several applications demonstrating the synergy created by combining these two programs. 1