Mutational activation of the Ras oncogene products (H-Ras, K-Ras, and N-Ras) is frequently observed in human cancers, making them promising anticancer drug targets. Nonetheless, no effective strategy has been available for the development of Ras inhibitors, partly owing to the absence of well-defined surface pockets suitable for drug binding. Only recently, such pockets have been found in the crystal structures of a unique conformation of Ras⋅GTP. Here we report the successful development of small-molecule Ras inhibitors by an in silico screen targeting a pocket found in the crystal structure of M-Ras⋅GTP carrying an H-Ras–type substitution P40D. The selected compound Kobe0065 and its analog Kobe2602 exhibit inhibitory activity toward H-Ras⋅GTP-c-Raf-1 binding both in vivo and in vitro. They effectively inhibit both anchorage-dependent and -independent growth and induce apoptosis of H- ras G12V –transformed NIH 3T3 cells, which is accompanied by down-regulation of downstream molecules such as MEK/ERK, Akt, and RalA as well as an upstream molecule, Son of sevenless. Moreover, they exhibit antitumor activity on a xenograft of human colon carcinoma SW480 cells carrying the K-ras G12V gene by oral administration. The NMR structure of a complex of the compound with H-Ras⋅GTP T35S , exclusively adopting the unique conformation, confirms its insertion into one of the surface pockets and provides a molecular basis for binding inhibition toward multiple Ras⋅GTP-interacting molecules. This study proves the effectiveness of our strategy for structure-based drug design to target Ras⋅GTP, and the resulting Kobe0065-family compounds may serve as a scaffold for the development of Ras inhibitors with higher potency and specificity.
Two virtual screening strategies, "query by bagging" (QBag) and "query by bagging with descriptor-sampling" (QBagDS), based on active learning were devised. The QBag strategy generates multiple structure-activity relationship rules by bagging and selects compounds to improve the rules. To find many structurally diverse hits, the QBagDS strategy generates rules by bagging with descriptor sampling. They can also use prior knowledge about hits to improve the efficiency at the beginning of screening. We performed simulation experiments and clustering analysis for several G-protein coupled receptors and showed that the QBag and QBagDS strategies outperform the conventional similarity-based strategy and that using both descriptor sampling and prior knowledge are effective for finding many hits. We applied the bagging with descriptor sampling strategy to novel hit finding, and 4 of the 10 selected compounds showed high inhibition.
Developing a peptide-based vaccine for the highly variable hepatitis C virus (HCV) remains a challenging task. Variant viruses not only escape antigen presentation but also persist in a patient as quasi-species. Such variants are often antagonistic to the responding T cell repertoire. To overcome these problems, we herein propose a cocktail vaccine consisting of a few epitope peptides, which make it possible to outpace the emergence of variant viruses. To design such a vaccine, we developed a way to identify HLA-A*2402-binding peptides efficiently by means of the computational scanning of the whole genome of the pathogen. Most of the predicted peptides exhibited strong binding to the HLA-A*2402 molecule, while also inducing CD8 T cell responses from the patients' peripheral blood mononuclear cells (PBMCs). Peptide-induced T cells were capable of lysing HCV-expressing HepG2 cells which process antigens endogenously. The amount of HCV core antigen in the patients' livers suggested that the lytic activity of the peptide-induced T cells was clearly in a range suitable for therapeutic use. If T cells were activated under optimal conditions by high density peptides, then they tended to be relatively tolerant of single amino acid variations for cytolysis. Finally, an analysis of the viral population isolated in Japan suggested no obvious changes due to immune evasion in the viral genome even in a host population highly biased toward HLA-A*2402.
BackgroundQuantitative structure-activity relationships (QSAR) analysis of peptides is helpful for designing various types of drugs such as kinase inhibitor or antigen. Capturing various properties of peptides is essential for analyzing two-dimensional QSAR. A descriptor of peptides is an important element for capturing properties. The atom pair holographic (APH) code is designed for the description of peptides and it represents peptides as the combination of thirty-six types of key atoms and their intermediate binding between two key atoms.ResultsThe substructure pair descriptor (SPAD) represents peptides as the combination of forty-nine types of key substructures and the sequence of amino acid residues between two substructures. The size of the key substructures is larger and the length of the sequence is longer than traditional descriptors. Similarity searches on C5a inhibitor data set and kinase inhibitor data set showed that order of inhibitors become three times higher by representing peptides with SPAD, respectively. Comparing scope of each descriptor shows that SPAD captures different properties from APH.ConclusionQSAR/QSPR for peptides is helpful for designing various types of drugs such as kinase inhibitor and antigen. SPAD is a novel and powerful descriptor for various types of peptides. Accuracy of QSAR/QSPR becomes higher by describing peptides with SPAD.
Learning. -(FUJIWARA*, Y.; YAMASHITA, Y.; OSODA, T.; ASOGAWA, M.; FUKUSHIMA, C.; ASAO, M.; SHIMADZU, H.; NAKAO, K.; SHIMIZU, R.; J. Chem. Inf. Model. (J. Chem. Inf. Comput. Sci.) 48 (2008) 4, 930-940; Serv. Platform Lab., NEC Corp., Minato, Tokyo 108, Japan; Eng.) -Lindner 29-218
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