Design, synthesis and biological evaluation of novel N-sulfonylamidine-based derivatives as c-Met inhibitors via Cu-catalyzed three-component reaction.

In our continuing efforts to develop novel c-Met inhibitors as potential anticancer candidates, a series of new N-sulfonylamidine derivatives were designed, synthesized via Cu-catalyzed multicomponent reaction (MCR) as the key step, and evaluated for their in vitro biological activities against c-Met kinase and four cancer cell lines (A549, HT-29, MKN-45 and MDA-MB-231). Most of the target compounds showed moderate to significant potency at both the enzyme-based and cell-based assay and possessed selectivity for A549 and HT-29 cancer cell lines. The preliminary SAR studies demonstrated that compound 26af (c-Met IC50 = 2.89 nM) was the most promising compound compared with the positive foretinib, which exhibited the remarkable antiproliferative activities, with IC50 values ranging from 0.28 to 0.72 µM. Mechanistic studies of 26af showed the anticancer activity was closely related to the blocking phosphorylation of c-Met, leading to cell cycle arresting at G2/M phase and apoptosis of A549 cells by a concentration-dependent manner. The promising compound 26af was further identified as a relatively selective inhibitor of c-Met kinase, which also possessed an acceptable safety profile and favorable pharmacokinetic properties in BALB/c mouse. The favorable drug-likeness of 26af suggested that N-sulfonylamidines may be used as a promising scaffold for antitumor drug development. Additionally, the docking study and molecular dynamics simulations of 26af revealed a common mode of interaction with the binding site of c-Met. These positive results indicated that compound 26af is a potential anti-cancer candidate for clinical trials, and deserves further development as a selective c-Met inhibitor.

Predicting protein subcellular location using digital signal processing.

The biological functions of a protein are closely related to its attributes in a cell. With the rapid accumulation of newly found protein sequence data in databanks, it is highly desirable to develop an automated method for predicting the subcellular location of proteins. The establishment of such a predictor will expedite the functional determination of newly found proteins and the process of prioritizing genes and proteins identified by genomic efforts as potential molecular targets for drug design. The traditional algorithms for predicting these attributes were based solely on amino acid composition in which no sequence order effect was taken into account. To improve the prediction quality, it is necessary to incorporate such an effect. However, the number of possible patterns in protein sequences is extremely large, posing a formidable difficulty for realizing this goal. To deal with such difficulty, a well-developed tool in digital signal processing named digital Fourier transform (DFT) [1] was introduced. After being translated to a digital signal according to the hydrophobicity of each amino acid, a protein was analyzed by DFT within the frequency domain. A set of frequency spectrum parameters, thus obtained, were regarded as the factors to represent the sequence order effect. A significant improvement in prediction quality was observed by incorporating the frequency spectrum parameters with the conventional amino acid composition. One of the crucial merits of this approach is that many existing tools in mathematics and engineering can be easily applied in the predicting process. It is anticipated that digital signal processing may serve as a useful vehicle for many other protein science areas.

[Association study between NPY and YWHAH gene polymorphisms and schizophrenia].

A case-control study was carried out on a sample of 583 cases vs. 372 controls in the Chinese Han population, investigating several published polymorphisms in the YWHAH and NPY genes, which reported to be associated with schizophrenia. The polymorphism -134 (GCCTGCA)2-4, in the YWHAH was not analyzed for the failure of amplification, and the polymorphism T1128C in the NPY is not existent in the samples. The analysis was then emphasized on the variants -485C > T(NPY) and G753A(YWHAH). However, no significant differences of allele frequencies (with P values of 0.696 and 0.743, OR values of 1.041 and 0.962 respectively) or genotype frequencies (with P value of 0.45 and 0.75, chi2 = 1.51 and 0.58 respectively) among the matched groups were found. No sex-dependent effect was found either. Also,the analysis of the relative risk between the genotypes of the two genes indicates that the two genes could not cooperate with each other to add the risk of disease (P > 0.05). The results suggest that the polymorphisms - 485C > T (NPY) and G753A (YWHAH) are unlikely to be linked with genetic susceptibility to schizophrenia in the Chinese Han population.

[Progress in molecular biology study of DNA computer].

DNA (deoxyribonucleotide acids) computer is an emerging new study area that basically combines molecular biology study of DNA molecules and computational study on how to employ these specific molecules to calculate. In 1994 Adleman described his pioneering research on DNA computing in Science. This is the first experimental report on DNA computer study. In 2001 Benenson et al published a paper in Nature regarding a programmable and autonomous DNA computing device. Because of its Turing-like functions, the device is regarded as another milestone progress for DNA computer study. The main features of DNA computer are massively parallel computing ability and potential enormous data storage capacity. Comparing with conventional electronic computers, DNA molecules provide conceptually a revolution in computing, and more and more implications have been found in various disciplines. DNA computer studies have brought great progress not only in its own computing mechanisms, but also in DNA manipulation technologies especially nano-technology. This article presents the basic principles of DNA computer, its applications, its important relationship with genomic research and our comments on all above issues.

Application of pseudo amino acid composition for predicting protein subcellular location: stochastic signal processing approach.

The function of a protein is closely correlated with its subcellular location. With the success of human genome project and the rapid increase in the number of newly found protein sequences entering into data banks, it is highly desirable to develop an automated method for predicting the subcellular location of proteins. The establishment of such a predictor will no doubt expedite the functionality determination of newly found proteins and the process of prioritizing genes and proteins identified by genomics efforts as potential molecular targets for drug design. Based on the concept of pseudo amino acid composition originally proposed by K. C. Chou (Proteins: Struct. Funct. Genet. 43: 246-255, 2001), the digital signal processing approach has been introduced to partially incorporate the sequence order effect. One of the remarkable merits by doing so is that many existing tools in mathematics and engineering can be straightforwardly used in predicting protein subcellular location. The results thus obtained are quite encouraging. It is anticipated that the digital signal processing may serve as a useful vehicle for many other protein science areas as well.