Asymmetric Amination of meso-Epoxide with Vegetable Powder as a Low-Toxicity Catalyst.

This paper describes the scope and limitation of substrates subjected to asymmetric amination with epoxides catalyzed by a soluble soybean polysaccharide (Soyafibe S-DN), which we recently discovered from the reaction of 1,2-epoxycyclohexane with cyclopropylamine. Various meso-epoxides reacted with various amines afforded the corresponding products with good enantiomeric selectivity. Since it was found that pectin was found to have a catalytic ability after screening commercially available polysaccharides, we studied 33 different vegetable powders having pectic substances, and we found that many vegetable powders showed catalytic ability. These results should guide in using vegetable components as low-toxic catalysts for the production of pharmaceuticals.

GPSuc: Global Prediction of Generic and Species-specific Succinylation Sites by aggregating multiple sequence features.

Lysine succinylation is one of the dominant post-translational modification of the protein that contributes to many biological processes including cell cycle, growth and signal transduction pathways. Identification of succinylation sites is an important step for understanding the function of proteins. The complicated sequence patterns of protein succinylation revealed by proteomic studies highlight the necessity of developing effective species-specific in silico strategies for global prediction succinylation sites. Here we have developed the generic and nine species-specific succinylation site classifiers through aggregating multiple complementary features. We optimized the consecutive features using the Wilcoxon-rank feature selection scheme. The final feature vectors were trained by a random forest (RF) classifier. With an integration of RF scores via logistic regression, the resulting predictor termed GPSuc achieved better performance than other existing generic and species-specific succinylation site predictors. To reveal the mechanism of succinylation and assist hypothesis-driven experimental design, our predictor serves as a valuable resource. To provide a promising performance in large-scale datasets, a web application was developed at http://kurata14.bio.kyutech.ac.jp/GPSuc/.