Biological conversion of the aqueous wastes from hydrothermal liquefaction of algae and pine wood by Rhodococci.

In this study, R. opacus PD630, R. jostii RHA1, R. jostii RHA1 VanA-, and their co-culture were employed to convert hydrothermal liquefaction aqueous waste (HTLAW) into lipids. After 11days, the COD reduction of algal-HTLAW reached 93.4% and 92.7% by R. jostii RHA1 and its mutant VanA-, respectively. Woody-HTLAW promoted lipid accumulation of 0.43glipid/gcell dry weight in R. opacus PD630 cells. Additionally, the total number of chemicals in HTLAW decreased by over 1/3 after 7days of coculture, and 0.10g/L and 0.46g/L lipids were incrementally accumulated in the cellular mass during the fermentation of wood- and algal-HTLAW, respectively. The GC-MS data supported that different metabolism pathways were followed when these Rhodococci strains degraded algae- and woody-HTLAW. These results indicated promising potential of bioconversion of under-utilized carbon and toxic compounds in HTLAW into useful products by selected Rhodococci.

Identification of DNA-binding proteins using support vector machine with sequence information.

DNA-binding proteins are fundamentally important in understanding cellular processes. Thus, the identification of DNA-binding proteins has the particularly important practical application in various fields, such as drug design. We have proposed a novel approach method for predicting DNA-binding proteins using only sequence information. The prediction model developed in this study is constructed by support vector machine-sequential minimal optimization (SVM-SMO) algorithm in conjunction with a hybrid feature. The hybrid feature is incorporating evolutionary information feature, physicochemical property feature, and two novel attributes. These two attributes use DNA-binding residues and nonbinding residues in a query protein to obtain DNA-binding propensity and nonbinding propensity. The results demonstrate that our SVM-SMO model achieves 0.67 Matthew's correlation coefficient (MCC) and 89.6% overall accuracy with 88.4% sensitivity and 90.8% specificity, respectively. Performance comparisons on various features indicate that two novel attributes contribute to the performance improvement. In addition, our SVM-SMO model achieves the best performance than state-of-the-art methods on independent test dataset.