Highly engineered biocatalysts for efficient small molecule pharmaceutical synthesis.

Technologies for the engineering of biocatalysts for efficient synthesis of pharmaceutical targets have advanced dramatically over the last few years. Integration of computational methods for structural modeling, combined with high through put methods for expression and screening of biocatalysts and algorithms for mining experimental data, have allowed the creation of highly engineered biocatalysts for the efficient synthesis of pharmaceuticals. Methods for the synthesis of chiral alcohols and amines have been particularly successful, along with the creation of non-natural activities for such desirable reactions as cyclopropanation and esterification.

Using compound similarity and functional domain composition for prediction of drug-target interaction networks.

Study of interactions between drugs and target proteins is an essential step in genomic drug discovery. It is very hard to determine the compound-protein interactions or drug-target interactions by experiment alone. As supplementary, effective prediction model using machine learning or data mining methods can provide much help. In this study, a prediction method based on Nearest Neighbor Algorithm and a novel metric, which was obtained by combining compound similarity and functional domain composition, was proposed. The target proteins were divided into the following groups: enzymes, ion channels, G protein-coupled receptors, and nuclear receptors. As a result, four predictors with the optimal parameters were established. The overall prediction accuracies, evaluated by jackknife cross-validation test, for four groups of target proteins are 90.23%, 94.74%, 97.80%, and 97.51%, respectively, indicating that compound similarity and functional domain composition are very effective to predict drug-target interaction networks.

Cholesterol-related genes in Alzheimer's disease.

Experimental data show that cholesterol can modulate central processes in the pathogenesis of Alzheimer's disease (AD). The epidemiological link between elevated plasma cholesterol at midlife and increased risk for AD and the possibility that 3-hydroxy-3-methylglutaryl-coenzym A reductase inhibitors (statins) may be protective against AD support a role of cholesterol metabolism in AD and have rendered it a potential therapeutic target in the treatment and prevention of the disease. The strong association of AD and AD endophenotypes with the APOE gene provides a genetic link between AD and cholesterol metabolism, because the apolipoprotein E (ApoE) is the most prevalent cholesterol transport protein in the central nervous system. Against this background several other genes with a role in cholesterol metabolism have been investigated for association with AD. In this review a compilation of genes related to cholesterol based on the information of the AmiGo gene ontology database is matched with the AlzGene database of AD candidate genes. 56 out of 149 (37.6%) genes with a relation to cholesterol metabolism have been investigated for association with AD. Given that only 660 out of about 23,000 (2.9%) genes have been assessed in hypothesis-driven candidate gene studies on AD, the cholesterol metabolic pathway is strongly represented among these genes. Among 34 cholesterol-related genes for which association with AD has been described APOE, CH25H, CLU, LDLR, SORL1 outstand with positive meta-analyses. However, it is unclear, if their association with AD is mediated by cholesterol-related mechanisms or by more specific direct effects of the respective proteins on Abeta metabolism.

Cooperation and cheating in microbial exoenzyme production--theoretical analysis for biotechnological applications.

The engineering of microorganisms to produce a variety of extracellular enzymes (exoenzymes), for example for producing renewable fuels and in biodegradation of xenobiotics, has recently attracted increasing interest. Productivity is often reduced by "cheater" mutants, which are deficient in exoenzyme production and benefit from the product provided by the "cooperating" cells. We present a game-theoretical model to analyze population structure and exoenzyme productivity in terms of biotechnologically relevant parameters. For any given population density, three distinct regimes are predicted: when the metabolic effort for exoenzyme production and secretion is low, all cells cooperate; at intermediate metabolic costs, cooperators and cheaters coexist; while at high costs, all cells use the cheating strategy. These regimes correspond to the harmony game, snowdrift game, and Prisoner's Dilemma, respectively. Thus, our results indicate that microbial strains engineered for exoenzyme production will not, under appropriate conditions, be outcompeted by cheater mutants. We also analyze the dependence of the population structure on cell density. At low costs, the fraction of cooperating cells increases with decreasing cell density and reaches unity at a critical threshold. Our model provides an estimate of the cell density maximizing exoenzyme production.