Protein features as determinants of wild-type glycoside hydrolase thermostability.

Thermostable enzymes for conversion of lignocellulosic biomass into biofuels have significant advantages over enzymes with more moderate themostability due to the challenging application conditions. Experimental discovery of thermostable enzymes is highly cost intensive, and the development of in-silico methods guiding the discovery process would be of high value. To develop such an in-silico method and provide the data foundation of it, we determined the melting temperatures of 602 fungal glycoside hydrolases from the families GH5, 6, 7, 10, 11, 43, and AA9 (formerly GH61). We, then used sequence and homology modeled structure information of these enzymes to develop the ThermoP melting temperature prediction method. Futhermore, in the context of thermostability, we determined the relative importance of 160 molecular features, such as amino acid frequencies and spatial interactions, and exemplified their biological significance. The presented prediction method is made publicly available at http://www.cbs.dtu.dk/services/ThermoP.

Cofactory: sequence-based prediction of cofactor specificity of Rossmann folds.

Obtaining optimal cofactor balance to drive production is a challenge in metabolically engineered microbial production strains. To facilitate identification of heterologous enzymes with desirable altered cofactor requirements from native content, we have developed Cofactory, a method for prediction of enzyme cofactor specificity using only primary amino acid sequence information. The algorithm identifies potential cofactor binding Rossmann folds and predicts the specificity for the cofactors FAD(H2), NAD(H), and NADP(H). The Rossmann fold sequence search is carried out using hidden Markov models whereas artificial neural networks are used for specificity prediction. Training was carried out using experimental data from protein-cofactor structure complexes. The overall performance was benchmarked against an independent evaluation set obtaining Matthews correlation coefficients of 0.94, 0.79, and 0.65 for FAD(H2), NAD(H), and NADP(H), respectively. The Cofactory method is made publicly available at http://www.cbs.dtu.dk/services/Cofactory.

Hyperactivity of the Ero1α oxidase elicits endoplasmic reticulum stress but no broad antioxidant response.

Oxidizing equivalents for the process of oxidative protein folding in the endoplasmic reticulum (ER) of mammalian cells are mainly provided by the Ero1α oxidase. The molecular mechanisms that regulate Ero1α activity in order to harness its oxidative power are quite well understood. However, the overall cellular response to oxidative stress generated by Ero1α in the lumen of the mammalian ER is poorly characterized. Here we investigate the effects of overexpressing a hyperactive mutant (C104A/C131A) of Ero1α. We show that Ero1α hyperactivity leads to hyperoxidation of the ER oxidoreductase ERp57 and induces expression of two established unfolded protein response (UPR) targets, BiP (immunoglobulin-binding protein) and HERP (homocysteine-induced ER protein). These effects could be reverted or aggravated by N-acetylcysteine and buthionine sulfoximine, respectively. Because both agents manipulate the cellular glutathione redox buffer, we conclude that the observed effects of Ero1α-C104A/C131A overexpression are likely caused by an oxidative perturbation of the ER glutathione redox buffer. In accordance, we show that Ero1α hyperactivity affects cell viability when cellular glutathione levels are compromised. Using microarray analysis, we demonstrate that the cell reacts to the oxidative challenge caused by Ero1α hyperactivity by turning on the UPR. Moreover, this analysis allowed the identification of two new targets of the mammalian UPR, CRELD1 and c18orf45. Interestingly, a broad antioxidant response was not induced. Our findings suggest that the hyperoxidation generated by Ero1α-C104A/C131A is addressed in the ER lumen and is unlikely to exert oxidative injury throughout the cell.

deFUME: Dynamic exploration of functional metagenomic sequencing data.

BACKGROUND: Functional metagenomic selections represent a powerful technique that is widely applied for identification of novel genes from complex metagenomic sources. However, whereas hundreds to thousands of clones can be easily generated and sequenced over a few days of experiments, analyzing the data is time consuming and constitutes a major bottleneck for experimental researchers in the field. FINDINGS: Here we present the deFUME web server, an easy-to-use web-based interface for processing, annotation and visualization of functional metagenomics sequencing data, tailored to meet the requirements of non-bioinformaticians. The web-server integrates multiple analysis steps into one single workflow: read assembly, open reading frame prediction, and annotation with BLAST, InterPro and GO classifiers. Analysis results are visualized in an online dynamic web-interface. CONCLUSION: The deFUME webserver provides a fast track from raw sequence to a comprehensive visual data overview that facilitates effortless inspection of gene function, clustering and distribution. The webserver is available at cbs.dtu.dk/services/deFUME/and the source code is distributed at github.com/EvdH0/deFUME.