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Zinc is one of the most important biologically active metals. Ten per cent of the human genome is thought to encode a zinc binding protein and its uses encompass catalysis, structural stability, gene expression and immunity. At present, there is no specific resource devoted to identifying and presenting all currently known zinc binding sites. Here we present ZincBind, a database of zinc binding sites and its web front-end. Using the structural data in the Protein Data Bank, ZincBind identifies every instance of zinc binding to a protein, identifies its binding site and clusters sites based on 90% sequence identity. There are currently 24 992 binding sites, clustered into 7489 unique sites. The data are available over the web where they can be browsed and downloaded, and via a REST API. ZincBind is regularly updated and will continue to be updated with new data and features.

Motivation Protein–protein interactions are vital for protein function with the average protein having between three and ten interacting partners. Knowledge of precise protein–protein interfaces comes from crystal structures deposited in the Protein Data Bank (PDB), but only 50% of structures in the PDB are complexes. There is therefore a need to predict protein–protein interfaces in silico and various methods for this purpose. Here we explore the use of a predictor based on structural features and which exploits random forest machine learning, comparing its performance with a number of popular established methods. Results On an independent test set of obligate and transient complexes, our IntPred predictor performs well (MCC = 0.370, ACC = 0.811, SPEC = 0.916, SENS = 0.411) and compares favourably with other methods. Overall, IntPred ranks second of six methods tested with SPPIDER having slightly better overall performance (MCC = 0.410, ACC = 0.759, SPEC = 0.783, SENS = 0.676), but considerably worse specificity than IntPred. As with SPPIDER, using an independent test set of obligate complexes enhanced performance (MCC = 0.381) while performance is somewhat reduced on a dataset of transient complexes (MCC = 0.303). The trade-off between sensitivity and specificity compared with SPPIDER suggests that the choice of the appropriate tool is application-dependent. Availability and implementation IntPred is implemented in Perl and may be downloaded for local use or run via a web server at www.bioinf.org.uk/intpred/. Contact andrew@bioinf.org.uk or andrew.martin@ucl.ac.uk Supplementary information Supplementary data are available at Bioinformatics online.

Polaromonas sp. str. GM1 is an aerobic, psychrotolerant, heterotrophic member of the Betaproteobacteria and is the only isolate capable of oxidising arsenite at temperatures below 10 °C. Sequencing of the aio gene cluster in GM1 revealed the presence of the aioB and aioA genes, which encode the arsenite oxidase but the regulatory genes typically found upstream of aioB in other members of the Proteobacteria were absent. The GM1 Aio was purified to homogeneity and was found to be a heterodimer. The enzyme contained Mo and Fe as cofactors and had, using the artificial electron acceptor 2,6-dichlorophenolindophenol, a Km for arsenite of 111.70 ± 0.88 μM and a Vmax of 12.16 ± 0.30 U mg(-1), which is the highest reported specific activity for any known Aio. The temperature-activity profiles of the arsenite oxidases from GM1 and the mesophilic betaproteobacterium Alcaligenes faecalis were compared and showed that the GM1 Aio was more active at low temperatures than that of A. faecalis. A homology model of the GM1 Aio was made using the X-ray crystal structure of the Aio from A. faecalis as the template. Structural changes that account for cold adaptation were identified and it was found that these resulted in increased enzyme flexibility and a reduction in the hydrophobicity of the core.

Familial hypercholesterolemia (FH) is caused predominately by variants in the low-density lipoprotein receptor gene (LDLR). We report here an update of the UCL LDLR variant database to include variants reported in the literature and in-house between 2008 and 2010, transfer of the database to LOVDv.2.0 platform (https://grenada.lumc.nl/LOVD2/UCL-Heart/home.php?select_db=LDLR) and pathogenicity analysis. The database now contains over 1288 different variants reported in FH patients: 55% exonic substitutions, 22% exonic small rearrangements (100 bp), 11% large rearrangements (100 bp), 2% promoter variants, 10% intronic variants and 1 variant in the 3' untranslated sequence. The distribution and type of newly reported variants closely matches that of the 2008 database, and we have used these variants (n= 223) as a representative sample to assess the utility of standard open access software (PolyPhen, SIFT, refined SIFT, Neural Network Splice Site Prediction Tool, SplicePort and NetGene2) and additional analyses (Single Amino Acid Polymorphism database, analysis of conservation and structure and Mutation Taster) for pathogenicity prediction. In combination, these techniques have enabled us to assign with confidence pathogenic predictions to 8/8 in-frame small rearrangements and 8/9 missense substitutions with previously discordant results from PolyPhen and SIFT analysis. Overall, we conclude that 79% of the reported variants are likely to be disease causing.