The conserved crown bridge loop at the catalytic centre of enzymes of the haloacid dehalogenase superfamily

The crown bridge loop is hexapeptide motif in which the backbone carbonyl group at position 1 is hydrogen bonded to the backbone imino groups of positions 4 and 6. Residues at positions 1 and 4–6 are held in a tight substructure, but different orientations of the plane of the peptide bond between positions 2 and 3 result in two conformers: the 2,3-αRαR crown bridge loop — found in approximately 7% of proteins — and the 2,3-βRαL crown bridge loop, found in approximately 1–2% of proteins. We constructed a relational database in which we identified 60 instances of the 2,3-βRαL conformer, and find that about half occur in enzymes of the haloacid dehalogenase (HAD) superfamily, where they are located next to the catalytic aspartate residue. Analysis of additional enzymes of the HAD superfamily in the extensive SCOPe dataset showed this crown bridge loop to be a conserved feature. Examination of available structures showed that the 2,3-βRαL conformation — but not the 2,3-αRαR conformation — allows the backbone carbonyl group at position 2 to interact with the essential Mg2+ ion. The possibility of interconversion between the 2,3-βRαL and 2,3-αRαR conformations during catalysis is discussed

Identification and characterization of two classes of G1 β-bulge

In standard β-bulges, a residue in one strand of a β-sheet forms hydrogen bonds to two successive residues (`1' and `2') of a second strand. Two categories, `classic' and `G1' β-bulges, are distinguished by their dihedral angles: 1,2-αRβR (classic) or 1,2-αLβR (G1). It had previously been observed that G1 β-bulges are most often found as components of two quite distinct composite structures, suggesting that a basis for further differentiation might exist. Here, it is shown that two subtypes of G1 β-bulges, G1α and G1β, may be distinguished by their conformation (αR or βR) at residue `0' of the second strand. β-Bulges that are constituents of the composite structure named the β-bulge loop are of the G1α type, whereas those that are constituents of the composite structure named β-link here are of the G1β type. A small proportion of G1β β-bulges, but not G1α β-bulges, occur in other contexts. There are distinctive differences in amino-acid composition and sequence pattern between these two types of G1 β-bulge which may have practical application in protein design

Motivated proteins: a web application for studying small three-dimensional protein motifs

<b>BACKGROUND:</b> Small loop-shaped motifs are common constituents of the three-dimensional structure of proteins. Typically they comprise between three and seven amino acid residues, and are defined by a combination of dihedral angles and hydrogen bonding partners. The most abundant of these are alphabeta-motifs, asx-motifs, asx-turns, beta-bulges, beta-bulge loops, beta-turns, nests, niches, Schellmann loops, ST-motifs, ST-staples and ST-turns.We have constructed a database of such motifs from a range of high-quality protein structures and built a web application as a visual interface to this. <b>DESCRIPTION:</b> The web application, Motivated Proteins, provides access to these 12 motifs (with 48 sub-categories) in a database of over 400 representative proteins. Queries can be made for specific categories or sub-categories of motif, motifs in the vicinity of ligands, motifs which include part of an enzyme active site, overlapping motifs, or motifs which include a particular amino acid sequence. Individual proteins can be specified, or, where appropriate, motifs for all proteins listed. The results of queries are presented in textual form as an (X)HTML table, and may be saved as parsable plain text or XML. Motifs can be viewed and manipulated either individually or in the context of the protein in the Jmol applet structural viewer. Cartoons of the motifs imposed on a linear representation of protein secondary structure are also provided. Summary information for the motifs is available, as are histograms of amino acid distribution, and graphs of dihedral angles at individual positions in the motifs. <b>CONCLUSION:</b> Motivated Proteins is a publicly and freely accessible web application that enables protein scientists to study small three-dimensional motifs without requiring knowledge of either Structured Query Language or the underlying database schem

NetTurnP – Neural Network Prediction of Beta-turns by Use of Evolutionary Information and Predicted Protein Sequence Features

UNLABELLED: β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino acid sequence. The individual β-turn types I, I', II, II', VIII, VIa1, VIa2, VIba and IV have been predicted based on classifications by PROMOTIF, and the two-class prediction of β-turn or not is a superset comprised of all β-turn types. The performance is evaluated using a golden set of non-homologous sequences known as BT426. Our two-class prediction method achieves a performance of: MCC=0.50, Qtotal=82.1%, sensitivity=75.6%, PPV=68.8% and AUC=0.864. We have compared our performance to eleven other prediction methods that obtain Matthews correlation coefficients in the range of 0.17-0.47. For the type specific β-turn predictions, only type I and II can be predicted with reasonable Matthews correlation coefficients, where we obtain performance values of 0.36 and 0.31, respectively. CONCLUSION: The NetTurnP method has been implemented as a webserver, which is freely available at http://www.cbs.dtu.dk/services/NetTurnP/. NetTurnP is the only available webserver that allows submission of multiple sequences

Measurement of the ZZγZZ\gamma and ZγγZ\gamma\gamma Couplings in ppˉp\bar p Collisions at s=1.8\sqrt{s} = 1.8 TeV

We have directly measured the ZZ-gamma and Z-gamma-gamma couplings by studying p pbar --> l+ l- gamma + X, (l = e, mu) events at the CM energy of 1.8$TeV with the D0 detector at the Fermilab Tevatron Collider. A fit to the transverse energy spectrum of the photon in the signal events, based on the data set corresponding to an integrated luminosity of 13.9 pb^-1 ($13.3 pb^-1) for the electron (muon) channel, yields the following 95% confidence level limits on the anomalous CP-conserving ZZ-gamma couplings: -1.9 < h^Z_30 < 1.8 (h^Z_40 = 0), and -0.5 < h^Z_40 < 0.5 (h^Z_30 = 0), for a form-factor scale Lambda = 500 GeV. Limits for the Z-gamma-gamma$ couplings and CP-violating couplings are also discussed.Comment: 11 pages, 1 table, and 3 figure

Determinants of recovery from post-COVID-19 dyspnoea: analysis of UK prospective cohorts of hospitalised COVID-19 patients and community-based controls

Background The risk factors for recovery from COVID-19 dyspnoea are poorly understood. We investigated determinants of recovery from dyspnoea in adults with COVID-19 and compared these to determinants of recovery from non-COVID-19 dyspnoea. Methods We used data from two prospective cohort studies: PHOSP-COVID (patients hospitalised between March 2020 and April 2021 with COVID-19) and COVIDENCE UK (community cohort studied over the same time period). PHOSP-COVID data were collected during hospitalisation and at 5-month and 1-year follow-up visits. COVIDENCE UK data were obtained through baseline and monthly online questionnaires. Dyspnoea was measured in both cohorts with the Medical Research Council Dyspnoea Scale. We used multivariable logistic regression to identify determinants associated with a reduction in dyspnoea between 5-month and 1-year follow-up. Findings We included 990 PHOSP-COVID and 3309 COVIDENCE UK participants. We observed higher odds of improvement between 5-month and 1-year follow-up among PHOSP-COVID participants who were younger (odds ratio 1.02 per year, 95% CI 1.01–1.03), male (1.54, 1.16–2.04), neither obese nor severely obese (1.82, 1.06–3.13 and 4.19, 2.14–8.19, respectively), had no pre-existing anxiety or depression (1.56, 1.09–2.22) or cardiovascular disease (1.33, 1.00–1.79), and shorter hospital admission (1.01 per day, 1.00–1.02). Similar associations were found in those recovering from non-COVID-19 dyspnoea, excluding age (and length of hospital admission). Interpretation Factors associated with dyspnoea recovery at 1-year post-discharge among patients hospitalised with COVID-19 were similar to those among community controls without COVID-19. Funding PHOSP-COVID is supported by a grant from the MRC-UK Research and Innovation and the Department of Health and Social Care through the National Institute for Health Research (NIHR) rapid response panel to tackle COVID-19. The views expressed in the publication are those of the author(s) and not necessarily those of the National Health Service (NHS), the NIHR or the Department of Health and Social Care. COVIDENCE UK is supported by the UK Research and Innovation, the National Institute for Health Research, and Barts Charity. The views expressed are those of the authors and not necessarily those of the funders