Predicting active site residue annotations in the Pfam database

<p>Abstract</p> <p>Background</p> <p>Approximately 5% of Pfam families are enzymatic, but only a small fraction of the sequences within these families (<0.5%) have had the residues responsible for catalysis determined. To increase the active site annotations in the Pfam database, we have developed a strict set of rules, chosen to reduce the rate of false positives, which enable the transfer of experimentally determined active site residue data to other sequences within the same Pfam family.</p> <p>Description</p> <p>We have created a large database of predicted active site residues. On comparing our active site predictions to those found in UniProtKB, Catalytic Site Atlas, PROSITE and <it>MEROPS </it>we find that we make many novel predictions. On investigating the small subset of predictions made by these databases that are not predicted by us, we found these sequences did not meet our strict criteria for prediction. We assessed the sensitivity and specificity of our methodology and estimate that only 3% of our predicted sequences are false positives.</p> <p>Conclusion</p> <p>We have predicted 606110 active site residues, of which 94% are not found in UniProtKB, and have increased the active site annotations in Pfam by more than 200 fold. Although implemented for Pfam, the tool we have developed for transferring the data can be applied to any alignment with associated experimental active site data and is available for download. Our active site predictions are re-calculated at each Pfam release to ensure they are comprehensive and up to date. They provide one of the largest available databases of active site annotation.</p

Socioeconomic inequalities in cancer survival in Scotland 1986–2000

We analysed trends in 5-year survival of the 18 commonest cancers in Scotland diagnosed between 1986 and 2000 and followed up to 2004 in each of five deprivation groups based on patients postcode of residence at diagnosis. We estimated relative survival up to 5 years after diagnosis, adjusting for the different background mortality in each deprivation group by age, sex and calendar period. We estimated trends in overall survival and in the deprivation gap in survival up to 2004. Five-year survival improved for all malignancies except bladder cancer and was associated with a widening in the deprivation gap in survival. For 25 of 30 cancer–sex combinations examined, 5-year survival was lower among more deprived patients diagnosed during 1996–2000, and the deprivation gap in survival had widened since 1986–1990 for 15 of these 25 cancers, similar to the trends seen in England and Wales

Quasi-Normal Modes of Stars and Black Holes

Perturbations of stars and black holes have been one of the main topics of relativistic astrophysics for the last few decades. They are of particular importance today, because of their relevance to gravitational wave astronomy. In this review we present the theory of quasi-normal modes of compact objects from both the mathematical and astrophysical points of view. The discussion includes perturbations of black holes (Schwarzschild, Reissner-Nordstr\"om, Kerr and Kerr-Newman) and relativistic stars (non-rotating and slowly-rotating). The properties of the various families of quasi-normal modes are described, and numerical techniques for calculating quasi-normal modes reviewed. The successes, as well as the limits, of perturbation theory are presented, and its role in the emerging era of numerical relativity and supercomputers is discussed.Comment: 74 pages, 7 figures, Review article for "Living Reviews in Relativity

Identification of Coevolving Residues and Coevolution Potentials Emphasizing Structure, Bond Formation and Catalytic Coordination in Protein Evolution

The structure and function of a protein is dependent on coordinated interactions between its residues. The selective pressures associated with a mutation at one site should therefore depend on the amino acid identity of interacting sites. Mutual information has previously been applied to multiple sequence alignments as a means of detecting coevolutionary interactions. Here, we introduce a refinement of the mutual information method that: 1) removes a significant, non-coevolutionary bias and 2) accounts for heteroscedasticity. Using a large, non-overlapping database of protein alignments, we demonstrate that predicted coevolving residue-pairs tend to lie in close physical proximity. We introduce coevolution potentials as a novel measure of the propensity for the 20 amino acids to pair amongst predicted coevolutionary interactions. Ionic, hydrogen, and disulfide bond-forming pairs exhibited the highest potentials. Finally, we demonstrate that pairs of catalytic residues have a significantly increased likelihood to be identified as coevolving. These correlations to distinct protein features verify the accuracy of our algorithm and are consistent with a model of coevolution in which selective pressures towards preserving residue interactions act to shape the mutational landscape of a protein by restricting the set of admissible neutral mutations

Genetic counselling for psychiatric disorders: accounts of psychiatric health professionals in the United Kingdom

Genetic counselling is not routinely offered for psychiatric disorders in the United Kingdom through NHS regional clinical genetics departments. However, recent genomic advances, confirming a genetic contribution to mental illness, are anticipated to increase demand for psychiatric genetic counselling. This is the first study of its kind to employ qualitative methods of research to explore accounts of psychiatric health professionals regarding the prospects for genetic counselling services within clinical psychiatry in the UK. Data were collected from 32 questionnaire participants, and 9 subsequent interviewees. Data analysis revealed that although participants had not encountered patients explicitly demanding psychiatric genetic counselling, psychiatric health professionals believe that such a service would be useful and desirable. Genomic advances may have significant implications for genetic counselling in clinical psychiatry even if these discoveries do not lead to genetic testing. Psychiatric health professionals describe clinical genetics as a skilled profession capable of combining complex risk communication with much needed psychosocial support. However, participants noted barriers to the implementation of psychiatric genetic counselling services including, but not limited to, the complexities of uncertainty in psychiatric diagnoses, patient engagement and ethical concerns regarding limited capacity

CMASA: an accurate algorithm for detecting local protein structural similarity and its application to enzyme catalytic site annotation

<p>Abstract</p> <p>Background</p> <p>The rapid development of structural genomics has resulted in many "unknown function" proteins being deposited in Protein Data Bank (PDB), thus, the functional prediction of these proteins has become a challenge for structural bioinformatics. Several sequence-based and structure-based methods have been developed to predict protein function, but these methods need to be improved further, such as, enhancing the accuracy, sensitivity, and the computational speed. Here, an accurate algorithm, the CMASA (Contact MAtrix based local Structural Alignment algorithm), has been developed to predict unknown functions of proteins based on the local protein structural similarity. This algorithm has been evaluated by building a test set including 164 enzyme families, and also been compared to other methods.</p> <p>Results</p> <p>The evaluation of CMASA shows that the CMASA is highly accurate (0.96), sensitive (0.86), and fast enough to be used in the large-scale functional annotation. Comparing to both sequence-based and global structure-based methods, not only the CMASA can find remote homologous proteins, but also can find the active site convergence. Comparing to other local structure comparison-based methods, the CMASA can obtain the better performance than both FFF (a method using geometry to predict protein function) and SPASM (a local structure alignment method); and the CMASA is more sensitive than PINTS and is more accurate than JESS (both are local structure alignment methods). The CMASA was applied to annotate the enzyme catalytic sites of the non-redundant PDB, and at least 166 putative catalytic sites have been suggested, these sites can not be observed by the Catalytic Site Atlas (CSA).</p> <p>Conclusions</p> <p>The CMASA is an accurate algorithm for detecting local protein structural similarity, and it holds several advantages in predicting enzyme active sites. The CMASA can be used in large-scale enzyme active site annotation. The CMASA can be available by the mail-based server (<url>http://159.226.149.45/other1/CMASA/CMASA.htm</url>).</p

Automatic prediction of catalytic residues by modeling residue structural neighborhood

Background: Prediction of catalytic residues is a major step in characterizing the function of enzymes. In its simpler formulation, the problem can be cast into a binary classification task at the residue level, by predicting whether the residue is directly involved in the catalytic process. The task is quite hard also when structural information is available, due to the rather wide range of roles a functional residue can play and to the large imbalance between the number of catalytic and non-catalytic residues.Results: We developed an effective representation of structural information by modeling spherical regions around candidate residues, and extracting statistics on the properties of their content such as physico-chemical properties, atomic density, flexibility, presence of water molecules. We trained an SVM classifier combining our features with sequence-based information and previously developed 3D features, and compared its performance with the most recent state-of-the-art approaches on different benchmark datasets. We further analyzed the discriminant power of the information provided by the presence of heterogens in the residue neighborhood.Conclusions: Our structure-based method achieves consistent improvements on all tested datasets over both sequence-based and structure-based state-of-the-art approaches. Structural neighborhood information is shown to be responsible for such results, and predicting the presence of nearby heterogens seems to be a promising direction for further improvements.Journal ArticleResearch Support, N.I.H. Extramuralinfo:eu-repo/semantics/publishe

Attitudes and Practices Among Internists Concerning Genetic Testing

Many questions remain concerning whether, when, and how physicians order genetic tests, and what factors are involved in their decisions. We surveyed 220 internists from two academic medical centers about their utilization of genetic testing. Rates of genetic utilizations varied widely by disease. Respondents were most likely to have ordered tests for Factor V Leiden (16.8 %), followed by Breast/Ovarian Cancer (15.0 %). In the past 6 months, 65 % had counseled patients on genetic issues, 44 % had ordered genetic tests, 38.5 % had referred patients to a genetic counselor or geneticist, and 27.5 % had received ads from commercial labs for genetic testing. Only 4.5 % had tried to hide or disguise genetic information, and <2 % have had patients report genetic discrimination. Only 53.4 % knew of a geneticist/genetic counselor to whom to refer patients. Most rated their knowledge as very/somewhat poor concerning genetics (73.7 %) and guidelines for genetic testing (87.1 %). Most felt needs for more training on when to order tests (79 %), and how to counsel patients (82 %), interpret results (77.3 %), and maintain privacy (80.6 %). Physicians were more likely to have ordered a genetic test if patients inquired about genetic testing (p  < .001), and if physicians had a geneticist/genetic counselor to whom to refer patients (p  < .002), had referred patients to a geneticist/genetic counselor in the past 6 months, had more comfort counseling patients about testing (p  < .019), counseled patients about genetics, larger practices (p  < .032), fewer African‐American patients (p  < .027), and patients who had reported genetic discrimination (p  < .044). In a multiple logistic regression, ordering a genetic test was associated with patients inquiring about testing, having referred patients to a geneticist/genetic counselor and knowing how to order tests. These data suggest that physicians recognize their knowledge deficits, and are interested in training. These findings have important implications for future medical practice, research, and education

PIPS: Pathogenicity Island Prediction Software

The adaptability of pathogenic bacteria to hosts is influenced by the genomic plasticity of the bacteria, which can be increased by such mechanisms as horizontal gene transfer. Pathogenicity islands play a major role in this type of gene transfer because they are large, horizontally acquired regions that harbor clusters of virulence genes that mediate the adhesion, colonization, invasion, immune system evasion, and toxigenic properties of the acceptor organism. Currently, pathogenicity islands are mainly identified in silico based on various characteristic features: (1) deviations in codon usage, G+C content or dinucleotide frequency and (2) insertion sequences and/or tRNA genetic flanking regions together with transposase coding genes. Several computational techniques for identifying pathogenicity islands exist. However, most of these techniques are only directed at the detection of horizontally transferred genes and/or the absence of certain genomic regions of the pathogenic bacterium in closely related non-pathogenic species. Here, we present a novel software suite designed for the prediction of pathogenicity islands (pathogenicity island prediction software, or PIPS). In contrast to other existing tools, our approach is capable of utilizing multiple features for pathogenicity island detection in an integrative manner. We show that PIPS provides better accuracy than other available software packages. As an example, we used PIPS to study the veterinary pathogen Corynebacterium pseudotuberculosis, in which we identified seven putative pathogenicity islands