A computational approach for detecting peptidases and their specific inhibitors at the genome level

<p>Abstract</p> <p>Background</p> <p>Peptidases are proteolytic enzymes responsible for fundamental cellular activities in all organisms. Apparently about 2–5% of the genes encode for peptidases, irrespectively of the organism source. The basic peptidase function is "protein digestion" and this can be potentially dangerous in living organisms when it is not strictly controlled by specific inhibitors. In genome annotation a basic question is to predict gene function. Here we describe a computational approach that can filter peptidases and their inhibitors out of a given proteome. Furthermore and as an added value to MEROPS, a specific database for peptidases already available in the public domain, our method can predict whether a pair of peptidase/inhibitor can interact, eventually listing all possible predicted ligands (peptidases and/or inhibitors).</p> <p>Results</p> <p>We show that by adopting a decision-tree approach the accuracy of PROSITE and HMMER in detecting separately the four major peptidase types (Serine, Aspartic, Cysteine and Metallo- Peptidase) and their inhibitors among a non redundant set of globular proteins can be improved by some percentage points with respect to that obtained with each method separately. More importantly, our method can then predict pairs of peptidases and interacting inhibitors, scoring a joint global accuracy of 99% with coverage for the positive cases (peptidase/inhibitor) close to 100% and a correlation coefficient of 0.91%. In this task the decision-tree approach outperforms the single methods.</p> <p>Conclusion</p> <p>The decision-tree can reliably classify protein sequences as peptidases or inhibitors, belonging to a certain class, and can provide a comprehensive list of possible interacting pairs of peptidase/inhibitor. This information can help the design of experiments to detect interacting peptidase/inhibitor complexes and can speed up the selection of possible interacting candidates, without searching for them separately and manually combining the obtained results. A web server specifically developed for annotating peptidases and their inhibitors (HIPPIE) is available at <url>http://gpcr.biocomp.unibo.it/cgi/predictors/hippie/pred_hippie.cgi</url></p

Real-time Interobserver Agreement in Bowel Ultrasonography for Diagnostic Assessment in Patients With Crohn's Disease:An International Multicenter Study

Background The unavailability of standardized parameters in bowel ultrasonography (US) commonly used in Crohn's disease (CD) and the shortage of skilled ultrasonographers are 2 limiting factors in the use of this imaging modality around the world. The aim of this study is to evaluate interobserver agreement among experienced sonographers in the evaluation of bowel US parameters in order to improve standardization in imaging reporting and interpretation. Methods Fifteen patients with an established diagnosis of CD underwent blinded bowel US performed by 6 experienced sonographers. Prior to the evaluation, the sonographers and clinical and radiological IBD experts met to formally define the US parameters. Interobserver agreement was tested with the Quatto method (s). Results All operators agreed on the presence/absence of CD lesions and distinguished absence of/mild activity or moderate/severe lesions in all patients. S values were moderate for bowel wall thickness (s = 0.48, P = n.s.), bowel wall pattern (s = 0.41, P = n.s.), vascularization (s = 0.52, P = n.s.), and presence of lymphnodes (s = 0.61, P = n.s.). Agreement was substantial for lesion location (s = 0.68, P = n.s.), fistula (s = 0.74, P = n.s.), phlegmon (s = 0.78, P = 0.04), and was almost perfect for abscess (s = 0.95, P = 0.02). Poor agreement was observed for mesenteric adipose tissue alteration, lesion extent, stenosis, and prestenotic dilation. Conclusions In this study, the majority of the US parameters used in CD showed moderate/substantial agreement. The development of shared US imaging interpretation patterns among sonographers will lead to improved comparability of US results among centers and facilitate the development of multicenter studies and the spread of bowel US training, thereby allowing a wider adoption of this useful technique

INNOVATIONS in earthquake risk reduction for resilience: RECENT advances and challenges

The Sendai Framework for Disaster Risk Reduction 2015-2030 (SFDRR) highlights the importance of scientific research, supporting the ‘availability and application of science and technology to decision making’ in disaster risk reduction (DRR). Science and technology can play a crucial role in the world’s ability to reduce casualties, physical damage, and interruption to critical infrastructure due to natural hazards and their complex interactions. The SFDRR encourages better access to technological innovations combined with increased DRR investments in developing cost-effective approaches and tackling global challenges. To this aim, it is essential to link multi- and interdisciplinary research and technological innovations with policy and engineering/DRR practice. To share knowledge and promote discussion on recent advances, challenges, and future directions on ‘Innovations in Earthquake Risk Reduction for Resilience’, a group of experts from academia and industry met in London, UK, in July 2019. The workshop focused on both cutting-edge ‘soft’ (e.g., novel modelling methods/frameworks, early warning systems, disaster financing and parametric insurance) and ‘hard’ (e.g., novel structural systems/devices for new structures and retrofitting of existing structures, sensors) risk-reduction strategies for the enhancement of structural and infrastructural earthquake safety and resilience. The workshop highlighted emerging trends and lessons from recent earthquake events and pinpointed critical issues for future research and policy interventions. This paper summarises some of the key aspects identified and discussed during the workshop to inform other researchers worldwide and extend the conversation to a broader audience, with the ultimate aim of driving change in how seismic risk is quantified and mitigated

Predicting protein stability changes from sequence with Support Vector Machines

none4Motivation: The prediction of protein stability change upon mutations is a key problem for understanding protein folding and misfolding. Presently methods are available to predict stability changes only when the atomic structure of the protein is available. Methods addressing the same task starting from the protein sequence are however necessary in order to complete genome annotation, especially in relation to single nucleotide polymorphisms (SNPs) and related diseases. Results: We develop a method based on support vector machines (SVM) the starting from the protein sequence predicts the sign and the value of free energy stability change upon single point mutation. We show that the accuracy of our predictor is as high as 77% in the specific task of predicting the ddG sign related to the corresponding protein stability. When predicting the ddG values, a satisfying correlation agreement with the experimental data is also found. As a final blind benchmark, the predictor is applied to proteins with a set of disease-related SNPs, for which thermodynamics data are also known. We found that our predictions corroborate the view that disease-related mutations correspond to decrease of protein stability. Availability: www.gpcr2.biocomp.unibo.itnoneCapriotti, E.; Fariselli, P.; Calabrese, R.; Casadio, R.Capriotti, E.; Fariselli, Piero; Calabrese, R.; Casadio, R

Late relapse in the neck: considerations from a case of seminoma and review of the literature

--

WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation

SNPs&GO is a method for the prediction of deleterious Single Amino acid Polymorphisms (SAPs) using protein functional annotation. In this work, we present the web server implementation of SNPs&GO (WS-SNPs&GO). The server is based on Support Vector Machines (SVM) and for a given protein, its input comprises: the sequence and/or its three-dimensional structure (when available), a set of target variations and its functional Gene Ontology (GO) terms. The output of the server provides, for each protein variation, the probabilities to be associated to human diseases. RESULTS: The server consists of two main components, including updated versions of the sequence-based SNPs&GO (recently scored as one of the best algorithms for predicting deleterious SAPs) and of the structure-based SNPs&GO(3d) programs. Sequence and structure based algorithms are extensively tested on a large set of annotated variations extracted from the SwissVar database. Selecting a balanced dataset with more than 38,000 SAPs, the sequence-based approach achieves 81% overall accuracy, 0.61 correlation coefficient and an Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve of 0.88. For the subset of ~6,600 variations mapped on protein structures available at the Protein Data Bank (PDB), the structure-based method scores with 84% overall accuracy, 0.68 correlation coefficient, and 0.91 AUC. When tested on a new blind set of variations, the results of the server are 79% and 83% overall accuracy for the sequence-based and structure-based inputs, respectively. CONCLUSIONS: WS-SNPs&GO is a valuable tool that includes in a unique framework information derived from protein sequence, structure, evolutionary profile, and protein function. WS-SNPs&GO is freely available at http://snps.biofold.org/snps-and-go

WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation

BACKGROUND: SNPs&GO is a method for the prediction of deleterious Single Amino acid Polymorphisms (SAPs) using protein functional annotation. In this work, we present the web server implementation of SNPs&GO (WS-SNPs&GO). The server is based on Support Vector Machines (SVM) and for a given protein, its input comprises: the sequence and/or its three-dimensional structure (when available), a set of target variations and its functional Gene Ontology (GO) terms. The output of the server provides, for each protein variation, the probabilities to be associated to human diseases. RESULTS: The server consists of two main components, including updated versions of the sequence-based SNPs&GO (recently scored as one of the best algorithms for predicting deleterious SAPs) and of the structure-based SNPs&GO(3d) programs. Sequence and structure based algorithms are extensively tested on a large set of annotated variations extracted from the SwissVar database. Selecting a balanced dataset with more than 38,000 SAPs, the sequence-based approach achieves 81% overall accuracy, 0.61 correlation coefficient and an Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve of 0.88. For the subset of ~6,600 variations mapped on protein structures available at the Protein Data Bank (PDB), the structure-based method scores with 84% overall accuracy, 0.68 correlation coefficient, and 0.91 AUC. When tested on a new blind set of variations, the results of the server are 79% and 83% overall accuracy for the sequence-based and structure-based inputs, respectively. CONCLUSIONS: WS-SNPs&GO is a valuable tool that includes in a unique framework information derived from protein sequence, structure, evolutionary profile, and protein function. WS-SNPs&GO is freely available at http://snps.biofold.org/snps-and-g