Predicting enzyme class from protein structure using Bayesian classification

Predicting enzyme class from protein structure parameters is a challenging problem in protein analysis. We developed a method to predict enzyme class that combines the strengths of statistical and data-mining methods. This method has a strong mathematical foundation and is simple to implement, achieving an accuracy of 45%. A comparison with the methods found in the literature designed to predict enzyme class showed that our method outperforms the existing methods.

Building multiple sequence alignments with a flavor of HSSP alignments

Homology-derived secondary structure of proteins (HSSP) is a well-known database of multiple sequence alignments (MSAs) which merges information of protein sequences and their three-dimensional structures. It is available for all proteins whose structure is deposited in the PDB. It is also used by STING and (Java)Protein Dossier to calculate and present relative entropy as a measure of the degree of conservation for each residue of proteins whose structure has been solved and deposited in the PDB. However, if the STING and (Java)Protein Dossier are to provide support for analysis of protein structures modeled in computers or being experimentally solved but not yet deposited in the PDB, then we need a new method for building alignments having a flavor of HSSP alignments (myMSAr). The present study describes a new method and its corresponding databank (SH2QS--database of sequences homologue to the query [structure-having] sequence). Our main interest in making myMSAr was to measure the degree of residue conservation for a given query sequence, regardless of whether it has a corresponding structure deposited in the PDB. In this study, we compare the measurement of residue conservation provided by corresponding alignments produced by HSSP and SH2QS. As a case study, we also present two biologically relevant examples, the first one highlighting the equivalence of analysis of the degree of residue conservation by using HSSP or SH2QS alignments, and the second one presenting the degree of residue conservation for a structure modeled in a computer, which , as a consequence, does not have an alignment reported by HSSP.

PDB-Metrics: a web tool for exploring the PDB contents

PDB-Metrics (http://sms.cbi.cnptia.embrapa.br/SMS/pdb_metrics/index.html) is a component of the Diamond STING suite of programs for the analysis of protein sequence, structure and function. It summarizes the characteristics of the collection of protein structure descriptions deposited in the Protein Data Bank (PDB) and provides a Web interface to search and browse the PDB, using a variety of alternative criteria. PDB-Metrics is a powerful tool for bioinformaticians to examine the data span in the PDB from several perspectives. Although other Web sites offer some similar resources to explore the PDB contents, PDB-Metrics is among those with the most complete set of such facilities, integrated into a single Web site. This program has been developed using SQLite, a C library that provides all the query facilities of a database management system

The Star STING server: a multiplatform environment for protein structure analysis

Star STING is the latest version of the STING suite of programs and corresponding database. We report on five important aspects of this package that have acquired some new characteristics, designed to add key advantages to the whole suite: 1) availability for most popular platforms and browsers, 2) introduction of the STING_DB quality assessment, 3) improvement in algorithms for calculation of three STING parameters, 4) introduction of five new STING modules, and 5) expansion of the existing modules. Star STING is freely accessible at: http://sms.cbi.cnptia.embrapa.br/SMS/, http://trantor.bioc.columbia.edu/SMS, http://www.es.embnet.org/SMS/, http://gibk26.bse.kyutech.ac.jp/SMS/ and http://www.ar.embnet.org/SMS.

Seroprevalencia de Helicobacter pylori en hemodializados

AIM: (a) to establish Hp infection seroprevalence among patients under chronic dialysis and to compare it with that of general population, (b) to assess the relationship between Hp prevalence and parameters associated with a higher morbidity, such as serum albumin levels, dialysis adequacy, and quality of life in this population. METHODS: Ninety-three consecutive patients of both genders (58 M/35 F), X 57.5 +/- 17.2 years old, with end-stage chronic renal failure on maintenance dialysis were included. All of them received dialysis at Hospital Italiano, Buenos Aires. The presence of anti-Hp antibodies was established and its prevalence was compared with a control group. In all cases, serum albumin levels and time in dialysis were determined. In addition, dialysis adequacy was established by KT/V and quality of life was measured with Karnofsky's index. Patients and controls were matched according variables associated with Hp infection in our country, i.e., age, socioeconomic and education levels. RESULTS: Ninety-three dialysis patients were matched with 93 controls. According to the quick serological test, 44 out of 93 (47.3%) dialyzed patients and 55 out of 93 (53.6%) controls were Hp positive (ns). Forty-one out of 76 patients (53.9%) with a serum albumin level 3.5 g/dl and 3/17 (17.6%) with a serum albumin level < 3.5 g/dl were anti-Hp positive (odds ratio, 0.06; 95% CI, 0.01-0.39; p < 0.01). Fifty-five out of 80 patients (69.2%) with Kt/V > 1.2 and 6/13 (43.8%) with Kt/V 1.2 Pounds were anti-Hp positive (odds ratio, 0.10; 95% CI, 0.02-0.65; p < 0.05). Four out of 15 patients (26.7%) with Karnofsky's index > 70, and 40/78 (51.3%) with Karnofsky's index < or = 70 were anti-Hp positive (odds ratio, 0.37; 95% CI, 0.06-2.95, p = 0.26). CONCLUSION: According to our results, dialysis patients do no represent a high-risk group of Hp infection. Those individuals with higher morbidity and mortality rates as evidenced by low serum albumin levels or by a low Kt/V have a lower Hp prevalence, perhaps due to a poor immune response o due to the use of antibiotics. Therefore, Hp infection screening en dialysis units does not differ from the guidelines developed in Maastrich for the general population.