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Structural analysis of hypothetical proteins from Helicobacter pylori: an approach to estimate functions of unknown or hypothetical proteins.

ABSTRACT: Helicobacter pylori (H. pylori) have a unique ability to survive in extreme acidic environments and to colonize the gastric mucosa. It can cause diverse gastric diseases such as peptic ulcers, chronic gastritis, mucosa-associated lymphoid tissue (MALT) lymphoma, gastric cancer, etc. Based on genomic research of H. pylori, over 1600 genes have been functionally identified so far. However, H. pylori possess some genes that are uncharacterized since: (i) the gene sequences are quite new; (ii) the function of genes have not been characterized in any other bacterial systems; and (iii) sometimes, the protein that is classified into a known protein based on the sequence homology shows some functional ambiguity, which raises questions about the function of the protein produced in H. pylori. Thus, there are still a lot of genes to be biologically or biochemically characterized to understand the whole picture of gene functions in the bacteria. In this regard, knowledge on the 3D structure of a protein, especially unknown or hypothetical protein, is frequently useful to elucidate the structure-function relationship of the uncharacterized gene product. That is, a structural comparison with known proteins provides valuable information to help predict the cellular functions of hypothetical proteins. Here, we show the 3D structures of some hypothetical proteins determined by NMR spectroscopy and X-ray crystallography as a part of the structural genomics of H. pylori. In addition, we show some successful approaches of elucidating the function of unknown proteins based on their structural information.

SUBMITTER: Park SJ

PROVIDER: S-EPMC3397514 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Structural analysis of hypothetical proteins from Helicobacter pylori: an approach to estimate functions of unknown or hypothetical proteins.

Park Sung Jean SJ Son Woo Sung WS Lee Bong-Jin BJ

International journal of molecular sciences 20120608 6

Helicobacter pylori (H. pylori) have a unique ability to survive in extreme acidic environments and to colonize the gastric mucosa. It can cause diverse gastric diseases such as peptic ulcers, chronic gastritis, mucosa-associated lymphoid tissue (MALT) lymphoma, gastric cancer, etc. Based on genomic research of H. pylori, over 1600 genes have been functionally identified so far. However, H. pylori possess some genes that are uncharacterized since: (i) the gene sequences are quite new; (ii) the f ...[more]

PMID: 22837682

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Project description:BackgroundAlthough previous epidemiological studies have examined the potential risk factors that increase the likelihood of acquiring Helicobacter pylori infections, most of these analyses have utilized conventional statistical models, including logistic regression, and have not benefited from advanced machine learning techniques.ObjectiveWe examined H. pylori infection risk factors among school children using machine learning algorithms to identify important risk factors as well as to determine whether machine learning can be used to predict H. pylori infection status.MethodsWe applied feature selection and classification algorithms to data from a school-based cross-sectional survey in Ethiopia. The data set included 954 school children with 27 sociodemographic and lifestyle variables. We conducted five runs of tenfold cross-validation on the data. We combined the results of these runs for each combination of feature selection (e.g., Information Gain) and classification (e.g., Support Vector Machines) algorithms.ResultsThe XGBoost classifier had the highest accuracy in predicting H. pylori infection status with an accuracy of 77%-a 13% improvement from the baseline accuracy of guessing the most frequent class (64% of the samples were H. Pylori negative.) K-Nearest Neighbors showed the worst performance across all classifiers. A similar performance was observed using the F1-score and area under the receiver operating curve (AUROC) classifier evaluation metrics. Among all features, place of residence (with urban residence increasing risk) was the most common risk factor for H. pylori infection, regardless of the feature selection method choice. Additionally, our machine learning algorithms identified other important risk factors for H. pylori infection, such as; electricity usage in the home, toilet type, and waste disposal location. Using a 75% cutoff for robustness, machine learning identified five of the eight significant features found by traditional multivariate logistic regression. However, when a lower robustness threshold is used, machine learning approaches identified more H. pylori risk factors than multivariate logistic regression and suggested risk factors not detected by logistic regression.ConclusionThis study provides evidence that machine learning approaches are positioned to uncover H. pylori infection risk factors and predict H. pylori infection status. These approaches identify similar risk factors and predict infection with comparable accuracy to logistic regression, thus they could be used as an alternative method.

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Structural analysis of hypothetical proteins from Helicobacter pylori: an approach to estimate functions of unknown or hypothetical proteins.

Publications

Structural analysis of hypothetical proteins from Helicobacter pylori: an approach to estimate functions of unknown or hypothetical proteins.

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