Dataset Information

Function prediction and analysis of mycobacterium tuberculosis hypothetical proteins.

ABSTRACT: High-throughput biology technologies have yielded complete genome sequences and functional genomics data for several organisms, including crucial microbial pathogens of humans, animals and plants. However, up to 50% of genes within a genome are often labeled "unknown", "uncharacterized" or "hypothetical", limiting our understanding of virulence and pathogenicity of these organisms. Even though biological functions of proteins encoded by these genes are not known, many of them have been predicted to be involved in key processes in these organisms. In particular, for Mycobacterium tuberculosis, some of these "hypothetical" proteins, for example those belonging to the Pro-Glu or Pro-Pro-Glu (PE/PPE) family, have been suspected to play a crucial role in the intracellular lifestyle of this pathogen, and may contribute to its survival in different environments. We have generated a functional interaction network for Mycobacterium tuberculosis proteins and used this to predict functions for many of its hypothetical proteins. Here we performed functional enrichment analysis of these proteins based on their predicted biological functions to identify annotations that are statistically relevant, and analysed and compared network properties of hypothetical proteins to the known proteins. From the statistically significant annotations and network information, we have tried to derive biologically meaningful annotations related to infection and disease. This quantitative analysis provides an overview of the functional contributions of Mycobacterium tuberculosis "hypothetical" proteins to many basic cellular functions, including its adaptability in the host system and its ability to evade the host immune response.

SUBMITTER: Mazandu GK

PROVIDER: S-EPMC3397526 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Function prediction and analysis of mycobacterium tuberculosis hypothetical proteins.

Mazandu Gaston K GK Mulder Nicola J NJ

International journal of molecular sciences 20120613 6

High-throughput biology technologies have yielded complete genome sequences and functional genomics data for several organisms, including crucial microbial pathogens of humans, animals and plants. However, up to 50% of genes within a genome are often labeled "unknown", "uncharacterized" or "hypothetical", limiting our understanding of virulence and pathogenicity of these organisms. Even though biological functions of proteins encoded by these genes are not known, many of them have been predicted ...[more]

PMID: 22837694

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Project description:BACKGROUND:Pyrazinamide is an important drug against the latent stage of tuberculosis and is used in both first- and second-line treatment regimens. Pyrazinamide-susceptibility test usually takes a week to have a diagnosis to guide initial therapy, implying a delay in receiving appropriate therapy. The continued increase in multi-drug resistant tuberculosis and the prevalence of pyrazinamide resistance in several countries makes the development of assays for prompt identification of resistance necessary. The main cause of pyrazinamide resistance is the impairment of pyrazinamidase function attributed to mutations in the promoter and/or pncA coding gene. However, not all pncA mutations necessarily affect the pyrazinamidase function. OBJECTIVE:To develop a methodology to predict pyrazinamidase function from detected mutations in the pncA gene. METHODS:We measured the catalytic constant (kcat), KM, enzymatic efficiency, and enzymatic activity of 35 recombinant mutated pyrazinamidase and the wild type (Protein Data Bank ID = 3pl1). From all the 3D modeled structures, we extracted several predictors based on three categories: structural stability (estimated by normal mode analysis and molecular dynamics), physicochemical, and geometrical characteristics. We used a stepwise Akaike's information criterion forward multiple log-linear regression to model each kinetic parameter with each category of predictors. We also developed weighted models combining the three categories of predictive models for each kinetic parameter. We tested the robustness of the predictive ability of each model by 6-fold cross-validation against random models. RESULTS:The stability, physicochemical, and geometrical descriptors explained most of the variability (R2) of the kinetic parameters. Our models are best suited to predict kcat, efficiency, and activity based on the root-mean-square error of prediction of the 6-fold cross-validation. CONCLUSIONS:This study shows a quick approach to predict the pyrazinamidase function only from the pncA sequence when point mutations are present. This can be an important tool to detect pyrazinamide resistance.

Dataset Information

Function prediction and analysis of mycobacterium tuberculosis hypothetical proteins.

Publications

Function prediction and analysis of mycobacterium tuberculosis hypothetical proteins.

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