Dataset Information

In silico prediction and in vitro characterization of multifunctional human RNase3.

ABSTRACT: Human ribonucleases A (hRNaseA) superfamily consists of thirteen members with high-structure similarities but exhibits divergent physiological functions other than RNase activity. Evolution of hRNaseA superfamily has gained novel functions which may be preserved in a unique region or domain to account for additional molecular interactions. hRNase3 has multiple functions including ribonucleolytic, heparan sulfate (HS) binding, cellular binding, endocytic, lipid destabilization, cytotoxic, and antimicrobial activities. In this study, three putative multifunctional regions, (34)RWRCK(38) (HBR1), (75)RSRFR(79) (HBR2), and (101)RPGRR(105) (HBR3), of hRNase3 have been identified employing in silico sequence analysis and validated employing in vitro activity assays. A heparin binding peptide containing HBR1 is characterized to act as a key element associated with HS binding, cellular binding, and lipid binding activities. In this study, we provide novel insights to identify functional regions of hRNase3 that may have implications for all hRNaseA superfamily members.

SUBMITTER: Lien PC

PROVIDER: S-EPMC3581242 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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In silico prediction and in vitro characterization of multifunctional human RNase3.

Lien Pei-Chun PC Kuo Ping-Hsueh PH Chen Chien-Jung CJ Chang Hsiu-Hui HH Fang Shun-lung SL Wu Wei-Shuo WS Lai Yiu-Kay YK Pai Tun-Wen TW Chang Margaret Dah-Tsyr MD

BioMed research international 20130117

Human ribonucleases A (hRNaseA) superfamily consists of thirteen members with high-structure similarities but exhibits divergent physiological functions other than RNase activity. Evolution of hRNaseA superfamily has gained novel functions which may be preserved in a unique region or domain to account for additional molecular interactions. hRNase3 has multiple functions including ribonucleolytic, heparan sulfate (HS) binding, cellular binding, endocytic, lipid destabilization, cytotoxic, and ant ...[more]

PMID: 23484086

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Project description:BACKGROUND: Although the majority of bacteria are innocuous or even beneficial for their host, others are highly infectious pathogens that can cause widespread and deadly diseases. When investigating the relationships between bacteria and other living organisms, it is therefore essential to be able to separate pathogenic organisms from non-pathogenic ones. Using traditional experimental methods for this purpose can be very costly and time-consuming, and also uncertain since animal models are not always good predictors for pathogenicity in humans. Bioinformatics-based methods are therefore strongly needed to mine the fast growing number of genome sequences and assess in a rapid and reliable way the pathogenicity of novel bacteria. METHODOLOGY/PRINCIPAL FINDINGS: We describe a new in silico method for the prediction of bacterial pathogenicity, based on the identification in microbial genomes of features that appear to correlate with virulence. The method does not rely on identifying genes known to be involved in pathogenicity (for instance virulence factors), but rather it inherently builds families of proteins that, irrespective of their function, are consistently present in only one of the two kinds of organisms, pathogens or non-pathogens. Whether a new bacterium carries proteins contained in these families determines its prediction as pathogenic or non-pathogenic. The application of the method on a set of known genomes correctly classified the virulence potential of 86% of the organisms tested. An additional validation on an independent test-set assigned correctly 22 out of 24 bacteria. CONCLUSIONS: The proposed approach was demonstrated to go beyond the species bias imposed by evolutionary relatedness, and performs better than predictors based solely on taxonomy or sequence similarity. A set of protein families that differentiate pathogenic and non-pathogenic strains were identified, including families of yet uncharacterized proteins that are suggested to be involved in bacterial pathogenicity.

Dataset Information

In silico prediction and in vitro characterization of multifunctional human RNase3.

Publications

In silico prediction and in vitro characterization of multifunctional human RNase3.

Similar Datasets

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