Dataset Information

SOSUI-GramN: high performance prediction for sub-cellular localization of proteins in gram-negative bacteria.

ABSTRACT: A predictive software system, SOSUI-GramN, was developed for assessing the subcellular localization of proteins in Gram-negative bacteria. The system does not require the sequence homology data of any known sequences; instead, it uses only physicochemical parameters of the N- and C-terminal signal sequences, and the total sequence. The precision of the prediction system for subcellular localization to extracellular, outer membrane, periplasm, inner membrane and cytoplasmic medium was 92.3%, 89.4%, 86.4%, 97.5% and 93.5%, respectively, with corresponding recall rates of 70.3%, 87.5%, 76.0%, 97.5% and 88.4%, respectively. The overall performance for precision and recall obtained using this method was 92.9% and 86.7%, respectively. The comparison of performance of SOSUI-GramN with that of other methods showed the performance of prediction for extracellular proteins, as well as inner and outer membrane proteins, was either superior or equivalent to that obtained with other systems. SOSUI-GramN particularly improved the accuracy for predictions of extracellular proteins which is an area of weakness common to the other methods.

SUBMITTER: Imai K

PROVIDER: S-EPMC2533062 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

SOSUI-GramN: high performance prediction for sub-cellular localization of proteins in gram-negative bacteria.

Imai Kenichiro K Asakawa Naoyuki N Tsuji Toshiyuki T Akazawa Fumitsugu F Ino Ayano A Sonoyama Masashi M Mitaku Shigeki S

Bioinformation 20080714 9

A predictive software system, SOSUI-GramN, was developed for assessing the subcellular localization of proteins in Gram-negative bacteria. The system does not require the sequence homology data of any known sequences; instead, it uses only physicochemical parameters of the N- and C-terminal signal sequences, and the total sequence. The precision of the prediction system for subcellular localization to extracellular, outer membrane, periplasm, inner membrane and cytoplasmic medium was 92.3%, 89.4 ...[more]

PMID: 18795116

Similar Datasets

Project description:BACKGROUND: Genome-wide prediction of protein subcellular localization is an important type of evidence used for inferring protein function. While a variety of computational tools have been developed for this purpose, errors in the gene models and use of protein sorting signals that are not recognized by the more commonly accepted tools can diminish the accuracy of their output. RESULTS: As part of an effort to manually curate the annotations of 19 strains of Shewanella, numerous insights were gained regarding the use of computational tools and proteomics data to predict protein localization. Identification of the suite of secretion systems present in each strain at the start of the process made it possible to tailor-fit the subsequent localization prediction strategies to each strain for improved accuracy. Comparisons of the computational predictions among orthologous proteins revealed inconsistencies in the computational outputs, which could often be resolved by adjusting the gene models or ortholog group memberships. While proteomic data was useful for verifying start site predictions and post-translational proteolytic cleavage, care was needed to distinguish cellular versus sample processing-mediated cleavage events. Searches for lipoprotein signal peptides revealed that neither TatP nor LipoP are designed for identification of lipoprotein substrates of the twin arginine translocation system and that the +2 rule for lipoprotein sorting does not apply to this Genus. Analysis of the relationships between domain occurrence and protein localization prediction enabled identification of numerous location-informative domains which could then be used to refine or increase confidence in location predictions. This collective knowledge was used to develop a general strategy for predicting protein localization that could be adapted to other organisms. CONCLUSION: Improved localization prediction accuracy is not simply a matter of developing better computational algorithms. It also entails gathering key knowledge regarding the host architecture and translocation machinery and associated substrate recognition via experimentation and integration of diverse computational analyses from many proteins and, where possible, that are derived from different species within the same genus.

Project description:Organismal adaptations to environmental stimuli are governed by intracellular signaling molecules such as nucleotide second messengers. Recent studies have identified functional roles for the noncanonical 2',3'-cyclic nucleotide monophosphates (2',3'-cNMPs) in both eukaryotes and prokaryotes. In Escherichia coli, 2',3'-cNMPs are produced by RNase I-catalyzed RNA degradation, and these cyclic nucleotides modulate biofilm formation through unknown mechanisms. The present work dissects cellular processes in E. coli and Salmonella enterica serovar Typhimurium that are modulated by 2',3'-cNMPs through the development of cell-permeable 2',3'-cNMP analogs and a 2',3'-cyclic nucleotide phosphodiesterase. Utilization of these chemical and enzymatic tools, in conjunction with phenotypic and transcriptomic investigations, identified pathways regulated by 2',3'-cNMPs, including flagellar motility and biofilm formation, and by oligoribonucleotides with 3'-terminal 2',3'-cyclic phosphates, including responses to cellular stress. Furthermore, interrogation of metabolomic and organismal databases has identified 2',3'-cNMPs in numerous organisms and homologs of the E. coli metabolic proteins that are involved in key eukaryotic pathways. Thus, the present work provides key insights into the roles of these understudied facets of nucleotide metabolism and signaling in prokaryotic physiology and suggest broad roles for 2',3'-cNMPs among bacteria and eukaryotes. IMPORTANCE Bacteria adapt to environmental challenges by producing intracellular signaling molecules that control downstream pathways and alter cellular processes for survival. Nucleotide second messengers serve to transduce extracellular signals and regulate a wide array of intracellular pathways. Recently, 2',3'-cyclic nucleotide monophosphates (2',3'-cNMPs) were identified as contributing to the regulation of cellular pathways in eukaryotes and prokaryotes. In this study, we define previously unknown cell processes that are affected by fluctuating 2',3'-cNMP levels or RNA oligomers with 2',3'-cyclic phosphate termini in E. coli and Salmonella Typhimurium, providing a framework for studying novel signaling networks in prokaryotes. Furthermore, we utilize metabolomics databases to identify additional prokaryotic and eukaryotic species that generate 2',3'-cNMPs as a resource for future studies.

Dataset Information

SOSUI-GramN: high performance prediction for sub-cellular localization of proteins in gram-negative bacteria.

Publications

SOSUI-GramN: high performance prediction for sub-cellular localization of proteins in gram-negative bacteria.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets