Dataset Information

NoD: a Nucleolar localization sequence detector for eukaryotic and viral proteins.

ABSTRACT: Nucleolar localization sequences (NoLSs) are short targeting sequences responsible for the localization of proteins to the nucleolus. Given the large number of proteins experimentally detected in the nucleolus and the central role of this subnuclear compartment in the cell, NoLSs are likely to be important regulatory elements controlling cellular traffic. Although many proteins have been reported to contain NoLSs, the systematic characterization of this group of targeting motifs has only recently been carried out.Here, we describe NoD, a web server and a command line program that predicts the presence of NoLSs in proteins. Using the web server, users can submit protein sequences through the NoD input form and are provided with a graphical output of the NoLS score as a function of protein position. While the web server is most convenient for making prediction for just a few proteins, the command line version of NoD can return predictions for complete proteomes. NoD is based on our recently described human-trained artificial neural network predictor. Through stringent independent testing of the predictor using available experimentally validated NoLS-containing eukaryotic and viral proteins, the NoD sensitivity and positive predictive value were estimated to be 71% and 79% respectively.NoD is the first tool to provide predictions of nucleolar localization sequences in diverse eukaryotes and viruses. NoD can be run interactively online at http://www.compbio.dundee.ac.uk/nod or downloaded to use locally.

SUBMITTER: Scott MS

PROVIDER: S-EPMC3166288 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature

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NoD: a Nucleolar localization sequence detector for eukaryotic and viral proteins.

Scott Michelle S MS Troshin Peter V PV Barton Geoffrey J GJ

BMC bioinformatics 20110803

<h4>Background</h4>Nucleolar localization sequences (NoLSs) are short targeting sequences responsible for the localization of proteins to the nucleolus. Given the large number of proteins experimentally detected in the nucleolus and the central role of this subnuclear compartment in the cell, NoLSs are likely to be important regulatory elements controlling cellular traffic. Although many proteins have been reported to contain NoLSs, the systematic characterization of this group of targeting moti ...[more]

PMID: 21812952

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Project description:BackgroundThe expansion of raw protein sequence databases in the post genomic era and availability of fresh annotated sequences for major localizations particularly motivated us to introduce a new improved version of our previously forged eukaryotic subcellular localizations prediction method namely "ESLpred". Since, subcellular localization of a protein offers essential clues about its functioning, hence, availability of localization predictor would definitely aid and expedite the protein deciphering studies. However, robustness of a predictor is highly dependent on the superiority of dataset and extracted protein attributes; hence, it becomes imperative to improve the performance of presently available method using latest dataset and crucial input features.ResultsHere, we describe augmentation in the prediction performance obtained for our most popular ESLpred method using new crucial features as an input to Support Vector Machine (SVM). In addition, recently available, highly non-redundant dataset encompassing three kingdoms specific protein sequence sets; 1198 fungi sequences, 2597 from animal and 491 plant sequences were also included in the present study. First, using the evolutionary information in the form of profile composition along with whole and N-terminal sequence composition as an input feature vector of 440 dimensions, overall accuracies of 72.7, 75.8 and 74.5% were achieved respectively after five-fold cross-validation. Further, enhancement in performance was observed when similarity search based results were coupled with whole and N-terminal sequence composition along with profile composition by yielding overall accuracies of 75.9, 80.8, 76.6% respectively; best accuracies reported till date on the same datasets.ConclusionThese results provide confidence about the reliability and accurate prediction of SVM modules generated in the present study using sequence and profile compositions along with similarity search based results. The presently developed modules are implemented as web server "ESLpred2" available at http://www.imtech.res.in/raghava/eslpred2/.

Project description:Combination antiretroviral therapy fails in complete suppression of HIV-1 due to drug resistance and persistent latency. Novel therapeutic intervention requires knowledge of intracellular pathways responsible for viral replication, specifically those untargeted by antiretroviral drugs. An understudied phenomenon is the nucleolar localization of Rev phosphoprotein, which completes nucleocytoplasmic transport of unspliced/partially spliced HIV mRNA through multimerization with intronic cis-acting targets-the Rev-response element (RRE). Rev contains a nucleolar localization signal (NoLS) comprising the COOH terminus of the arginine-rich motif for accumulation within nucleoli-speculated as the interaction ground for Rev with cellular proteins mediating mRNA-independent nuclear export and splicing. Functionality of Rev nucleolar access during HIV-1 production and infection was investigated in the context of deletion and single-point mutations within Rev-NoLS. Mutations induced upon Rev-NoLS are hypothesized to inactivate the HIV-1 infectious cycle. HIV-1HXB2 replication ceased with Rev mutations lacking nucleolar access due to loss or replacement of multiple arginine residues. Rev mutations missing single arginine residues remained strictly nucleolar in pattern and participated in proviral production, however, with reduced efficiency. Viral RNA packaging also decreased in efficiency after expression of nucleolar-localizing mutations. These results were observed during propagation of variant HIV-1NL4-3 containing nucleolar-localizing mutations within the viral backbone (M4, M5, and M6). Lentiviral particles produced with Rev single-point mutations were transducible at extremely low frequency. Similarly, HIV-1NL4-3 Rev-NoLS variants lost infectivity, unlike virulent WT (wild type) HIV-1NL4-3. HIV-1NL4-3 variants were capable of CD4+ host entry and reverse transcription as WT HIV-1NL4-3, but lacked ability to complete a full infectious cycle. We currently reveal that viral integration is deregulated in the presence of Rev-NoLS mutations.

Dataset Information

NoD: a Nucleolar localization sequence detector for eukaryotic and viral proteins.

Publications

NoD: a Nucleolar localization sequence detector for eukaryotic and viral proteins.

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