Dataset Information

SeqNLS: nuclear localization signal prediction based on frequent pattern mining and linear motif scoring.

ABSTRACT: Nuclear localization signals (NLSs) are stretches of residues in proteins mediating their importing into the nucleus. NLSs are known to have diverse patterns, of which only a limited number are covered by currently known NLS motifs. Here we propose a sequential pattern mining algorithm SeqNLS to effectively identify potential NLS patterns without being constrained by the limitation of current knowledge of NLSs. The extracted frequent sequential patterns are used to predict NLS candidates which are then filtered by a linear motif-scoring scheme based on predicted sequence disorder and by the relatively local conservation (IRLC) based masking. The experiment results on the newly curated Yeast and Hybrid datasets show that SeqNLS is effective in detecting potential NLSs. The performance comparison between SeqNLS with and without the linear motif scoring shows that linear motif features are highly complementary to sequence features in discerning NLSs. For the two independent datasets, our SeqNLS not only can consistently find over 50% of NLSs with prediction precision of at least 0.7, but also outperforms other state-of-the-art NLS prediction methods in terms of F1 score or prediction precision with similar or higher recall rates. The web server of the SeqNLS algorithm is available at http://mleg.cse.sc.edu/seqNLS.

SUBMITTER: Lin JR

PROVIDER: S-EPMC3812174 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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SeqNLS: nuclear localization signal prediction based on frequent pattern mining and linear motif scoring.

Lin Jhih-rong JR Hu Jianjun J

PloS one 20131029 10

Nuclear localization signals (NLSs) are stretches of residues in proteins mediating their importing into the nucleus. NLSs are known to have diverse patterns, of which only a limited number are covered by currently known NLS motifs. Here we propose a sequential pattern mining algorithm SeqNLS to effectively identify potential NLS patterns without being constrained by the limitation of current knowledge of NLSs. The extracted frequent sequential patterns are used to predict NLS candidates which a ...[more]

PMID: 24204689

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Project description:BackgroundMany studies report about detection and functional characterization of cis-regulatory motifs in untranslated regions (UTRs) of mRNAs but little is known about the nature and functional role of their distribution. To address this issue we have developed a computational approach based on the use of data mining techniques. The idea is that of mining frequent combinations of translation regulatory motifs, since their significant co-occurrences could reveal functional relationships important for the post-transcriptional control of gene expression. The experimentation has been focused on targeted mitochondrial transcripts to elucidate the role of translational control in mitochondrial biogenesis and function.ResultsThe analysis is based on a two-stepped procedure using a sequential pattern mining algorithm. The first step searches for frequent patterns (FPs) of motifs without taking into account their spatial displacement. In the second step, frequent sequential patterns (FSPs) of spaced motifs are generated by taking into account the conservation of spacers between each ordered pair of co-occurring motifs. The algorithm makes no assumption on the relation among motifs and on the number of motifs involved in a pattern. Different FSPs can be found depending on different combinations of two parameters, i.e. the threshold of the minimum percentage of sequences supporting the pattern, and the granularity of spacer discretization. Results can be retrieved at the UTRminer web site: http://utrminer.ba.itb.cnr.it/. The discovered FPs of motifs amount to 216 in the overall dataset and to 140 in the human subset. For each FP, the system provides information on the discovered FSPs, if any. A variety of search options help users in browsing the web resource. The list of sequence IDs supporting each pattern can be used for the retrieval of information from the UTRminer database.ConclusionComputational prediction of structural properties of regulatory sequences is not trivial. The presented data mining approach is able to overcome some limits observed in other competitive tools. Preliminary results on UTR sequences from nuclear transcripts targeting mitochondria are promising and lead us to be confident on the effectiveness of the approach for future developments.

Project description:Nuclear targeting of capsid proteins (VPs) is important for genome delivery and precedes assembly in the replication cycle of porcine parvovirus (PPV). Clusters of basic amino acids, corresponding to potential nuclear localization signals (NLS), were found only in the unique region of VP1 (VP1up, for VP1 unique part). Of the five identified basic regions (BR), three were important for nuclear localization of VP1up: BR1 was a classic Pat7 NLS, and the combination of BR4 and BR5 was a classic bipartite NLS. These NLS were essential for viral replication. VP2, the major capsid protein, lacked these NLS and contained no region with more than two basic amino acids in proximity. However, three regions of basic clusters were identified in the folded protein, assembled into a trimeric structure. Mutagenesis experiments showed that only one of these three regions was involved in VP2 transport to the nucleus. This structural NLS, termed the nuclear localization motif (NLM), is located inside the assembled capsid and thus can be used to transport trimers to the nucleus in late steps of infection but not for virions in initial infection steps. The two NLS of VP1up are located in the N-terminal part of the protein, externalized from the capsid during endosomal transit, exposing them for nuclear targeting during early steps of infection. Globally, the determinants of nuclear transport of structural proteins of PPV were different from those of closely related parvoviruses. Importance: Most DNA viruses use the nucleus for their replication cycle. Thus, structural proteins need to be targeted to this cellular compartment at two distinct steps of the infection: in early steps to deliver viral genomes to the nucleus and in late steps to assemble new viruses. Nuclear targeting of proteins depends on the recognition of a stretch of basic amino acids by cellular transport proteins. This study reports the identification of two classic nuclear localization signals in the minor capsid protein (VP1) of porcine parvovirus. The major protein (VP2) nuclear localization was shown to depend on a complex structural motif. This motif can be used as a strategy by the virus to avoid transport of incorrectly folded proteins and to selectively import assembled trimers into the nucleus. Structural nuclear localization motifs can also be important for nuclear proteins without a classic basic amino acid stretch, including multimeric cellular proteins.

Project description:Histone deacetylase 5 (HDAC5), a class IIa deacetylase, is a prominent regulator of cellular and epigenetic processes that underlie the progression of human disease, ranging from cardiac hypertrophy to cancer. Although it is established that phosphorylation mediates 14-3-3 protein binding and provides the essential link between HDAC5 nucleo-cytoplasmic shuttling and transcriptional repression, thus far only four phospho-acceptor sites have been functionally characterized. Here, using a combinatorial proteomics approach and phosphomutant screening, we present the first evidence that HDAC5 has at least 17 in vivo phosphorylation sites within functional domains, including Ser278 and Ser279 within the nuclear localization signal (NLS), Ser1108 within the nuclear export signal, and Ser755 in deacetylase domain. Global and targeted MS/MS analyses of NLS peptides demonstrated the presence of single (Ser278 and Ser279) and double (Ser278/Ser279) phosphorylations. The double S278/279A mutation showed reduced association with HDAC3, slightly decreased deacetylation activity, and significantly increased cytoplasmic localization compared with wild type HDAC5, whereas the S278A and S1108A phosphomutants were not altered. Live cell imaging revealed a deficiency in nuclear import of S278/279A HDAC5. Phosphomutant stable cell lines confirmed the cellular redistribution of NLS mutants and revealed a more pronounced cytoplasmic localization for the single S279A mutant. Proteomic analysis of immunoisolated S278/279A, S279A, and S259/498A mutants linked altered cellular localization to changes in protein interactions. S278/279A and S279A HDAC5 showed reduced association with the NCoR-HDAC3 nuclear corepressor complex as well as protein kinase D enzymes, which were potentiated in the S259/498A mutant. These results provide the first link between phosphorylation outside the known 14-3-3 sites and downstream changes in protein interactions. Together these studies identify Ser279 as a critical phosphorylation within the NLS involved in the nuclear import of HDAC5, providing a regulatory point in nucleo-cytoplasmic shuttling that may be conserved in other class IIa HDACs-HDAC4 and HDAC9.

Dataset Information

SeqNLS: nuclear localization signal prediction based on frequent pattern mining and linear motif scoring.

Publications

SeqNLS: nuclear localization signal prediction based on frequent pattern mining and linear motif scoring.

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