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A comparative study of short linear motif compositions of the influenza A virus ribonucleoproteins.

ABSTRACT: Protein-protein interactions through short linear motifs (SLiMs) are an emerging concept that is different from interactions between globular domains. The SLiMs encode a functional interaction interface in a short (three to ten residues) poorly conserved sequence. This characteristic makes them much more likely to arise/disappear spontaneously via mutations, and they may be more evolutionarily labile than globular domains. The diversity of SLiM composition may provide functional diversity for a viral protein from different viral strains. This study is designed to determine the different SLiM compositions of ribonucleoproteins (RNPs) from influenza A viruses (IAVs) from different hosts and with different levels of virulence. The 96 consensus sequences (regular expressions) of SLiMs from the ELM server were used to conduct a comprehensive analysis of the 52,513 IAV RNP sequences. The SLiM compositions of RNPs from IAVs from different hosts and with different levels of virulence were compared. The SLiM compositions of 845 RNPs from highly virulent/pandemic IAVs were also analyzed. In total, 292 highly conserved SLiMs were found in RNPs regardless of the IAV host range. These SLiMs may be basic motifs that are essential for the normal functions of RNPs. Moreover, several SLiMs that are rare in seasonal IAV RNPs but are present in RNPs from highly virulent/pandemic IAVs were identified.The SLiMs identified in this study provide a useful resource for experimental virologists to study the interactions between IAV RNPs and host intracellular proteins. Moreover, the SLiM compositions of IAV RNPs also provide insights into signal transduction pathways and protein interaction networks with which IAV RNPs might be involved. Information about SLiMs might be useful for the development of anti-IAV drugs.

SUBMITTER: Yang CW

PROVIDER: S-EPMC3371030 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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A comparative study of short linear motif compositions of the influenza A virus ribonucleoproteins.

Yang Chu-Wen CW

PloS one 20120608 6

Protein-protein interactions through short linear motifs (SLiMs) are an emerging concept that is different from interactions between globular domains. The SLiMs encode a functional interaction interface in a short (three to ten residues) poorly conserved sequence. This characteristic makes them much more likely to arise/disappear spontaneously via mutations, and they may be more evolutionarily labile than globular domains. The diversity of SLiM composition may provide functional diversity for a ...[more]

PMID: 22715401

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Project description:BackgroundAn important class of interaction switches for biological circuits and disease pathways are short binding motifs. However, the biological experiments to find these binding motifs are often laborious and expensive. With the availability of protein interaction data, novel binding motifs can be discovered computationally: by applying standard motif extracting algorithms on protein sequence sets each interacting with either a common protein or a protein group with similar properties. The underlying assumption is that proteins with common interacting partners will share some common binding motifs. Although novel binding motifs have been discovered with such approach, it is not applicable if a protein interacts with very few other proteins or when prior knowledge of protein group is not available or erroneous. Experimental noise in input interaction data can further deteriorate the dismal performance of such approaches.ResultsWe propose a novel approach of finding correlated short sequence motifs from protein-protein interaction data to effectively circumvent the above-mentioned limitations. Correlated motifs are those motifs that consistently co-occur only in pairs of interacting protein sequences, and could possibly interact with each other directly or indirectly to mediate interactions. We adopted the (l, d)-motif model and formulate finding the correlated motifs as an (l, d)-motif pair finding problem. We present both an exact algorithm, D-MOTIF, as well as its approximation algorithm, D-STAR to solve this problem. Evaluation on extensive simulated data showed that our approach not only eliminated the need for any prior protein grouping, but is also more robust in extracting motifs from noisy interaction data. Application on two biological datasets (SH3 interaction network and TGFbeta signaling network) demonstrates that the approach can extract correlated motifs that correspond to actual interacting subsequences.ConclusionThe correlated motif approach outlined in this paper is able to find correlated linear motifs from sparse and noisy interaction data. This, in turn, will expedite the discovery of novel linear binding motifs, and facilitate the studies of biological pathways mediated by them.

Dataset Information

A comparative study of short linear motif compositions of the influenza A virus ribonucleoproteins.

Publications

A comparative study of short linear motif compositions of the influenza A virus ribonucleoproteins.

Similar Datasets

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