Dataset Information

PREDICTED STRUCTURE AND BINDING MOTIFS OF COLLAGEN ?1(XI).

ABSTRACT: The amino propeptide of collagen ?1(XI) (NPP) has been shown to bind glycosaminoglycans and to form a dimer. While these are independent biochemical events, it is likely that dimerization facilitates the interaction with glycosaminoglycans or alternatively, that glycosaminoglycan interaction facilitates the formation of an NPP:NPP dimer. The computer program MODELLER was used to generate a homology model of the collagen ?1(XI) NPP monomer using the crystal structure of the closely related noncollagenous-4 (NC4) domain of collagen ?1(IX) (PDB:2UUR) as the template. Additionally, a dimer model of collagen ?1(XI) NPP domain was created based upon the thrombospondin dimer template (PDB:1Z78). The structure of the dimer created in MODELLER was validated by comparison to a dimer model generated by docking two monomers of PDB:2UUR using ClusPro. Calculations of relative binding energy for the interaction between each collagen ?1(XI) NPP model and glycosaminoglycans as ligands was performed using AutoDock4. Computational results support a higher affinity between heparan sulfate and a dimer compared to a monomer. These findings are supported by affinity chromatography experiments in which distinct monomer and dimer peaks were observed. Sequential point mutation studies of the putative binding site (147-KKKITK-152) indicated the importance of the basic lysine residue for binding to heparan sulfate. Two orders of magnitude change in binding affinity was predicted when comparing wild type to the mutation K152A. Experimental data supports the predicted change in affinity.

SUBMITTER: McDougal OM

PROVIDER: S-EPMC4193664 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature

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PREDICTED STRUCTURE AND BINDING MOTIFS OF COLLAGEN α1(XI).

McDougal Owen M OM Warner Lisa R LR Mallory Chris C Oxford Julia Thom JT

GSTF international journal on bioinformatics & biotechnology 20111201 1

The amino propeptide of collagen α1(XI) (NPP) has been shown to bind glycosaminoglycans and to form a dimer. While these are independent biochemical events, it is likely that dimerization facilitates the interaction with glycosaminoglycans or alternatively, that glycosaminoglycan interaction facilitates the formation of an NPP:NPP dimer. The computer program MODELLER was used to generate a homology model of the collagen α1(XI) NPP monomer using the crystal structure of the closely related noncol ...[more]

PMID: 25309886

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Project description:BACKGROUND: RNA-binding proteins interact with specific RNA molecules to regulate important cellular processes. It is therefore necessary to identify the RNA interaction partners in order to understand the precise functions of such proteins. Protein-RNA interactions are typically characterized using in vivo and in vitro experiments but these may not detect all binding partners. Therefore, computational methods that capture the protein-dependent nature of such binding interactions could help to predict potential binding partners in silico. RESULTS: We have developed three methods to predict whether an RNA can interact with a particular RNA-binding protein using support vector machines and different features based on the sequence (the Oli method), the motif score (the OliMo method) and the secondary structure (the OliMoSS method). We applied these approaches to different experimentally-derived datasets and compared the predictions with RNAcontext and RPISeq. Oli outperformed OliMoSS and RPISeq, confirming our protein-specific predictions and suggesting that tetranucleotide frequencies are appropriate discriminative features. Oli and RNAcontext were the most competitive methods in terms of the area under curve. A precision-recall curve analysis achieved higher precision values for Oli. On a second experimental dataset including real negative binding information, Oli outperformed RNAcontext with a precision of 0.73 vs. 0.59. CONCLUSIONS: Our experiments showed that features based on primary sequence information are sufficiently discriminating to predict specific RNA-protein interactions. Sequence motifs and secondary structure information were not necessary to improve these predictions. Finally we confirmed that protein-specific experimental data concerning RNA-protein interactions are valuable sources of information that can be used for the efficient training of models for in silico predictions. The scripts are available upon request to the corresponding author.

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Dataset Information

PREDICTED STRUCTURE AND BINDING MOTIFS OF COLLAGEN ?1(XI).

Publications

PREDICTED STRUCTURE AND BINDING MOTIFS OF COLLAGEN α1(XI).

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