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LRR Conservation Mapping to Predict Functional Sites within Protein Leucine-Rich Repeat Domains

Computational prediction of protein functional sites can be a critical first step for analysis of large or complex proteins. Contemporary methods often require several homologous sequences and/or a known protein structure, but these resources are not available for many proteins. Leucine-rich repeats...

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Autores principales: Helft, Laura, Reddy, Vignyan, Chen, Xiyang, Koller, Teresa, Federici, Luca, Fernández-Recio, Juan, Gupta, Rishabh, Bent, Andrew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3138743/
https://www.ncbi.nlm.nih.gov/pubmed/21789174
http://dx.doi.org/10.1371/journal.pone.0021614
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author Helft, Laura
Reddy, Vignyan
Chen, Xiyang
Koller, Teresa
Federici, Luca
Fernández-Recio, Juan
Gupta, Rishabh
Bent, Andrew
author_facet Helft, Laura
Reddy, Vignyan
Chen, Xiyang
Koller, Teresa
Federici, Luca
Fernández-Recio, Juan
Gupta, Rishabh
Bent, Andrew
author_sort Helft, Laura
collection PubMed
description Computational prediction of protein functional sites can be a critical first step for analysis of large or complex proteins. Contemporary methods often require several homologous sequences and/or a known protein structure, but these resources are not available for many proteins. Leucine-rich repeats (LRRs) are ligand interaction domains found in numerous proteins across all taxonomic kingdoms, including immune system receptors in plants and animals. We devised Repeat Conservation Mapping (RCM), a computational method that predicts functional sites of LRR domains. RCM utilizes two or more homologous sequences and a generic representation of the LRR structure to identify conserved or diversified patches of amino acids on the predicted surface of the LRR. RCM was validated using solved LRR+ligand structures from multiple taxa, identifying ligand interaction sites. RCM was then used for de novo dissection of two plant microbe-associated molecular pattern (MAMP) receptors, EF-TU RECEPTOR (EFR) and FLAGELLIN-SENSING 2 (FLS2). In vivo testing of Arabidopsis thaliana EFR and FLS2 receptors mutagenized at sites identified by RCM demonstrated previously unknown functional sites. The RCM predictions for EFR, FLS2 and a third plant LRR protein, PGIP, compared favorably to predictions from ODA (optimal docking area), Consurf, and PAML (positive selection) analyses, but RCM also made valid functional site predictions not available from these other bioinformatic approaches. RCM analyses can be conducted with any LRR-containing proteins at www.plantpath.wisc.edu/RCM, and the approach should be modifiable for use with other types of repeat protein domains.
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spelling pubmed-31387432011-07-25 LRR Conservation Mapping to Predict Functional Sites within Protein Leucine-Rich Repeat Domains Helft, Laura Reddy, Vignyan Chen, Xiyang Koller, Teresa Federici, Luca Fernández-Recio, Juan Gupta, Rishabh Bent, Andrew PLoS One Research Article Computational prediction of protein functional sites can be a critical first step for analysis of large or complex proteins. Contemporary methods often require several homologous sequences and/or a known protein structure, but these resources are not available for many proteins. Leucine-rich repeats (LRRs) are ligand interaction domains found in numerous proteins across all taxonomic kingdoms, including immune system receptors in plants and animals. We devised Repeat Conservation Mapping (RCM), a computational method that predicts functional sites of LRR domains. RCM utilizes two or more homologous sequences and a generic representation of the LRR structure to identify conserved or diversified patches of amino acids on the predicted surface of the LRR. RCM was validated using solved LRR+ligand structures from multiple taxa, identifying ligand interaction sites. RCM was then used for de novo dissection of two plant microbe-associated molecular pattern (MAMP) receptors, EF-TU RECEPTOR (EFR) and FLAGELLIN-SENSING 2 (FLS2). In vivo testing of Arabidopsis thaliana EFR and FLS2 receptors mutagenized at sites identified by RCM demonstrated previously unknown functional sites. The RCM predictions for EFR, FLS2 and a third plant LRR protein, PGIP, compared favorably to predictions from ODA (optimal docking area), Consurf, and PAML (positive selection) analyses, but RCM also made valid functional site predictions not available from these other bioinformatic approaches. RCM analyses can be conducted with any LRR-containing proteins at www.plantpath.wisc.edu/RCM, and the approach should be modifiable for use with other types of repeat protein domains. Public Library of Science 2011-07-18 /pmc/articles/PMC3138743/ /pubmed/21789174 http://dx.doi.org/10.1371/journal.pone.0021614 Text en Helft et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Helft, Laura
Reddy, Vignyan
Chen, Xiyang
Koller, Teresa
Federici, Luca
Fernández-Recio, Juan
Gupta, Rishabh
Bent, Andrew
LRR Conservation Mapping to Predict Functional Sites within Protein Leucine-Rich Repeat Domains
title LRR Conservation Mapping to Predict Functional Sites within Protein Leucine-Rich Repeat Domains
title_full LRR Conservation Mapping to Predict Functional Sites within Protein Leucine-Rich Repeat Domains
title_fullStr LRR Conservation Mapping to Predict Functional Sites within Protein Leucine-Rich Repeat Domains
title_full_unstemmed LRR Conservation Mapping to Predict Functional Sites within Protein Leucine-Rich Repeat Domains
title_short LRR Conservation Mapping to Predict Functional Sites within Protein Leucine-Rich Repeat Domains
title_sort lrr conservation mapping to predict functional sites within protein leucine-rich repeat domains
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3138743/
https://www.ncbi.nlm.nih.gov/pubmed/21789174
http://dx.doi.org/10.1371/journal.pone.0021614
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