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Protein attributes contribute to halo-stability, bioinformatics approach
Halophile proteins can tolerate high salt concentrations. Understanding halophilicity features is the first step toward engineering halostable crops. To this end, we examined protein features contributing to the halo-toleration of halophilic organisms. We compared more than 850 features for halophil...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117752/ https://www.ncbi.nlm.nih.gov/pubmed/21592393 http://dx.doi.org/10.1186/1746-1448-7-1 |
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author | Ebrahimie, Esmaeil Ebrahimi, Mansour Sarvestani, Narjes Rahpayma Ebrahimi, Mahdi |
author_facet | Ebrahimie, Esmaeil Ebrahimi, Mansour Sarvestani, Narjes Rahpayma Ebrahimi, Mahdi |
author_sort | Ebrahimie, Esmaeil |
collection | PubMed |
description | Halophile proteins can tolerate high salt concentrations. Understanding halophilicity features is the first step toward engineering halostable crops. To this end, we examined protein features contributing to the halo-toleration of halophilic organisms. We compared more than 850 features for halophilic and non-halophilic proteins with various screening, clustering, decision tree, and generalized rule induction models to search for patterns that code for halo-toleration. Up to 251 protein attributes selected by various attribute weighting algorithms as important features contribute to halo-stability; from them 14 attributes selected by 90% of models and the count of hydrogen gained the highest value (1.0) in 70% of attribute weighting models, showing the importance of this attribute in feature selection modeling. The other attributes mostly were the frequencies of di-peptides. No changes were found in the numbers of groups when K-Means and TwoStep clustering modeling were performed on datasets with or without feature selection filtering. Although the depths of induced trees were not high, the accuracies of trees were higher than 94% and the frequency of hydrophobic residues pointed as the most important feature to build trees. The performance evaluation of decision tree models had the same values and the best correctness percentage recorded with the Exhaustive CHAID and CHAID models. We did not find any significant difference in the percent of correctness, performance evaluation, and mean correctness of various decision tree models with or without feature selection. For the first time, we analyzed the performance of different screening, clustering, and decision tree algorithms for discriminating halophilic and non-halophilic proteins and the results showed that amino acid composition can be used to discriminate between halo-tolerant and halo-sensitive proteins. |
format | Online Article Text |
id | pubmed-3117752 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-31177522011-06-18 Protein attributes contribute to halo-stability, bioinformatics approach Ebrahimie, Esmaeil Ebrahimi, Mansour Sarvestani, Narjes Rahpayma Ebrahimi, Mahdi Saline Syst Research Halophile proteins can tolerate high salt concentrations. Understanding halophilicity features is the first step toward engineering halostable crops. To this end, we examined protein features contributing to the halo-toleration of halophilic organisms. We compared more than 850 features for halophilic and non-halophilic proteins with various screening, clustering, decision tree, and generalized rule induction models to search for patterns that code for halo-toleration. Up to 251 protein attributes selected by various attribute weighting algorithms as important features contribute to halo-stability; from them 14 attributes selected by 90% of models and the count of hydrogen gained the highest value (1.0) in 70% of attribute weighting models, showing the importance of this attribute in feature selection modeling. The other attributes mostly were the frequencies of di-peptides. No changes were found in the numbers of groups when K-Means and TwoStep clustering modeling were performed on datasets with or without feature selection filtering. Although the depths of induced trees were not high, the accuracies of trees were higher than 94% and the frequency of hydrophobic residues pointed as the most important feature to build trees. The performance evaluation of decision tree models had the same values and the best correctness percentage recorded with the Exhaustive CHAID and CHAID models. We did not find any significant difference in the percent of correctness, performance evaluation, and mean correctness of various decision tree models with or without feature selection. For the first time, we analyzed the performance of different screening, clustering, and decision tree algorithms for discriminating halophilic and non-halophilic proteins and the results showed that amino acid composition can be used to discriminate between halo-tolerant and halo-sensitive proteins. BioMed Central 2011-05-18 /pmc/articles/PMC3117752/ /pubmed/21592393 http://dx.doi.org/10.1186/1746-1448-7-1 Text en Copyright © 2011 Ebrahimie et al; licensee BioMed Central Ltd. https://creativecommons.org/licenses/by/2.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0 (https://creativecommons.org/licenses/by/2.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Ebrahimie, Esmaeil Ebrahimi, Mansour Sarvestani, Narjes Rahpayma Ebrahimi, Mahdi Protein attributes contribute to halo-stability, bioinformatics approach |
title | Protein attributes contribute to halo-stability, bioinformatics approach |
title_full | Protein attributes contribute to halo-stability, bioinformatics approach |
title_fullStr | Protein attributes contribute to halo-stability, bioinformatics approach |
title_full_unstemmed | Protein attributes contribute to halo-stability, bioinformatics approach |
title_short | Protein attributes contribute to halo-stability, bioinformatics approach |
title_sort | protein attributes contribute to halo-stability, bioinformatics approach |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117752/ https://www.ncbi.nlm.nih.gov/pubmed/21592393 http://dx.doi.org/10.1186/1746-1448-7-1 |
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