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Identification of properties important to protein aggregation using feature selection
BACKGROUND: Protein aggregation is a significant problem in the biopharmaceutical industry (protein drug stability) and is associated medically with over 40 human diseases. Although a number of computational models have been developed for predicting aggregation propensity and identifying aggregation...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3819749/ https://www.ncbi.nlm.nih.gov/pubmed/24165390 http://dx.doi.org/10.1186/1471-2105-14-314 |
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author | Fang, Yaping Gao, Shan Tai, David Middaugh, C Russell Fang, Jianwen |
author_facet | Fang, Yaping Gao, Shan Tai, David Middaugh, C Russell Fang, Jianwen |
author_sort | Fang, Yaping |
collection | PubMed |
description | BACKGROUND: Protein aggregation is a significant problem in the biopharmaceutical industry (protein drug stability) and is associated medically with over 40 human diseases. Although a number of computational models have been developed for predicting aggregation propensity and identifying aggregation-prone regions in proteins, little systematic research has been done to determine physicochemical properties relevant to aggregation and their relative importance to this important process. Such studies may result in not only accurately predicting peptide aggregation propensities and identifying aggregation prone regions in proteins, but also aid in discovering additional underlying mechanisms governing this process. RESULTS: We use two feature selection algorithms to identify 16 features, out of a total of 560 physicochemical properties, presumably important to protein aggregation. Two predictors (ProA-SVM and ProA-RF) using selected features are built for predicting peptide aggregation propensity and identifying aggregation prone regions in proteins. Both methods are compared favourably to other state-of-the-art algorithms in cross validation. The identified important properties are fairly consistent with previous studies and bring some new insights into protein and peptide aggregation. One interesting new finding is that aggregation prone peptide sequences have similar properties to signal peptide and signal anchor sequences. CONCLUSIONS: Both predictors are implemented in a freely available web application (http://www.abl.ku.edu/ProA/). We suggest that the quaternary structure of protein aggregates, especially soluble oligomers, may allow the formation of new molecular recognition signals that guide aggregate targeting to specific cellular sites. |
format | Online Article Text |
id | pubmed-3819749 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-38197492013-11-11 Identification of properties important to protein aggregation using feature selection Fang, Yaping Gao, Shan Tai, David Middaugh, C Russell Fang, Jianwen BMC Bioinformatics Research Article BACKGROUND: Protein aggregation is a significant problem in the biopharmaceutical industry (protein drug stability) and is associated medically with over 40 human diseases. Although a number of computational models have been developed for predicting aggregation propensity and identifying aggregation-prone regions in proteins, little systematic research has been done to determine physicochemical properties relevant to aggregation and their relative importance to this important process. Such studies may result in not only accurately predicting peptide aggregation propensities and identifying aggregation prone regions in proteins, but also aid in discovering additional underlying mechanisms governing this process. RESULTS: We use two feature selection algorithms to identify 16 features, out of a total of 560 physicochemical properties, presumably important to protein aggregation. Two predictors (ProA-SVM and ProA-RF) using selected features are built for predicting peptide aggregation propensity and identifying aggregation prone regions in proteins. Both methods are compared favourably to other state-of-the-art algorithms in cross validation. The identified important properties are fairly consistent with previous studies and bring some new insights into protein and peptide aggregation. One interesting new finding is that aggregation prone peptide sequences have similar properties to signal peptide and signal anchor sequences. CONCLUSIONS: Both predictors are implemented in a freely available web application (http://www.abl.ku.edu/ProA/). We suggest that the quaternary structure of protein aggregates, especially soluble oligomers, may allow the formation of new molecular recognition signals that guide aggregate targeting to specific cellular sites. BioMed Central 2013-10-28 /pmc/articles/PMC3819749/ /pubmed/24165390 http://dx.doi.org/10.1186/1471-2105-14-314 Text en Copyright © 2013 Fang et al.; licensee BioMed Central Ltd. http://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), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Fang, Yaping Gao, Shan Tai, David Middaugh, C Russell Fang, Jianwen Identification of properties important to protein aggregation using feature selection |
title | Identification of properties important to protein aggregation using feature selection |
title_full | Identification of properties important to protein aggregation using feature selection |
title_fullStr | Identification of properties important to protein aggregation using feature selection |
title_full_unstemmed | Identification of properties important to protein aggregation using feature selection |
title_short | Identification of properties important to protein aggregation using feature selection |
title_sort | identification of properties important to protein aggregation using feature selection |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3819749/ https://www.ncbi.nlm.nih.gov/pubmed/24165390 http://dx.doi.org/10.1186/1471-2105-14-314 |
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