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dc.contributor.authorFang, Yaping
dc.contributor.authorGao, Shan
dc.contributor.authorTai, David
dc.contributor.authorMiddaugh, C. Russell
dc.contributor.authorFang, Jianwen
dc.date.accessioned2014-01-16T22:51:54Z
dc.date.available2014-01-16T22:51:54Z
dc.date.issued2013-10-28
dc.identifier.citationFang, Yaping, Shan Gao, David Tai, C Russell Middaugh, and Jianwen Fang. 2013. “Identification of Properties Important to Protein Aggregation Using Feature Selection.” BMC Bioinformatics 14 (1) (October): 314. http://dx.doi.org/10.1186/1471-2105-14-314.
dc.identifier.urihttp://hdl.handle.net/1808/12758
dc.description.abstractBackground: 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/ webcite). 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.
dc.language.isoen_US
dc.publisherBioMed Central
dc.subjectAggregation
dc.subjectAmyloid
dc.subjectPeptide
dc.subjectPrediction
dc.subjectFeature selection
dc.subjectMachine learning
dc.titleIdentification of Properties Important to Protein Aggregation Using Feature Selection
dc.typeArticle
kusw.kuauthorFang, Yaping
kusw.kuauthorGao, Shan
kusw.kuauthorTai, David
kusw.kuauthorMiddaugh, C. Russell
kusw.kuauthorFang, Jianwen
kusw.kudepartmentApplied Bioinformatics Laboratory
kusw.kudepartmentPharmaceutical Chemistry
kusw.kudepartmentElectrical Engineering & Computer Science
kusw.oastatusfullparticipation
dc.identifier.doi10.1186/1471-2105-14-314
dc.identifier.orcidhttps://orcid.org/0000-0002-8919-1338
kusw.oapolicyThis item meets KU Open Access policy criteria.
dc.rights.accessrightsopenAccess


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