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dc.contributor.authorFang, Yaping
dc.contributor.authorMiddaugh, C. Russell
dc.contributor.authorFang, Jianwen
dc.date.accessioned2014-03-18T19:13:54Z
dc.date.available2014-03-18T19:13:54Z
dc.date.issued2012-09-26
dc.identifier.citationFang, Y., Middaugh, C. R., & Fang, J. (2012). In Silico Classification of Proteins from Acidic and Neutral Cytoplasms. PLoS ONE, 7(9). http://dx.doi.org/10.1371/journal.pone.0045585
dc.identifier.urihttp://hdl.handle.net/1808/13235
dc.description.abstractProtein acidostability is a common problem in biopharmaceutical and other industries. However, it remains a great challenge to engineer proteins for enhanced acidostability because our knowledge of protein acidostabilization is still very limited. In this paper, we present a comparative study of proteins from bacteria with acidic (AP) and neutral cytoplasms (NP) using an integrated statistical and machine learning approach. We construct a set of 393 non-redundant AP-NP ortholog pairs and calculate a total of 889 sequence based features for these proteins. The pairwise alignments of these ortholog pairs are used to build a residue substitution propensity matrix between APs and NPs. We use Gini importance provided by the Random Forest algorithm to rank the relative importance of these features. A scoring function using the 10 most significant features is developed and optimized using a hill climbing algorithm. The accuracy of the score function is 86.01% in predicting AP-NP ortholog pairs and is 76.65% in predicting non-ortholog AP-NP pairs, suggesting that there are significant differences between APs and NPs which can be used to predict relative acidostability of proteins. The overall trends uncovered in the study can be used as general guidelines for designing acidostable proteins. To best of our knowledge, this work represents the first systematic comparative study of the acidostable proteins and their non-acidostable orthologs.
dc.publisherPublic Library of Science
dc.rights©Fang et al. 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 credited.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAmino acid analysis
dc.subjectAmino acid substitutuion
dc.subjectBlast algorithm
dc.subjectCytoplasm
dc.subjectProtein sequencing
dc.subjectProtein structure
dc.subjectSequence alignment
dc.subjectSequence similarity searching
dc.titleIn Silico Classification of Proteins from Acidic and Neutral Cytoplasms
dc.typeArticle
kusw.kuauthorMiddaugh, C. Russell
kusw.kuauthorFang, Jainwen
kusw.kudepartmentDepartment of Pharmaceutical Chemistry
kusw.oastatusfullparticipation
dc.identifier.doi10.1371/journal.pone.0045585
kusw.oaversionScholarly/refereed, publisher version
kusw.oapolicyThis item meets KU Open Access policy criteria.
dc.rights.accessrightsopenAccess


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©Fang et al. 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 credited.
Except where otherwise noted, this item's license is described as: ©Fang et al. 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 credited.