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Prediction and Analysis of Antibody Amyloidogenesis from Sequences

dc.citation.epageen_US
dc.citation.issue1en_US
dc.citation.volume8en_US
dc.citation.woscount3
dc.contributor.authorLiaw, Chynen_US
dc.contributor.authorTung, Chun-Weien_US
dc.contributor.authorHo, Shinn-Yingen_US
dc.contributor.department生物科技學系zh_TW
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.departmentDepartment of Biological Science and Technologyen_US
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.date.accessioned2014-12-08T15:29:13Z
dc.date.available2014-12-08T15:29:13Z
dc.date.issued2013-01-07en_US
dc.description.abstractAntibody amyloidogenesis is the aggregation of soluble proteins into amyloid fibrils that is one of major causes of the failures of humanized antibodies. The prediction and prevention of antibody amyloidogenesis are helpful for restoring and enhancing therapeutic effects. Due to a large number of possible germlines, the existing method is not practical to predict sequences of novel germlines, which establishes individual models for each known germline. This study proposes a first automatic and across-germline prediction method (named AbAmyloid) capable of predicting antibody amyloidogenesis from sequences. Since the amyloidogenesis is determined by a whole sequence of an antibody rather than germline-dependent properties such as mutated residues, this study assess three types of germline-independent sequence features (amino acid composition, dipeptide composition and physicochemical properties). AbAmyloid using a Random Forests classifier with dipeptide composition performs well on a data set of 12 germlines. The within-and across-germline prediction accuracies are 83.10% and 83.33% using Jackknife tests, respectively, and the novel-germline prediction accuracy using a leave-one-germline-out test is 72.22%. A thorough analysis of sequence features is conducted to identify informative properties for further providing insights to antibody amyloidogenesis. Some identified informative physicochemical properties are amphiphilicity, hydrophobicity, reverse turn, helical structure, isoelectric point, net charge, mutability, coil, turn, linker, nuclear protein, etc. Additionally, the numbers of ubiquitylation sites in amyloidogenic and nonamyloidogenic antibodies are found to be significantly different. It reveals that antibodies less likely to be ubiquitylated tend to be amyloidogenic. The method AbAmyloid capable of automatically predicting antibody amyloidogenesis of novel germlines is implemented as a publicly available web server at http://iclab.life.nctu.edu.tw/abamyloid.en_US
dc.identifier.doi10.1371/journal.pone.0053235en_US
dc.identifier.issn1932-6203en_US
dc.identifier.journalPLOS ONEen_US
dc.identifier.urihttp://dx.doi.org/10.1371/journal.pone.0053235en_US
dc.identifier.urihttps://ir.lib.nycu.edu.tw/handle/11536/21055
dc.identifier.wosnumberWOS:000313429100043
dc.language.isoen_USen_US
dc.titlePrediction and Analysis of Antibody Amyloidogenesis from Sequencesen_US
dc.typeArticleen_US

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