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Prediction and Analysis of Antibody Amyloidogenesis from Sequences

Antibody 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 po...

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Autores principales: Liaw, Chyn, Tung, Chun-Wei, Ho, Shinn-Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3538782/
https://www.ncbi.nlm.nih.gov/pubmed/23308169
http://dx.doi.org/10.1371/journal.pone.0053235
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author Liaw, Chyn
Tung, Chun-Wei
Ho, Shinn-Ying
author_facet Liaw, Chyn
Tung, Chun-Wei
Ho, Shinn-Ying
author_sort Liaw, Chyn
collection PubMed
description Antibody 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 non-amyloidogenic 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.
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spelling pubmed-35387822013-01-10 Prediction and Analysis of Antibody Amyloidogenesis from Sequences Liaw, Chyn Tung, Chun-Wei Ho, Shinn-Ying PLoS One Research Article Antibody 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 non-amyloidogenic 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. Public Library of Science 2013-01-07 /pmc/articles/PMC3538782/ /pubmed/23308169 http://dx.doi.org/10.1371/journal.pone.0053235 Text en © 2013 Liaw et al http://creativecommons.org/licenses/by/4.0/ 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 properly credited.
spellingShingle Research Article
Liaw, Chyn
Tung, Chun-Wei
Ho, Shinn-Ying
Prediction and Analysis of Antibody Amyloidogenesis from Sequences
title Prediction and Analysis of Antibody Amyloidogenesis from Sequences
title_full Prediction and Analysis of Antibody Amyloidogenesis from Sequences
title_fullStr Prediction and Analysis of Antibody Amyloidogenesis from Sequences
title_full_unstemmed Prediction and Analysis of Antibody Amyloidogenesis from Sequences
title_short Prediction and Analysis of Antibody Amyloidogenesis from Sequences
title_sort prediction and analysis of antibody amyloidogenesis from sequences
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3538782/
https://www.ncbi.nlm.nih.gov/pubmed/23308169
http://dx.doi.org/10.1371/journal.pone.0053235
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