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Prediction of Cell Penetrating Peptides by Support Vector Machines

Cell penetrating peptides (CPPs) are those peptides that can transverse cell membranes to enter cells. Once inside the cell, different CPPs can localize to different cellular components and perform different roles. Some generate pore-forming complexes resulting in the destruction of cells while othe...

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Autores principales: Sanders, William S., Johnston, C. Ian, Bridges, Susan M., Burgess, Shane C., Willeford, Kenneth O.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3136433/
https://www.ncbi.nlm.nih.gov/pubmed/21779156
http://dx.doi.org/10.1371/journal.pcbi.1002101
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author Sanders, William S.
Johnston, C. Ian
Bridges, Susan M.
Burgess, Shane C.
Willeford, Kenneth O.
author_facet Sanders, William S.
Johnston, C. Ian
Bridges, Susan M.
Burgess, Shane C.
Willeford, Kenneth O.
author_sort Sanders, William S.
collection PubMed
description Cell penetrating peptides (CPPs) are those peptides that can transverse cell membranes to enter cells. Once inside the cell, different CPPs can localize to different cellular components and perform different roles. Some generate pore-forming complexes resulting in the destruction of cells while others localize to various organelles. Use of machine learning methods to predict potential new CPPs will enable more rapid screening for applications such as drug delivery. We have investigated the influence of the composition of training datasets on the ability to classify peptides as cell penetrating using support vector machines (SVMs). We identified 111 known CPPs and 34 known non-penetrating peptides from the literature and commercial vendors and used several approaches to build training data sets for the classifiers. Features were calculated from the datasets using a set of basic biochemical properties combined with features from the literature determined to be relevant in the prediction of CPPs. Our results using different training datasets confirm the importance of a balanced training set with approximately equal number of positive and negative examples. The SVM based classifiers have greater classification accuracy than previously reported methods for the prediction of CPPs, and because they use primary biochemical properties of the peptides as features, these classifiers provide insight into the properties needed for cell-penetration. To confirm our SVM classifications, a subset of peptides classified as either penetrating or non-penetrating was selected for synthesis and experimental validation. Of the synthesized peptides predicted to be CPPs, 100% of these peptides were shown to be penetrating.
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spelling pubmed-31364332011-07-21 Prediction of Cell Penetrating Peptides by Support Vector Machines Sanders, William S. Johnston, C. Ian Bridges, Susan M. Burgess, Shane C. Willeford, Kenneth O. PLoS Comput Biol Research Article Cell penetrating peptides (CPPs) are those peptides that can transverse cell membranes to enter cells. Once inside the cell, different CPPs can localize to different cellular components and perform different roles. Some generate pore-forming complexes resulting in the destruction of cells while others localize to various organelles. Use of machine learning methods to predict potential new CPPs will enable more rapid screening for applications such as drug delivery. We have investigated the influence of the composition of training datasets on the ability to classify peptides as cell penetrating using support vector machines (SVMs). We identified 111 known CPPs and 34 known non-penetrating peptides from the literature and commercial vendors and used several approaches to build training data sets for the classifiers. Features were calculated from the datasets using a set of basic biochemical properties combined with features from the literature determined to be relevant in the prediction of CPPs. Our results using different training datasets confirm the importance of a balanced training set with approximately equal number of positive and negative examples. The SVM based classifiers have greater classification accuracy than previously reported methods for the prediction of CPPs, and because they use primary biochemical properties of the peptides as features, these classifiers provide insight into the properties needed for cell-penetration. To confirm our SVM classifications, a subset of peptides classified as either penetrating or non-penetrating was selected for synthesis and experimental validation. Of the synthesized peptides predicted to be CPPs, 100% of these peptides were shown to be penetrating. Public Library of Science 2011-07-14 /pmc/articles/PMC3136433/ /pubmed/21779156 http://dx.doi.org/10.1371/journal.pcbi.1002101 Text en Sanders 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
Sanders, William S.
Johnston, C. Ian
Bridges, Susan M.
Burgess, Shane C.
Willeford, Kenneth O.
Prediction of Cell Penetrating Peptides by Support Vector Machines
title Prediction of Cell Penetrating Peptides by Support Vector Machines
title_full Prediction of Cell Penetrating Peptides by Support Vector Machines
title_fullStr Prediction of Cell Penetrating Peptides by Support Vector Machines
title_full_unstemmed Prediction of Cell Penetrating Peptides by Support Vector Machines
title_short Prediction of Cell Penetrating Peptides by Support Vector Machines
title_sort prediction of cell penetrating peptides by support vector machines
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3136433/
https://www.ncbi.nlm.nih.gov/pubmed/21779156
http://dx.doi.org/10.1371/journal.pcbi.1002101
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