CPPpred: prediction of cell penetrating peptides

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Title: CPPpred: prediction of cell penetrating peptides
Authors: Holton, Thérèse A.
Pollastri, Gianluca
Shields, Denis C.
Mooney, Catherine
Permanent link: http://hdl.handle.net/10197/10082
Date: 1-Dec-2013
Online since: 2019-04-23T12:01:31Z
Abstract: Summary: Cell penetrating peptides (CPPs) are attracting much attention as a means of overcoming the inherently poor cellular uptake of various bioactive molecules. Here, we introduce CPPpred, a web server for the prediction of CPPs using a N-to-1 neural network. The server takes one or more peptide sequences, between 5 and 30 amino acids in length, as input and returns a prediction of how likely each peptide is to be cell penetrating. CPPpred was developed with redundancy reduced training and test sets, offering an advantage over the only other currently available CPP prediction method.
Funding Details: Enterprise Ireland
Science Foundation Ireland
Type of material: Journal Article
Publisher: Oxford University Press
Journal: Bioinformatics
Volume: 29
Issue: 23
Start page: 3094
End page: 3096
Copyright (published version): 2013 Oxford University Press
Keywords: Cell penetrating peptidesPeptide sequencesCPP prediction methodNeural networksComputational biology
DOI: 10.1093/bioinformatics/btt518
Language: en
Status of Item: Peer reviewed
Appears in Collections:Computer Science Research Collection

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