Type

Journal Article

Authors

Catherine Mooney
Denis C. Shields
Gianluca Pollastri
Thérèse A Holton

Subjects

Biology

Topics
sequences neural networks web computational biology prediction peptide sequences cpp prediction method training neural network cellular uptake cell penetrating peptides acids

CPPpred: prediction of cell penetrating peptides. (2013)

Abstract 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.
Collections Ireland -> University College Dublin -> College of Science
Ireland -> University College Dublin -> Computer Science Research Collection
Ireland -> University College Dublin -> PubMed
Ireland -> University College Dublin -> School of Computer Science

Full list of authors on original publication

Catherine Mooney, Denis C. Shields, Gianluca Pollastri, Thérèse A Holton

Experts in our system

1
Catherine Mooney
University College Dublin
Total Publications: 63
 
2
Denis C. Shields
University College Dublin
Total Publications: 123