CNN-PepPred: an open-source tool to create convolutional NN models for the discovery of patterns in peptide sets-application to peptide-MHC class II binding prediction.

Summary: The ability to unveil binding patterns in peptide sets has important applications in several biomedical areas, including the development of vaccines. We present an open-source tool, CNN-PepPred, that uses convolutional neural networks to discover such patterns, along with its application to peptide-HLA class II binding prediction. The tool can be used locally on different operating systems, with CPUs or GPUs, to train, evaluate, apply and visualize models.
Availability and Implementation: CNN-PepPred is freely available as a Python tool with a detailed User's Guide at https://github.com/ComputBiol-IBB/CNN-PepPred. The site includes the peptide sets used in this study, extracted from IEDB (www.iedb.org).
Supplementary Information: Supplementary data are available at Bioinformatics online.
(© The Author(s) 2021. Published by Oxford University Press.)