Paolo Marcatili

TL;DR: NetMHCpan-4.0, a method trained on binding affinity and eluted ligand data leveraging the information from both data types, demonstrates an increase in predictive performance compared with state-of-the-art methods when it comes to identification of naturally processed ligands, cancer neoantigens, and T cell epitopes.

TL;DR: This new method was found to outperform other available tools for sequence-based epitope prediction both on epitope data derived from solved 3D structures, and on a large collection of linear epitopes downloaded from the IEDB database.

TL;DR: It is shown that training with this extended data set improved the performance for peptide binding predictions for both methods, and updated versions of two MHC-II-peptide binding affinity prediction methods, NetM HCII and NetMHCIIpan are presented.

TL;DR: The accuracy of NetSurfP‐2.0 is assessed and it is found to consistently produce state‐of‐the‐art predictions for each of its output features, and the processing time has been optimized to allow predicting more than 1000 proteins in less than 2 hours, and complete proteomes in more than 1 day.

TL;DR: This IEDB-AR update provides a substantial set of updated and novel features for epitope prediction and analysis, focusing on the 10 new tools that have been added since the last report in the 2012 NAR webserver edition.