An automated benchmarking platform for MHC class II binding
Andreatta, M., Trolle, T., Yan, Z., Greenbaum, J. A., Peters, B. and Nielsen, M.
Instituto de Investigaciones Biotecnologicas, Universidad Nacional de San Martin, 1650 San Martin, Buenos Aires, Argentina.
Evaxion Biotech, DK-2200 Copenhagen N, Denmark.
Bioinformatics Core Facility, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.
Department of Bio and Health Informatics, Technical University of Denmark, DK-2800 Lyngby, Denmark.
Motivation: Computational methods for the prediction of peptide-MHC binding have become an integral and essential component for candidate selection in experimental T cell epitope discovery studies. The sheer amount of published prediction methods - and often discordant reports on their performance - poses a considerable quandary to the experimentalist who needs to choose the best tool for their research. Results: With the goal to provide an unbiased, transparent evaluation of the state-of-the-art in the field, we created an automated platform to benchmark peptide-MHC class II binding prediction tools. The platform evaluates the absolute and relative predictive performance of all participating tools on data newly entered into the Immune Epitope Database (IEDB) before they are made public, thereby providing a frequent, unbiased assessment of available prediction tools. The benchmark runs on a weekly basis, is fully automated, and displays up-to-date results on a publicly accessible website. The initial benchmark described here included six commonly used prediction servers, but other tools are encouraged to join with a simple sign-up procedure. Performance evaluation on 59 data sets composed of over 10,000 binding affinity measurements suggested that NetMHCIIpan is currently the most accurate tool, followed by NN-align and the IEDB consensus method. Availability: Weekly reports on the participating methods can be found online at: Contact: Supplementary information: Supplementary data are available at Bioinformatics online.
Bioinformatics 34(9): 1522-1528 (2018)