A Systematic Assessment of MHC Class II Peptide Binding Predictions and Evaluation of a Consensus Approach
By Peng Wang, John Sidney, Courtney Dow, Bianca Mothé, Alessandro Sette, Bjoern Peters. PLoS Comput. Biol., 2008
853 citations recorded by the source corpus.
The identification of MHC class II restricted peptide epitopes is an important goal in immunological research. A number of computational tools have been developed for this purpose, but there is a lack of large-scale systematic evaluation of their performance. Herein, we used a comprehensive dataset consisting of more than 10,000 previously unpublished MHC-peptide binding affinities, 29 peptide/MHC crystal structures, and 664 peptides experimentally tested for CD4+ T cell responses to systematically evaluate the performances of publicly available MHC class II binding prediction tools. While in selected instances the best tools were associated with AUC values up to 0.86, in general, class II predictions did not perform as well as historically noted for class I predictions. It appears that the ability of MHC class II molecules to bind variable length peptides, which requires the correct assignment of peptide binding cores, is a critical factor limiting the performance of existing prediction tools. To improve performance, we implemented a consensus prediction approach that combines methods with top performances. We show that this consensus approach achieved best overall performance. Finally, we make the large datasets used publicly available as a benchmark to facilitate further development of MHC class II binding peptide prediction methods.
Subfields: Immunology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/8b1cafdb2c1e48bff8c4427a9de4d77ed28cc6a0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Immunology
- Bjoern Peters
- Morten Nielsen
- Alessandro Sette
- Sinu Paul
- Ole Lund
- Swapnil Mahajan
- Sandeep Kumar Dhanda
- Ward Fleri
In Molecular Biology
- Morten Nielsen
- Bjoern Peters
- Alessandro Sette
- Ole Lund
- Sinu Paul
- Swapnil Mahajan
- Sandeep Kumar Dhanda
- John Sidney
Citations in the corpus
- The Immune Epitope Database and Analysis Resource in Epitope Discovery and Synthetic Vaccine Design (2017, methodology): “Several different prediction methods are available including IEDB recommended, Consensus (41, 46), netMHCIIpan (47, 48), SMM-align (49), Sturniolo (50), and NN-align (51).”
- NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction (2009, methodology): “During the last decade, large efforts have been invested in developing methods to allow for in silico screening of pathogenic organisms with the purpose of identifying peptides that will bind MHC class II molecules in a given host [5-20].”
- An overview of bioinformatics tools for epitope prediction: Implications on vaccine development (2015, methodology): “In order to improve these limitations, a consensus prediction tool was develop by combining these two class II binding prediction methods, observing an improved performance when compared to individual outputs as 10 of the 14 candidates bound MHCII [90].”
- Fundamentals and Methods for T- and B-Cell Epitope Prediction (2017, neutral): “Moreover, it has been shown that the performance of peptideMHC predictions is improved by combining several methods and providing consensus predictions [52].”
- Peptide binding predictions for HLA DR, DP and DQ molecules (2010, methodology): “< 1000 nM) and nonbinders (experimental IC50≥1000 nM) as described previously [30].”