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Combining Three-Dimensional Modeling with Artificial Intelligence to Increase Specificity and Precision in Peptide–MHC Binding Predictions
- Source :
- The Journal of Immunology. 205:1962-1977
- Publication Year :
- 2020
- Publisher :
- The American Association of Immunologists, 2020.
-
Abstract
- The reliable prediction of the affinity of candidate peptides for the MHC is important for predicting their potential antigenicity and thus influences medical applications, such as decisions on their inclusion in T cell–based vaccines. In this study, we present a rapid, predictive computational approach that combines a popular, sequence-based artificial neural network method, NetMHCpan 4.0, with three-dimensional structural modeling. We find that the ensembles of bound peptide conformations generated by the programs MODELLER and Rosetta FlexPepDock are less variable in geometry for strong binders than for low-affinity peptides. In tests on 1271 peptide sequences for which the experimental dissociation constants of binding to the well-characterized murine MHC allele H-2Db are known, by applying thresholds for geometric fluctuations the structure-based approach in a standalone manner drastically improves the statistical specificity, reducing the number of false positives. Furthermore, filtering candidates generated with NetMHCpan 4.0 with the structure-based predictor led to an increase in the positive predictive value (PPV) of the peptides correctly predicted to bind very strongly (i.e., Kd < 100 nM) from 40 to 52% (p = 0.027). The combined method also significantly improved the PPV when tested on five human alleles, including some with limited data for training. Overall, an average increase of 10% in the PPV was found over the standalone sequence-based method. The combined method should be useful in the rapid design of effective T cell–based vaccines.
- Subjects :
- chemistry.chemical_classification
Antigenicity
Artificial neural network
biology
Chemistry
Immunology
Peptide
MODELLER
Plasma protein binding
Computational biology
Major histocompatibility complex
03 medical and health sciences
0302 clinical medicine
Protein structure
biology.protein
False positive paradox
Immunology and Allergy
030215 immunology
Subjects
Details
- ISSN :
- 15506606 and 00221767
- Volume :
- 205
- Database :
- OpenAIRE
- Journal :
- The Journal of Immunology
- Accession number :
- edsair.doi...........9a286913d16076dcb25e3c408ea1b37e