Large-Scale Structure-Based Prediction of Stable Peptide Binding to Class I HLAs Using Random Forests

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dc.citation.journalTitleFrontiers in Immunologyen_US
dc.contributor.authorAbella, Jayvee R.en_US
dc.contributor.authorAntunes, Dinler A.en_US
dc.contributor.authorClementi, Ceciliaen_US
dc.contributor.authorKavraki, Lydia E.en_US
dc.contributor.orgCenter for Theoretical Biological Physicsen_US
dc.date.accessioned2020-11-04T18:47:51Zen_US
dc.date.available2020-11-04T18:47:51Zen_US
dc.date.issued2020en_US
dc.description.abstractPrediction of stable peptide binding to Class I HLAs is an important component for designing immunotherapies. While the best performing predictors are based on machine learning algorithms trained on peptide-HLA (pHLA) sequences, the use of structure for training predictors deserves further exploration. Given enough pHLA structures, a predictor based on the residue-residue interactions found in these structures has the potential to generalize for alleles with little or no experimental data. We have previously developed APE-Gen, a modeling approach able to produce pHLA structures in a scalable manner. In this work we use APE-Gen to model over 150,000 pHLA structures, the largest dataset of its kind, which were used to train a structure-based pan-allele model. We extract simple, homogenous features based on residue-residue distances between peptide and HLA, and build a random forest model for predicting stable pHLA binding. Our model achieves competitive AUROC values on leave-one-allele-out validation tests using significantly less data when compared to popular sequence-based methods. Additionally, our model offers an interpretation analysis that can reveal how the model composes the features to arrive at any given prediction. This interpretation analysis can be used to check if the model is in line with chemical intuition, and we showcase particular examples. Our work is a significant step toward using structure to achieve generalizable and more interpretable prediction for stable pHLA binding.en_US
dc.identifier.citationAbella, Jayvee R., Antunes, Dinler A., Clementi, Cecilia, et al.. "Large-Scale Structure-Based Prediction of Stable Peptide Binding to Class I HLAs Using Random Forests." <i>Frontiers in Immunology,</i> (2020) Frontiers: https://doi.org/10.3389/fimmu.2020.01583.en_US
dc.identifier.doihttps://doi.org/10.3389/fimmu.2020.01583en_US
dc.identifier.urihttps://hdl.handle.net/1911/109502en_US
dc.language.isoengen_US
dc.publisherFrontiersen_US
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleLarge-Scale Structure-Based Prediction of Stable Peptide Binding to Class I HLAs Using Random Forestsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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