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dc.contributor.authorAbella, Jayvee R.
Antunes, Dinler A.
Clementi, Cecilia
Kavraki, Lydia E.
dc.date.accessioned 2020-11-04T18:47:51Z
dc.date.available 2020-11-04T18:47:51Z
dc.date.issued 2020
dc.identifier.citation Abella, 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." Frontiers in Immunology, (2020) Frontiers: https://doi.org/10.3389/fimmu.2020.01583.
dc.identifier.urihttps://hdl.handle.net/1911/109502
dc.description.abstract Prediction 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.
dc.language.iso eng
dc.publisher Frontiers
dc.rights This 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.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.title Large-Scale Structure-Based Prediction of Stable Peptide Binding to Class I HLAs Using Random Forests
dc.type Journal article
dc.citation.journalTitle Frontiers in Immunology
dc.contributor.org Center for Theoretical Biological Physics
dc.type.dcmi Text
dc.identifier.doihttps://doi.org/10.3389/fimmu.2020.01583
dc.type.publication publisher version


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