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dc.contributor.authorZeng, Haoyang
dc.contributor.authorGifford, David K
dc.date.accessioned2020-12-23T20:34:01Z
dc.date.available2020-12-23T20:34:01Z
dc.date.issued2019-08
dc.date.submitted2019-03
dc.identifier.issn2405-4712
dc.identifier.urihttps://hdl.handle.net/1721.1/128919
dc.description.abstractThe computational identification of peptides that can bind the major histocompatibility complex (MHC) with high affinity is an essential step in developing personal immunotherapies and vaccines. We introduce PUFFIN, a deep residual network-based computational approach that quantifies uncertainty in peptide-MHC affinity prediction that arises from observational noise and the lack of relevant training examples. With PUFFIN's uncertainty metrics, we define binding likelihood, the probability a peptide binds to a given MHC allele at a specified affinity threshold. Compared to affinity point estimates, we find that binding likelihood correlates better with the observed affinity and reduces false positives in high-affinity peptide design. When applied to examine an existing peptide vaccine, PUFFIN identifies an alternative vaccine formulation with higher binding likelihood. PUFFIN is freely available for download at http://github.com/gifford-lab/PUFFIN. Machine-learning models that predict the binding affinity of a peptide-MHC pair are essential in peptide-based therapeutic design, but state-of-the-art methods provide point estimates of affinity that do not consider measurement noise and model uncertainty. We introduce PUFFIN, a method that quantifies the prediction uncertainty and prioritizes peptides with “binding likelihood” to achieve improved accuracy in high-affinity peptide selection for therapeutic design.en_US
dc.description.sponsorshipNational Institute of Health (Grant R01CA218094)en_US
dc.language.isoen
dc.publisherElsevier BVen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.cels.2019.05.004en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.sourcePMCen_US
dc.titleQuantification of Uncertainty in Peptide-MHC Binding Prediction Improves High-Affinity Peptide Selection for Therapeutic Designen_US
dc.typeArticleen_US
dc.identifier.citationZeng, Haoyang and David K. Gifford. "Quantification of Uncertainty in Peptide-MHC Binding Prediction Improves High-Affinity Peptide Selection for Therapeutic Design." Cell Systems 9, 2 (August 2019): P159-166.e3 © 2019 Elsevier Incen_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.relation.journalCell Systemsen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2020-12-15T17:09:55Z
dspace.orderedauthorsZeng, H; Gifford, DKen_US
dspace.date.submission2020-12-15T17:09:57Z
mit.journal.volume9en_US
mit.journal.issue2en_US
mit.licensePUBLISHER_CC


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