Quantification of Uncertainty in Peptide-MHC Binding Prediction Improves High-Affinity Peptide Selection for Therapeutic Design
Author(s)Zeng, Haoyang; Gifford, David K
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The 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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Zeng, 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 Inc
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