Finally, we will outline our own contribution to this field: the application of QSAR techniques to the prediction of peptide-MHC binding.doi:10.1007/978-0-387-39241-7_9Channa K HattotuwagamaPingping GuanMatthew DaviesDebra J TaylorDarren R Flower...
(MHC) molecules have an essential role in T-cell activation and initiating an adaptive immune response.Development of methods for prediction of MHC-Peptide binding is important in vaccine design and immunotherapy.In this study,we try to predict the binding between peptides and MHC class II....
validated by a series of experiments and achieved state-of-the-art performance in the peptide–MHC–TCR, peptide–MHC and peptide–TCR binding prediction tasks with up to 15% improvements in area under the precision–recall curve taking the peptide–MHC–TCR binding prediction task as an example...
It is important to accurately determine the performance of peptide:MHC binding predictions, as this enables users to compare and choose between different prediction methods and provides estimates of the expected error rate. Two common approaches to determine prediction performance are cross-validation, in...
Motivated by a text mining model designed for buildinga classifier from labeled and unlabeled examples, we have developed an iterative supervised learningmodel for the prediction of MHC class II binding peptides.Results: A linear programming (LP) model was employed for the learning task at each ...
MHCflurry implements class I peptide/MHC binding affinity prediction. The current version provides pan-MHC I predictors supporting any MHC allele of known sequence. MHCflurry runs on Python 3.9+ using thetensorflowneural network library. It exposescommand-lineandPython libraryinterfaces. ...
Existing studies show that MHC molecules can be classified into supertypes in terms of peptide-binding specificities. Therefore, we first give a method for reducing the redundancy in a given dataset based on information entropy, then present a novel approach for prediction by learning a predictive ...
Deep learning pan‐specific model for interpretable MHC‐I peptide binding prediction with improved attention mechanism. Proteins 89, 866–883 (2021). Article Google Scholar Yang, X., Zhao, L., Wei, F. & Li, J. DeepNetBim: deep learning model for predicting HLA-epitope interactions based ...
Here we introduce PUFFIN (Prediction of Uncertainty in MHC-peptide aFFInity using residual networks), a method for predicting MHC-peptide binding that outputs both the “expected affinity” of an input MHC-peptide pair as well as the “uncertainty” of the model about its prediction. PUFFIN has...
Numerous methods have been applied to the problem of predicting MHC binding. Prediction of MHC class I binding has been very successful, reporting prediction accuracies of up to 95% (e.g. [15]). Attempts at predicting class II MHC binding show significantly lower accuracies, although many ...