DOI: 10.1101/468363Nov 14, 2018Paper

ACME: Pan-specific peptide-MHC class I binding prediction through attention-based deep neural networks

BioRxiv : the Preprint Server for Biology
Yan HuJianyang Zeng


Prediction of peptide binding to MHC molecules plays a vital role in the development of therapeutic vaccines for the treatment of cancer. Although numerous computational methods have been developed to this end, several challenges still remain in predicting peptide-MHC interactions. Many previous methods are allele-specific, training separate models for individual alleles and are thus unable to yield accurate predictions for those alleles with limited training data. Despite that there exist several pan-specific algorithms that train a common model for different alleles, they only adopt simple model structures that generally have limited performance in capturing the complex underlying patterns of peptide-MHC interactions. Here we present ACME (Attention-based Convolutional neural networks for MHC Epitope binding prediction), a new pan-specific algorithm to accurately predict the binding affinities between peptides and MHC class I molecules, even for those new alleles that are not seen in the training data. Extensive tests have demonstrated that ACME can significantly outperform other state-of-the-art prediction methods with an increase of the Pearson Correlation Coefficient by up to 23 percent. In addition, its ability to identif...Continue Reading

Related Concepts

Malignant Neoplasms
Histocompatibility Antigens Class I
Oxytocin, N-acetyl-2-O-methyl-Tyr-
Therapeutic vaccine, NOS
Binding (Molecular Function)
Small Molecule

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