Title: Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution.

Authors: Jiang, Limin; Yu, Hui; Li, Jiawei; Tang, Jijun; Guo, Yan; Guo, Fei

Published In Brief Bioinform, (2021 11 05)

Abstract: Major histocompatibility complex (MHC) possesses important research value in the treatment of complex human diseases. A plethora of computational tools has been developed to predict MHC class I binders. Here, we comprehensively reviewed 27 up-to-date MHC I binding prediction tools developed over the last decade, thoroughly evaluating feature representation methods, prediction algorithms and model training strategies on a benchmark dataset from Immune Epitope Database. A common limitation was identified during the review that all existing tools can only handle a fixed peptide sequence length. To overcome this limitation, we developed a bilateral and variable long short-term memory (BVLSTM)-based approach, named BVLSTM-MHC. It is the first variable-length MHC class I binding predictor. In comparison to the 10 mainstream prediction tools on an independent validation dataset, BVLSTM-MHC achieved the best performance in six out of eight evaluated metrics. A web server based on the BVLSTM-MHC model was developed to enable accurate and efficient MHC class I binder prediction in human, mouse, macaque and chimpanzee.

PubMed ID: 34131696

MeSH Terms: Amino Acid Sequence; Binding Sites*; Carrier Proteins/chemistry*; Carrier Proteins/metabolism; Computational Biology/methods*; Databases, Factual; Deep Learning; Epitopes/chemistry; Epitopes/immunology; Epitopes/metabolism; Histocompatibility Antigens Class I/chemistry*; Histocompatibility Antigens Class I/immunology; Histocompatibility Antigens Class I/metabolism; Machine Learning; Neural Networks, Computer*; Protein Binding; ROC Curve; Reproducibility of Results; Software*; Web Browser