MITNet: a fusion transformer and convolutional neural network architecture approach for T-cell epitope prediction

Abstract

Classifying epitopes is essential since they can be applied in various fields, including therapeutics, diagnostics and peptide-based vaccines. To determine the epitope or peptide against an antibody, epitope mapping with peptides is the most extensively used method. However, this method is more time-consuming and inefficient than using present methods. The ability to retrieve data on protein sequences through laboratory procedures has led to the development of computational models that predict epitope binding based on machine learning and deep learning (DL). It has also evolved to become a crucial part of developing effective cancer immunotherapies. This paper proposes an architecture to generalize this case since various research strives to solve a low-performance classification problem. A proposed DL model is the fusion architecture, which combines two architectures: Transformer architecture and convolutional neural network (CNN), called MITNet and MITNet-Fusion. Combining these two architectures enriches feature space to correlate epitope labels with the binary classification method. The selected epitope–T-cell receptor (TCR) interactions are GILG, GLCT and NLVP, acquired from three databases: IEDB, VDJdb and McPAS-TCR. The previous input data was extracted using amino acid composition, dipeptide composition, spectrum descriptor and the combination of all those features called AADIP composition to encode the input data to DL architecture. For ensuring consistency, fivefold cross-validations were performed using the area under curve metric. Results showed that GILG, GLCT and NLVP received scores of 0.85, 0.87 and 0.86, respectively. Those results were compared to prior architecture and outperformed other similar deep learning models.

TCR-H: Machine Learning Prediction of T-cell Receptor Epitope Binding on Unseen Datasets

“…With the increasing availability of TCR and epitope sequencing data from high-throughput sequencing techniques in publicly available data resources (Jokinen et al, 2021), AI and ML approaches have been able to be used to predict TCR CDR3β binding to epitopes presented by MHC class 1 (MHC-I) (Hudson et al, 2023). These tools apply methods ranging from relatively simple ML models such as Random Forest and clustering (Chronister et al, 2021; Dash et al, 2017; Jokinen et al, 2021; Pham et al, 2023) to various forms of deep learning-based AI techniques, including convolutional and recurrent neural networks (Bravi et al, 2023; Cai et al, 2022; Darmawan et al, 2023; Gao, Gao, Li, et al, 2023; Jiang et al, 2023; Jokinen et al, 2023; Myronov et al, 2023; D. Wang et al, 2023; Z.…”

“…Furthermore, it has also been shown that the use of features that lend themselves to ML models leads to better performance in TCR specificity prediction (Pham et al, 2023). Most ML models have been sequence-based, encoding sequences using either the BLOSUM substitution matrix (Cai et al, 2022; Darmawan et al, 2023; Gao, Gao, Li, et al, 2023; Pham et al, 2023) and/or Atchley factors(Atchley et al, 2005). The BLOSUM matrix applies a score for amino-acid substitutions while Atchley factors are multidimensional, composite features for each amino acid derived using unsupervised ML on primarily physicochemical features (Jiang et al, 2023; Lu et al, 2021; Moris et al, 2021).…”