Comparison of different substitution matrices for distance based T-cell receptor epitope predictions using tcrdist3

Abstract

Various methods, differing in complexity, have been developed to predict T-cell receptor epitopes. tcrdist3, which implements an easy-to-interpret distance-based approach, has demonstrated performance comparable to the best feature-based methods. Here, a new substitution matrix for tcrdist3 is proposed and its performance is compared to various other substitution matrices. Small performance gains were possible; however tcrdist3 was found to perform reliably well with most substitution matrices. Randomly generated substitution matrices were used as a baseline and resulted in good classification results. It was observed that the prediction quality was negatively correlated with the relative standard deviation of the matrix used (i.e. a larger variance of the weights resulted in poorer predictivity). The most important factor of the tcrdist3-distance between two sequences that could be singled out is the number of substitutions. tcrdist3 implicitly considers the number of substitutions and the type of substitution simultaneously. Using substitution matrices with larger variance penalizes certain substitutions more strongly, which blurs the clusters of sequences with the same number of substitutions. Since the number of substitutions was a key predictor, this resulted in decreased prediction performance.

Keywords

• T-cell receptor
• Epitope prediction
• Computational
• Distance-based approaches