RT Journal Article
SR Electronic
T1 Interpretable deep learning to uncover the molecular binding patterns determining TCR–epitope interactions
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.05.02.490264
DO 10.1101/2022.05.02.490264
A1 Ceder Dens
A1 Wout Bittremieux
A1 Fabio Affaticati
A1 Kris Laukens
A1 Pieter Meysman
YR 2022
UL http://biorxiv.org/content/early/2022/05/02/2022.05.02.490264.abstract
AB Background The recognition of an epitope by a T-cell receptor (TCR) is crucial for eliminating pathogens and establishing immunological memory. Prediction of the binding of any TCR–epitope pair is still a challenging task, especially with novel epitopes, because the underlying patterns that drive the recognition are still largely unknown to both domain experts and machine learning models.Results The binding of a TCR and epitope sequence can only occur when amino acids from both sequences are in close contact with each other. We analyze the distance between interacting molecules of the TCR and epitope sequences and compare this to the amino acids that are important for TCR–epitope prediction models. Important residues are determined by using interpretable deep learning techniques or, more specifically, feature attribution extraction methods, on two state-of-the-art TCR–epitope prediction models: ImRex and TITAN. Highlighting feature attributions on the molecular complex reveals additional insights to the domain expert about why the prediction was made and can offer novel insights into the factors that determine TCR affinity on a molecular level. We also show which residues of the TCR and epitope sequences determine binding prediction for ImRex and TITAN and use those to explain model performance.Conclusions Extracting feature attributions is a useful way to verify your model and data for challenging problems where small hard-to-detect problems can accumulate to inaccurate results.Competing Interest StatementKL and PM hold shares in ImmuneWatch BV, an immunoinformatics company. No payments or services were received.