RT Journal Article
SR Electronic
T1 Current challenges for epitope-agnostic TCR interaction prediction and a new perspective derived from image classification
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2019.12.18.880146
DO 10.1101/2019.12.18.880146
A1 Pieter Moris
A1 Joey De Pauw
A1 Anna Postovskaya
A1 Sofie Gielis
A1 Nicolas De Neuter
A1 Wout Bittremieux
A1 Benson Ogunjimi
A1 Kris Laukens
A1 Pieter Meysman
YR 2020
UL http://biorxiv.org/content/early/2020/09/11/2019.12.18.880146.abstract
AB The prediction of epitope recognition by T-cell receptors (TCRs) has seen many advancements in recent years, with several methods now available that can predict recognition for a specific set of epitopes. However, the generic case of evaluating all possible TCR-epitope pairs remains challenging, mainly due to the high diversity of the interacting sequences and the limited amount of currently available training data. In this work, we provide an overview of the current state of this unsolved problem. First, we examine appropriate validation strategies to accurately assess the generalization performance of generic TCR-epitope recognition models when applied to both known and novel epitopes. In addition, we present a novel feature representation approach which we call ImRex (interaction map recognition). This approach is based on the pairwise combination of physicochemical properties of the individual amino acids in the CDR3 and epitope sequences, which provides a convolutional neural network with the combined representation of both sequences. Lastly, we highlight various challenges that are particular to TCR-epitope data and that can adversely affect model performance. These include the issue of selecting negative data, the imbalanced epitope distribution of curated TCR-epitope datasets, and the potential exchangeability of TCR alpha and beta chains. Our results indicate that while extrapolation to novel epitopes remains a difficult challenge, ImRex makes this feasible for a subset of epitopes that are not too dissimilar from the training data. We show that appropriate feature engineering methods and rigorous benchmark standards are required to create and validate TCR-epitope predictive models.Competing Interest StatementThe authors have declared no competing interest.