Benchmarking generative scaffold design methods for peptide engineering in TCR-MHC complexes

Abstract

De novo peptide design at T cell receptor-peptide-major histocompatibility complex (TCR-pMHC) interfaces is a central challenge in computational immunology, with direct implications for vaccine development, cancer immunotherapy, and autoimmune disease. Despite rapid advances in generative protein modeling, there is currently no systematic benchmark evaluating these methods in the highly constrained and immunologically relevant setting of peptide-MHC presentation and TCR recognition. Here, we present two complementary contributions. First, we introduce a multi-stage computational pipeline for peptide design in predefined TCR-pMHC contexts, integrating generative modeling with sequence optimization and structure-based filtering. Second, we establish a benchmark for evaluating generative peptide design methods in TCR-pMHC complexes. Using a curated dataset of high-quality crystal structures deposited after the AlphaFold3 training cutoff, we assess state-of-the-art generative approaches for peptide backbone generation, sequence design, and the enrichment of near-native solutions. We explicitly examine whether different backbone generation strategies respect the geometric constraints of the MHC binding groove and recover native-like peptide conformations. Our results reveal substantial method-dependent differences: some generative strategies fail systematically in the groove-bound peptide setting, whereas others generate physically plausible backbones with varying accuracy and conformational diversity. We further show that enforcing anchor constraints strongly influences peptide conformations at non-anchor positions, highlighting a trade-off between structural accuracy and conformational sampling. To enable fair and reproducible comparison, we introduce a standardized, multi-stage scoring protocol that integrates MHC binding prediction, physics-based energy evaluation, and independent structure prediction confidence metrics to enrich near-native designs from large candidate pools. Together, this work establishes the first comprehensive pipeline and benchmark for generative peptide design at TCR-pMHC interfaces and provides practical guidelines for developing peptide design workflows and evaluating generative models in immunologically constrained protein design settings.