Newest papers

EMBER: Predicting TCR--pMHC Binding Without a Docked Complex

Lin Ha, Chen Zhou, Haozheng Luo, Han Liu, Xiaoyu Zhang, Yan Chen · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27334 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

私たちは、TCR−pMHC結合予測のための構造ベースのフレームワークであるEMBERを提案し、予測ドッキング複合体の必要性を排除し、それに伴い、そのような予測を支配する配置エラーを排除します。伝統的な構造ベースの予測器は、受容体と抗原の共に折りたたたみ複雑な構造を使用し、その接触表面から結合を解釈します。したがって、下流の機能は、導入された構造予測ツールのいかなる偏差も継承します。 方法論的には、EMBERはTCRとpMHCを独立して折りたたみ、それらをSE(3)不変の残留グラフとしてコードし、共通の座標フレームを構築することなく

Machine translation (Meta M2M100); the English original follows.

Abstract

We propose EMBER, a structure-based framework for TCR--pMHC binding prediction that eliminates the need for a predicted docked complex, and with it the placement error that dominates such predictions. Traditional structure-based predictors utilize the co-folded complex structure of the receptor and the antigen and interpret binding from its contact surface. Consequently, every downstream feature inherits any deviation the structure-prediction tool introduced. Methodologically, EMBER folds the TCR and pMHC independently, encodes them as SE(3)-invariant residue graphs, and compares their learned representations without constructing a shared coordinate frame. A relative pose is therefore not an input of the model, so placement error cannot propagate into the prediction. Computationally, EMBER preprocesses $O(N{+}M)$ monomer folds where a complex-based predictor needs $O(NM)$ operations. Experimentally, under epitope-disjoint evaluation with six retrained baselines, EMBER is first on every metric under all four folding tools, by 0.9 to 14.4% in micro-AUROC, 18.5 to 25.3% in micro-AUPRC and 10.6 to 24.1% in MCC. Its ranking is also up to 85% more stable across folding tools.

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Topics: Computer science, Metric (unit), Set (abstract data type), Folding (DSP implementation), Algorithm, Artificial intelligence

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