PANDORA: a fast, anchor-restrained modelling protocol for peptide:MHC complexes

Deeper understanding T-cell mediated adaptive immune responses bears important implications in designing cancer immunotherapies and antiviral vaccines against pandemic outbreaks. T cells fire when they recognize foreign peptides that are presented on the cell surface by Major Histocompatibility Complexes (MHC), forming peptide:MHC (pMHC) complexes. 3D structure of pMHC complexes provides fundamental insight of T-cell recognition mechanism and aids immunotherapy design. High MHC and peptide diversities necessitate efficient computational modelling to enable whole proteome structural analysis. Here we present PANDORA, a robust pMHC structure modelling pipeline. Given a query, PANDORA searches for structural templates in its extensive database and then applies anchor restraints to the modelling process. This restrained energy minimization ensures one of the fastest pMHC modelling pipelines so far. On a set of 835 pMHC-I complexes over 78 MHC types, PANDORA generated models with a median RMSD of 0.68 [A] and achieved a 93% success rate in top 10 models. PANDORA performs competitively with three pMHC-I modelling state-of-the-art approaches and outperforms AlphaFold in terms of accuracy while being superior to it in speed. PANDORA is a modularized and user-configurable python package with easy installation. We envision PANDORA to fuel deep learning with large-scale high-quality 3D models to tackle long-standing immunology challenges.