APE-Gen: A Fast Method for Generating Ensembles of Bound Peptide-MHC Conformations

Abstract

The Class I Major Histocompatibility Complex (MHC) is a central protein in immunology as it binds to intracellular peptides and displays them at the cell surface for recognition by T-cells. The structural analysis of bound peptide-MHC complexes (pMHCs) holds the promise of interpretable and general binding prediction (i.e., testing whether a given peptide binds to a given MHC). However, structural analysis is limited in part by the difficulty in modelling pMHCs given the size and flexibility of the peptides that can be presented by MHCs. This article describes APE-Gen (Anchored Peptide-MHC Ensemble Generator), a fast method for generating ensembles of bound pMHC conformations. APE-Gen generates an ensemble of bound conformations by iterated rounds of (i) anchoring the ends of a given peptide near known pockets in the binding site of the MHC, (ii) sampling peptide backbone conformations with loop modelling, and then (iii) performing energy minimization to fix steric clashes, accumulating conformations at each round. APE-Gen takes only minutes on a standard desktop to generate tens of bound conformations, and we show the ability of APE-Gen to sample conformations found in X-ray crystallography even when only sequence information is used as input. APE-Gen has the potential to be useful for its scalability (i.e., modelling thousands of pMHCs or even non-canonical longer peptides) and for its use as a flexible search tool. We demonstrate an example for studying cross-reactivity.

HLA-Arena: A Customizable Environment for the Structural Modeling and Analysis of Peptide-HLA Complexes for Cancer Immunotherapy

“… 14 - 16 Such conserved folding makes HLA modeling an easy task with tools leveraging homology modeling. 8 , 17 , 18 In contrast, predicting the binding modes of peptides to HLA receptors is much harder because of the size and flexibility of these peptides. As recently reviewed, strategies used to overcome this challenge include constrained backbone prediction, constrained termini prediction, and incremental prediction.…”

“…The first, called APE-Gen (anchored peptide-MHC ensemble generator), can quickly produce an ensemble of binding modes for a pHLA complex, using termini templates to position the peptide in the HLA binding cleft ( Fig 1 ; Appendix). 8 The second, called DINC, can incrementally dock a peptide in the binding site and does not require any template ( Fig 2 ; Appendix). 9 , 10 , 21 Each approach has different strengths and limitations and can therefore suit various user needs, depending on the task at hand.…”

“… 9 , 22 Both APE-Gen and DINC have been validated in previous publications. 8 , 10 , 23 In this report, we present a unified environment that facilitates the use of APE-Gen, DINC, and other tools for various research applications.…”

“…For example, we have described a fast method, called APE-Gen, to generate ensembles of peptide conformations bound to a given HLA receptor. 8 We have also developed a meta-docking approach, called DINC, which allows prediction of binding modes of pHLA complexes. 9 , 10 …”

Structural Modeling and Molecular Dynamics of the Immune Checkpoint Molecule HLA-G

“…A structure-based stability analysis was performed to compare two different HLA-G binders, RIIPRHLQL and RLPKDFRIL. The aforementioned complete model of HLA-G1 (HLA-G*01:01), after removed the bound peptide structure, was used as input to the Anchored Peptide-MHC Ensemble Generator (APE-Gen) ( 56 ). Generated ensembles of peptide conformations were later minimized with OpenMM ( 57 ), and the lowest energy conformation for each peptide was selected using the Vinardo scoring function ( 58 ).…”