Large-Scale Structure-Based Prediction of Stable Peptide Binding to Class I HLAs Using Random Forests

Prediction of stable peptide binding to Class I HLAs is an important component for designing immunotherapies. While the best performing predictors are based on machine learning algorithms trained on peptide-HLA (pHLA) sequences, the use of structure for training predictors deserves further exploration. Given enough pHLA structures, a predictor based on the residue-residue interactions found in these structures has the potential to generalize for alleles with little or no experimental data. We have previously developed APE-Gen, a modeling approach able to produce pHLA structures in a scalable manner. In this work we use APE-Gen to model over 150,000 pHLA structures, the largest dataset of its kind, which were used to train a structure-based pan-allele model. We extract simple, homogenous features based on residue-residue distances between peptide and HLA, and build a random forest model for predicting stable pHLA binding. Our model achieves competitive AUROC values on leave-one-allele-out validation tests using significantly less data when compared to popular sequence-based methods. Additionally, our model offers an interpretation analysis that can reveal how the model composes the features to arrive at any given prediction. This interpretation analysis can be used to check if the model is in line with chemical intuition, and we showcase particular examples. Our work is a significant step toward using structure to achieve generalizable and more interpretable prediction for stable pHLA binding

Molecular docking of SP40 peptide towards cellular receptors for enterovirus 71 (EV-A71)

Abstract: Enterovirus 71 (EV-A71) is one of the predominant etiological agents of hand, foot and mouth disease (HMFD), which can cause severe central nervous system infections in young children. There is no clinically approved vaccine or antiviral agent against HFMD. The SP40 peptide, derived from the VP1 capsid of EV-A71, was reported to be a promising antiviral peptide that targeted the host receptor(s) involved in viral attachment or entry. So far, the mechanism of action of SP40 peptide is unknown. In this study, interactions between ten reported cell receptors of EV-A71 and the antiviral SP40 peptide were evaluated through molecular docking simulations, followed by in vitro receptor blocking with specific antibodies. The preferable binding region of each receptor to SP40 was predicted by global docking using HPEPDOCK and the cell receptor-SP40 peptide complexes were refined using FlexPepDock. Local molecular docking using GOLD (Genetic Optimization for Ligand Docking) showed that the SP40 peptide had the highest binding score to nucleolin followed by annexin A2, SCARB2 and human tryptophanyl-tRNA synthetase. The average Gold-Score for 5 top-scoring models of human cyclophilin, fibronectin, human galectin, DC-SIGN and vimentin were almost similar. Analysis of the nucleolin-SP40 peptide complex showed that SP40 peptide binds to the RNA binding domains (RBDs) of nucleolin. Furthermore, receptor blocking by specific monoclonal antibody was performed for seven cell receptors of EV-A71 and the results showed that the blocking of nucleolin by anti-nucleolin alone conferred a 93% reduction in viral infectivity. Maximum viral inhibition (99.5%) occurred when SCARB2 was concurrently blocked with anti-SCARB2 and the SP40 peptide. This is the first report to reveal the mechanism of action of SP40 peptide in silico through molecular docking analysis. This study provides information on the possible binding site of SP40 peptide to EV-A71 cellular receptors. Such information could be useful to further validate the interaction of the SP40 peptide with nucleolin by site-directed mutagenesis of the nucleolin binding site

DynaDom: structure-based prediction of T cell receptor inter-domain and T cell receptor-peptide-MHC (class I) association angles

Table S3. Per residue flip states using Reduce, Protoss and DynaDom comparing single domains and TCR complexes. (PDF 145 kb

Subangstrom Accuracy in pHLA‑I Modeling by Rosetta FlexPepDock Refinement Protocol

Flexible peptides binding to human leukocyte antigen (HLA) play a key role in mediating human immune responses and are also involved in idiosyncratic adverse drug reactions according to recent research. However, the structural determinations of pHLA complexes remain challenging under the present conditions. In this paper, the performance of a new peptide docking method, namely FlexPepDock, was systematically investigated by a benchmark of 30 crystallized structures of peptide-HLA class I complexes. The docking results showed that the near-native pHLA-I models with peptide bb-RMSD less than 2 Å were ranked in the top 1 model for 100% (70/70) docking cases, and the subangstrom models with peptide bb-RMSD less than 1 Å were ranked in the top 5 lowest-energy models for 65.7% (46/70) docking cases. Furthermore, 10 out of 70 docking cases ranked the subangstrom all-atom models in the top 5 lowest-energy models. The results showed that the FlexPepDock can generate high-quality models of pHLA-I complexes and can be widely applied to pHLA-I modeling and mechanism research of peptide-mediated immune responses