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Abstract The ability to accurately identify peptide ligands for a given major histocompatibility complex class I (MHC-I) molecule has immense value for targeted anticancer therapeutics. However, the highly polymorphic nature of the MHC-I protein makes universal prediction of peptide ligands challenging due to lack of experimental data describing most MHC-I variants. To address this challenge, we have developed a deep convolutional neural network, HLA-Inception, capable of predicting MHC-I peptide binding motifs using electrostatic properties of the MHC-I binding pocket. By approaching this immunological issue using molecular biophysics, we measure the impact of sidechain arrangement and topology on peptide binding, feature not captured by sequence-based MHC-I prediction methods. Through a combination of molecular modeling and simulation, 5821 MHC-I alleles were modeled, providing extensive coverage across human populations. Predicted peptide binding motifs fell into distinct clusters, each defined with different degrees of submotif heterogeneity. Peptide binding scores generated by HLA-Inception are strongly correlated with quantitative MHC-I binding data, indicating predicted peptides can be ranked, both within and between alleles. HLA-inception also showed high precision when predicting naturally presented peptides and can be used for rapid proteome-scale MHC-I peptide binding predictions. Finally, we show that the binding pocket diversity measured by HLA inception predicts response to checkpoint blockade. Citation Format: Eric A. Wilson, John Kevin Cava, Diego Chowell, Abhishek Singharoy, Karen S. Anderson. Protein structure-based modeling to improve MHC class I epitope predictions. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5376.
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Rapid microsphere‐assisted peptide screening (MAPS) of promiscuous MHCII‐binding peptides in Zika virus envelope protein
Abstract Despite promising developments in computational tools, peptide‐class II MHC (MHCII) binding predictors continue to lag behind their peptide‐class I MHC counterparts. Consequently, peptide–MHCII binding is often evaluated experimentally using competitive binding assays, which tend to sacrifice throughput for quantitative binding detail. Here, we developed a high‐throughput semiquantitative peptide–MHCII screening strategy termed microsphere‐assisted peptide screening (MAPS) that aims to balance the accuracy of competitive binding assays with the throughput of computational tools. Using MAPS, we screened a peptide library from Zika virus envelope (E) protein for binding to four common MHCII alleles (DR1, DR4, DR7, DR15). Interestingly, MAPS revealed a significant overlap between peptides that promiscuously bind multiple MHCII alleles and antibody neutralization sites. This overlap was also observed for rotavirus outer capsid glycoprotein VP7, suggesting a deeper relationship between B cell and CD4+T cell specificity which can facilitate the design of broadly protective vaccines to Zika and other viruses.
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- PAR ID:
- 10459516
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- AIChE Journal
- Volume:
- 66
- Issue:
- 3
- ISSN:
- 0001-1541
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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