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Free, publicly-accessible full text available January 1, 2026
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The aerobic electron transfer chain builds a proton gradient by proton coupled electron transfer reactions through a series of proteins. Complex I is the 昀椀rst enzyme in the sequence. Here transfer of two electrons from NADH to quinone yields four protons pumped from the membrane N- (negative, higher pH) side to the P- (positive, lower pH) side. Protons move through three linear antiporter paths, with a few amino acids and waters providing the route; and through the E-channel, a complex of competing paths, with clusters of interconnected protonatable residues. Proton loading sites (PLS) transiently bind protons as they are transported from N- to P-compartments. PLS can be individual residues or extended clusters of residues. The program MCCE uses Monte Carlos sampling to analyze the E-channel proton binding in equilibrium with individual Molecular Dynamics snapshots from tra- jectories of Thermus thermuphillus Complex I in the apo, quinone and quinol bound states. At pH 7, the 昀椀ve E- channel subunits (Nqo4, Nqo7, Nqo8, Nqo10, and Nqo11) take >25,000 protonation microstates, each with different residues protonated. The microstate explosion is tamed by analyzing interconnected clusters of residues along the proton transfer paths. A proton is bound and released from a cluster of 昀椀ve coupled residues on the protein N-side and to six coupled residues in the protein center. Loaded microstates bind protons to sites closer to the P-side in the forward pumping direction. MCCE microstate analysis identi昀椀es strongly coupled proton binding amongst individual residues in the two PLS clusters.more » « lessFree, publicly-accessible full text available January 1, 2026
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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.