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1. The Role of Hydrophobicity in Peptide-MHC Binding

   https://doi.org/10.1007/978-3-030-91241-3_3  

   Arnav Solanki, Marc Riedel (October 2021, Lecture notes in computer science)

   George Bebis, Terry Gaasterland (Ed.)

   Major Histocompability Complex (MHC) Class I molecules provide a pathway for cells to present endogenous peptides to the immune system, allowing it to distinguish healthy cells from those infected by pathogens. Software tools based on neural networks such as NetMHC and NetMHCpan predict whether peptides will bind to variants of MHC molecules. These tools are trained with experimental data, consisting of the amino acid sequence of peptides and their observed binding strength. Such tools generally do not explicitly consider hydrophobicity, a significant biochemical factor relevant to peptide binding. It was observed that these tools predict that some highly hydrophobic peptides will be strong binders, which biochemical factors suggest is incorrect. This paper investigates the correlation of the hydrophobicity of 9-mer peptides with their predicted binding strength to the MHC variant HLA-A*0201 for these software tools. Two studies were performed, one using the data that the neural networks were trained on and the other using a sample of the human proteome. A significant bias within NetMHC-4.0 towards predicting highly hydrophobic peptides as strong binders was observed in both studies. This suggests that hydrophobicity should be included in the training data of the neural networks. Retraining the neural networks with such biochemical annotations of hydrophobicity could increase the accuracy of their predictions, increasing their impact in applications such as vaccine design and neoantigen identification. 

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2. Improved prediction of MHC-peptide binding using protein language models

   https://doi.org/10.3389/fbinf.2023.1207380  

   Hashemi, Nasser; Hao, Boran; Ignatov, Mikhail; Paschalidis, Ioannis Ch.; Vakili, Pirooz; Vajda, Sandor; Kozakov, Dima (August 2023, Frontiers in Bioinformatics)

   Major histocompatibility complex Class I (MHC-I) molecules bind to peptides derived from intracellular antigens and present them on the surface of cells, allowing the immune system (T cells) to detect them. Elucidating the process of this presentation is essential for regulation and potential manipulation of the cellular immune system. Predicting whether a given peptide binds to an MHC molecule is an important step in the above process and has motivated the introduction of many computational approaches to address this problem. NetMHCPan, a pan-specific model for predicting binding of peptides to any MHC molecule, is one of the most widely used methods which focuses on solving this binary classification problem using shallow neural networks. The recent successful results of Deep Learning (DL) methods, especially Natural Language Processing (NLP-based) pretrained models in various applications, including protein structure determination, motivated us to explore their use in this problem. Specifically, we consider the application of deep learning models pretrained on large datasets of protein sequences to predict MHC Class I-peptide binding. Using the standard performance metrics in this area, and the same training and test sets, we show that our models outperform NetMHCpan4.1, currently considered as the-state-of-the-art. 

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3. The electrostatic landscape of MHC-peptide binding revealed using inception networks

   https://doi.org/10.1016/j.cels.2024.03.001  

   Wilson, Eric; Cava, John Kevin; Chowell, Diego; Raja, Remya; Mangalaparthi, Kiran K; Pandey, Akhilesh; Curtis, Marion; Anderson, Karen S; Singharoy, Abhishek (April 2024, Cell Systems)
4. Markov state modeling reveals alternative unbinding pathways for peptide–MHC complexes

   https://doi.org/10.1073/pnas.2007246117  

   Abella, Jayvee R.; Antunes, Dinler; Jackson, Kyle; Lizée, Gregory; Clementi, Cecilia; Kavraki, Lydia E. (November 2020, Proceedings of the National Academy of Sciences)

   Peptide binding to major histocompatibility complexes (MHCs) is a central component of the immune system, and understanding the mechanism behind stable peptide–MHC binding will aid the development of immunotherapies. While MHC binding is mostly influenced by the identity of the so-called anchor positions of the peptide, secondary interactions from nonanchor positions are known to play a role in complex stability. However, current MHC-binding prediction methods lack an analysis of the major conformational states and might underestimate the impact of secondary interactions. In this work, we present an atomically detailed analysis of peptide–MHC binding that can reveal the contributions of any interaction toward stability. We propose a simulation framework that uses both umbrella sampling and adaptive sampling to generate a Markov state model (MSM) for a coronavirus-derived peptide (QFKDNVILL), bound to one of the most prevalent MHC receptors in humans (HLA-A24:02). While our model reaffirms the importance of the anchor positions of the peptide in establishing stable interactions, our model also reveals the underestimated importance of position 4 (p4), a nonanchor position. We confirmed our results by simulating the impact of specific peptide mutations and validated these predictions through competitive binding assays. By comparing the MSM of the wild-type system with those of the D4A and D4P mutations, our modeling reveals stark differences in unbinding pathways. The analysis presented here can be applied to any peptide–MHC complex of interest with a structural model as input, representing an important step toward comprehensive modeling of the MHC class I pathway. 

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5. Novel insights on aquatic mammal MHC evolution: Evidence from manatee DQB diversity

   https://doi.org/10.1016/j.dci.2022.104398  

   Sá, André Luiz; Baker, Pamela Ketrya; Breaux, Breanna; Oliveira, Jairo Moura; Klautau, Alex Garcia; Legatzki, Kristian; Luna, Fábia de; Attademo, Fernanda Löffler; Hunter, Margaret Elizabeth; Criscitiello, Michael Frederick; et al (July 2022, Developmental & Comparative Immunology)