Markov state modeling reveals alternative unbinding pathways for peptide-MHC complexes.

Autor:	Abella JR; Department of Computer Science, Rice University, Houston, TX 77005., Antunes D; Department of Computer Science, Rice University, Houston, TX 77005., Jackson K; Department of Melanoma Medical Oncology-Research, The University of Texas MD Anderson Cancer Center, Houston, TX 77030., Lizée G; Department of Melanoma Medical Oncology-Research, The University of Texas MD Anderson Cancer Center, Houston, TX 77030., Clementi C; Center for Theoretical Biological Physics, Rice University, Houston, TX 77005.; Department of Chemistry, Rice University, Houston, TX 77005., Kavraki LE; Department of Computer Science, Rice University, Houston, TX 77005; kavraki@rice.edu.
Jazyk:	angličtina
Zdroj:	Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2020 Dec 01; Vol. 117 (48), pp. 30610-30618. Date of Electronic Publication: 2020 Nov 12.
DOI:	10.1073/pnas.2007246117
Abstrakt:	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. Competing Interests: The authors declare no competing interest.
Databáze:	MEDLINE
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