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Modeling Sequence Dependent Peptide Fluctuations in Immunologic Recognition.

Modeling Sequence Dependent Peptide Fluctuations in Immunologic Recognition.
Author Information (click to view)

Ayres CM, Riley TP, Corcelli SA, Baker BM,


Ayres CM, Riley TP, Corcelli SA, Baker BM, (click to view)

Ayres CM, Riley TP, Corcelli SA, Baker BM,

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Journal of chemical information and modeling 2017 07 11() doi 10.1021/acs.jcim.7b00118

Abstract

In cellular immunity, T cells recognize peptide antigens bound and presented by major histocompatibility complex (MHC) proteins. The motions of peptides bound to MHC play a significant role in determining immunogenicity. However, existing approaches for investigating peptide/MHC motional dynamics are challenging or of low throughput, hindering the development of algorithms for predicting immunogenicity from large databases, such as those of tumor or genetically unstable viral genomes. We addressed this by performing exhaustive molecular dynamics simulations on a large structural database of peptides bound to the most commonly expressed human class I MHC protein, HLA-A*0201. The simulations reproduced experimental indicators of motion and were used to generate simple models for predicting site-specific motions of bound peptides through differences in their sequence and chemical composition alone. The models can easily be applied on their own or incorporated into immunogenicity prediction algorithms. Beyond their predictive power, the models provided insight into how amino acid substitutions can influence peptide and protein motions and how dynamic information is communicated across peptides. They also indicate a link between peptide rigidity and hydrophobicity, two features known to be important in influencing cellular immune responses.

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