ABSTRACT
MHC-I molecules expose the intracellular protein content on the cell surface, allowing T cells to detect foreign or mutated peptides. The combination of six MHC-I alleles each individual carries defines the sub-peptidome that can be effectively presented. We applied this concept to human cancer, hypothesizing that oncogenic mutations could arise in gaps in personal MHC-I presentation. To validate this hypothesis, we developed and applied a residue-centric patient presentation score to 9,176 cancer patients across 1,018 recurrent oncogenic mutations. We found that patient MHC-I genotype-based scores could predict which mutations were more likely to emerge in their tumor. Accordingly, poor presentation of a mutation across patients was correlated with higher frequency among tumors. These results support that MHC-I genotype-restricted immunoediting during tumor formation shapes the landscape of oncogenic mutations observed in clinically diagnosed tumors and paves the way for predicting personal cancer susceptibilities from knowledge of MHC-I genotype.
Subject(s)
Antigen Presentation , Histocompatibility Antigens Class I/genetics , Histocompatibility Antigens Class I/immunology , Mutation , Neoplasms/immunology , Cell Line, Tumor , Computer Simulation , Female , HeLa Cells , Humans , Male , Monitoring, Immunologic , ProteomeABSTRACT
Transient complexes are crucial for diverse biological processes such as biochemical pathways and signaling cascades in the cell. Here, we give an overview of the transient interactions; the importance of transient interactions as drug targets; and the structural characterization of transient protein-protein complexes based on the geometrical and physicochemical features of the transient complexes' interfaces. To better understand and eventually design transient protein-protein interactions (TPPIs), a molecular perspective of the protein-protein interfaces is necessary. Obtaining high-quality structures of protein-protein interactions could be one way of achieving this goal. After introducing the association kinetics of TPPIs, we elaborate on the experimental techniques detecting TPPIs in combination with the computational methods which classify transient and/or non-obligate complexes. In this review, currently available databases and servers that can be used to identify and predict TPPIs are also compiled.
