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1.
Structure ; 32(3): 292-303.e7, 2024 Mar 07.
Article in English | MEDLINE | ID: mdl-38157858

ABSTRACT

The CD4 or CD8 co-receptors' interaction with the protein-tyrosine kinase Lck initiates the tyrosine phosphorylation cascade leading to T cell activation. A critical question is: to what extent are co-receptors and Lck coupled? Our contribution concerns Zn2+, indispensable for CD4- and CD8-Lck formation. We combined biochemical and cellular approaches to show that dynamic fluctuations of free Zn2+ in physiological ranges influence Zn(CD4)2 and Zn(CD4)(Lck) species formation and their ratio, although the same Zn(Cys)2(Cys)2 cores. Moreover, we demonstrated that the affinity of Zn2+ to CD4 and CD4-Lck species differs significantly. Increased intracellular free Zn2+ concentration in T cells causes higher CD4 partitioning in the plasma membrane. We additionally found that CD4 palmitoylation decreases the specificity of CD4-Lck formation in the reconstituted membrane model. Our findings help elucidate co-receptor-Lck coupling stoichiometry and demonstrate that intracellular free Zn2+ has a major role in the interplay between CD4 dimers and CD4-Lck assembly.


Subject(s)
Lymphocyte Specific Protein Tyrosine Kinase p56(lck) , T-Lymphocytes , T-Lymphocytes/metabolism , Lymphocyte Specific Protein Tyrosine Kinase p56(lck)/metabolism , CD4 Antigens , Signal Transduction , Phosphorylation , Zinc/metabolism , Receptors, Antigen, T-Cell
2.
Science ; 381(6656): eadh1720, 2023 07 28.
Article in English | MEDLINE | ID: mdl-37499032

ABSTRACT

Fine-tuning of protein-protein interactions occurs naturally through coevolution, but this process is difficult to recapitulate in the laboratory. We describe a platform for synthetic protein-protein coevolution that can isolate matched pairs of interacting muteins from complex libraries. This large dataset of coevolved complexes drove a systems-level analysis of molecular recognition between Z domain-affibody pairs spanning a wide range of structures, affinities, cross-reactivities, and orthogonalities, and captured a broad spectrum of coevolutionary networks. Furthermore, we harnessed pretrained protein language models to expand, in silico, the amino acid diversity of our coevolution screen, predicting remodeled interfaces beyond the reach of the experimental library. The integration of these approaches provides a means of simulating protein coevolution and generating protein complexes with diverse molecular recognition properties for biotechnology and synthetic biology.


Subject(s)
Directed Molecular Evolution , Protein Interaction Domains and Motifs , Proteins , Amino Acids/chemistry , Machine Learning , Proteins/chemistry , Directed Molecular Evolution/methods , Datasets as Topic , Staphylococcal Protein A/chemistry
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