RESUMO
The Src-like adaptor proteins (SLAP/SLAP2) bind to CBL E3 ubiquitin ligase to downregulate antigen, cytokine and tyrosine kinase receptor signalling. In contrast to the phosphotyrosine-dependent binding of CBL substrates through its tyrosine kinase-binding domain (TKBD), CBL TKBD associates with the C-terminal tail of SLAP2 in a phospho-independent manner. To understand the distinct nature of this interaction, a purification protocol for SLAP2 in complex with CBL TKBD was established and the complex was crystallized. However, determination of the complex crystal structure was hindered by the apparent degradation of SLAP2 during the crystallization process, such that only the CBL TKBD residues could initially be modelled. Close examination of the CBL TKBD structure revealed a unique dimer interface that included two short segments of electron density of unknown origin. To elucidate which residues of SLAP2 to model into this unassigned density, a co-expression system was generated to test SLAP2 deletion mutants and define the minimal SLAP2 binding region. SLAP2 degradation products were also analysed by mass spectrometry. The model-building and map-generation features of the Phenix software package were employed, leading to successful modelling of the C-terminal tail of SLAP2 into the unassigned electron-density segments.
Assuntos
Proteínas Adaptadoras de Transdução de Sinal/química , Proteínas Proto-Oncogênicas c-cbl/química , Proteínas Adaptadoras de Transdução de Sinal/genética , Proteínas Adaptadoras de Transdução de Sinal/metabolismo , Sítios de Ligação , Cristalografia por Raios X , Elétrons , Humanos , Modelos Moleculares , Complexos Multiproteicos/química , Complexos Multiproteicos/isolamento & purificação , Complexos Multiproteicos/metabolismo , Proteínas Proto-Oncogênicas c-cbl/genética , Proteínas Proto-Oncogênicas c-cbl/metabolismoRESUMO
A method is presented that modifies a 2mFobs - DFmodel σA-weighted map such that the resulting map can strengthen a weak signal, if present, and can reduce model bias and noise. The method consists of first randomizing the starting map and filling in missing reflections using multiple methods. This is followed by restricting the map to regions with convincing density and the application of sharpening. The final map is then created by combining a series of histogram-equalized intermediate maps. In the test cases shown, the maps produced in this way are found to have increased interpretability and decreased model bias compared with the starting 2mFobs - DFmodel σA-weighted map.