RESUMO
Isotopic labeling is widely used in various fields like proteomics, metabolomics, fluxomics, as well as in NMR structural studies, but it requires an efficient determination of the isotopic enrichment. Mass spectrometry is the method of choice for such analysis. However, when complex expression systems like hairy roots are used for production, multiple populations of labeled proteins may be obtained. If the isotopic incorporation determination is actually well-known for unimodal distributions, the multimodal distributions have scarcely been investigated. Actually, only a few approaches allow the determination of the different labeled population proportions from multimodal distributions. Furthermore, they cannot be used when the number of the populations and their respective isotope ratios are unknown. The present study implements a new strategy to measure the (15)N labeled populations inside a multimodal distribution knowing only the peptide sequence and peak intensities from mass spectrometry analyses. Noteworthy, it could be applied to other elements, like carbon and hydrogen, and extended to a larger range of biomolecules.
Assuntos
Brassica rapa/química , Proteínas de Fluorescência Verde/análise , Raízes de Plantas/química , Humanos , Espectrometria de Massas , Isótopos de NitrogênioRESUMO
By virtual screening using a fragment-based drug design (FBDD) approach, 33 fragments were selected within small pockets around interaction hot spots on the Sec7 surface of the nucleotide exchange factor Arno, and then their ability to interfere with the Arno-catalyzed nucleotide exchange on the G-protein Arf1 was evaluated. By use of SPR, NMR, and fluorescence assays, the direct binding of three of the identified fragments to Arno Sec7 domain was demonstrated and the promiscuous aggregate behavior evaluated. Then the binding mode of one fragment and of a more active analogue was solved by X-ray crystallography. This highlighted the role of stable and transient pockets at the Sec7 domain surface in the discovery and binding of interfering compounds. These results provide structural information on how small organic compounds can interfere with the Arf1-Arno Sec7 domain interaction and may guide the rational drug design of competitive inhibitors of Arno enzymatic activity.