Ultra-high mass multimer analysis of protein-1a capping domains by a silicon nanomembrane detector.

Conventional time of flight ion detectors are based on secondary electron multipliers encountering a significant loss in detection efficiency, sensitivity and resolution with protein mass above 50kDa. In this work we employ a silicon nanomembrane detector in a Matrix-Assisted Laser Desorption/Ionization coupled to time of flight (MALDI-TOF) mass spectrometer. The operating principle relies on phonon-assisted field emission with excellent performance in the high mass range from 0.001-2MDa. In addition to the analysis of standard proteins the nanomembrane detector (NMD) has the potential for the detection and structural investigation of complex macromolecular assemblies through non-covalent interactions. In order to investigate this hypothesis, the N-terminal capping/methyltransferase domain (CAP) of the Brome Mosaic Virus (BMV) 1a replication protein by MALDI-TOF-NMD is analyzed. The signals detected at the high m/z-ratios of 912.6/982.7 (×103) and 1333.3 (×103) could be modified species of CAP-tricta/tetractamer and the octadecamer. For the first time, the NMD is applied to detect biologically complex macromolecular protein assemblies. Hence, this technology overcomes the limitations of conventional TOF-detectors and increases the analytical range of MALDI-TOF. This technology will be a future alternative for the structural analysis of intact virus capsids that will complement other MS-based techniques such as native mass spectrometry.

Untargeted metabolomic analysis using liquid chromatography quadrupole time-of-flight mass spectrometry for non-volatile profiling of wines.

The current study presents a method for comprehensive untargeted metabolomic fingerprinting of the non-volatile profile of the Graciano Vitis vinifera wine variety, using liquid chromatography/electrospray ionization time of flight mass spectrometry (LC-ESI-QTOF). Pre-treatment of samples, chromatographic columns, mobile phases, elution gradients and ionization sources, were evaluated for the extraction of the maximum number of metabolites in red wine. Putative compounds were extracted from the raw data using the extraction algorithm, molecular feature extractor (MFE). For the metabolite identification the WinMet database was designed based on electronic databases and literature research and includes only the putative metabolites reported to be present in oenological matrices. The results from WinMet were compared with those in the METLIN database to evaluate how much the databases overlap for performing identifications. The reproducibility of the analysis was assessed using manual processing following replicate injections of Vitis vinifera cv. Graciano wine spiked with external standards. In the present work, 411 different metabolites in Graciano Vitis vinifera red wine were identified, including primary wine metabolites such as sugars (4%), amino acids (23%), biogenic amines (4%), fatty acids (2%), and organic acids (32%) and secondary metabolites such as phenols (27%) and esters (8%). Significant differences between varieties Tempranillo and Graciano were related to the presence of fifteen specific compounds.