NanoUPLC-MSE proteomic data assessment of soybean seeds using the Uniprot database

Abstract

BackgroundRecombinant DNA technology has been extensively employed to generate a variety of products from genetically modified organisms (GMOs) over the last decade, and the development of technologies capable of analyzing these products is crucial to understanding gene expression patterns. Liquid chromatography coupled with mass spectrometry is a powerful tool for analyzing protein contents and possible expression modifications in GMOs. Specifically, the NanoUPLC-MSE technique provides rapid protein analyses of complex mixtures with supported steps for high sample throughput, identification and quantization using low sample quantities with outstanding repeatability. Here, we present an assessment of the peptide and protein identification and quantification of soybean seed EMBRAPA BR16 cultivar contents using NanoUPLC-MSE and provide a comparison to the theoretical tryptic digestion of soybean sequences from Uniprot database.ResultsThe NanoUPLC-MSE peptide analysis resulted in 3,400 identified peptides, 58% of which were identified to have no miscleavages. The experiment revealed that 13% of the peptides underwent in-source fragmentation, and 82% of the peptides were identified with a mass measurement accuracy of less than 5 ppm. More than 75% of the identified proteins have at least 10 matched peptides, 88% of the identified proteins have greater than 30% of coverage, and 87% of the identified proteins occur in all four replicates. 78% of the identified proteins correspond to all glycinin and beta-conglycinin chains.The theoretical Uniprot peptide database has 723,749 entries, and 548,336 peptides have molecular weights of greater than 500 Da. Seed proteins represent 0.86% of the protein database entries. At the peptide level, trypsin-digested seed proteins represent only 0.3% of the theoretical Uniprot peptide database. A total of 22% of all database peptides have a pI value of less than 5, and 25% of them have a pI value between 5 and 8. Based on the detection range of typical NanoUPLC-MSE experiments, i.e., 500 to 5000 Da, 64 proteins will not be identified.ConclusionsNanoUPLC-MSE experiments provide good protein coverage within a peptide error of 5 ppm and a wide MW detection range from 500 to 5000 Da. A second digestion enzyme should be used depending on the tissue or proteins to be analyzed. In the case of seed tissue, trypsin protein digestion results offer good databank coverage. The Uniprot database has many duplicate entries that may result in false protein homolog associations when using NanoUPLC-MSE analysis. The proteomic profile of the EMBRAPA BR-16 seed lacks certain described proteins relative to the profiles of transgenic soybeans reported in other works.

Analysis ofParacoccidioideslutziimitochondria: a proteomic approach

“…The samples, in biological duplicates (70 µg), were analysed using nanoscale liquid chromatography coupled with tandem mass spectrometry (NanoUPLC‐MS E ) as previously described (Lima et al, ; Murad and Rech, ) in three experimental replicates, totalling six runs. Briefly, 50 mM ammonium bicarbonate was added to the sample, followed by addition of 25 μL of RapiGEST TM (0.2% v/v; Waters Corporation, Milford, MA, USA).…”

“…Protein identifications were performed by searching against the P. lutzii database (http://www.broadinstitute.org/annotation/genome/paracoccidioides_brasiliensis/MultiHome.html) (Desjardins et al, ). Protein and peptides tables generated by PLGS were merged and the dynamic range of the experiments, peptides detection type and mass accuracy were determined for each condition, as previously described (Murad and Rech, ).…”

Mining seed proteome: from protein dynamics to modification profiles

“…In the easiest and most straightforward way, ground plant material is extracted directly with the sample buffer, containing urea and thiourea in presence or absence of CHAPS or optionally Triton X-100 (Gallardo, Kurt, Thompson, and Ochatt, 2003;Han, Yin, He, and Yang, 2013;Gallardo, Kurt, Thompson, and Ochatt, 2018). Thereby, to reduce interference with low molecular weight metabolites, pre-extraction with petroleum ether can be employed (Murad and Rech, 2012). In the most comprehensive way, the total protein fraction can be isolated by phenol or acetone extraction (Isaacson et al, 2006).…”

NanoUPLC/MSE proteomic analysis reveals modulation on left ventricle proteome from hypertensive rats after exercise training

“…Proteins were identified using the software's embedded ion accounting algorithm and a search of the Uniprot Rattus novergicus database with MassPREP digestion standards (MPDS) UniProtKB/Swiss-Prot sequences (Phosphorylase -P00489 -PHS2_RABIT, Bovine Hemoglobin -P02070 -HBB_BOVIN, ADH -P00330 -ADH1_YEAST, BSA -P02769 -ALBU_BOVIN) appended to the database. Identifications and quantitative data packaging were generated using dedicated algorithms [41][42][43][44] and a search against a species-specific Uniprot database. The ion detection, clustering, and log-scale parametric normalizations were performed in PLGS with an Expression E license installed.…”