Addressing the Protease Bias in Quantitative Proteomics.

Protein quantification strategies using multiple proteases have been shown to deliver poor interprotease accuracy in label-free mass spectrometry experiments. By utilizing six different proteases with different cleavage sites, this study explores the protease bias and its effect on accuracy and precision by using recombinant protein standards. We established 557 SRM assays, using a recombinant protein standard resource, toward 10 proteins in human plasma and determined their concentration with multiple proteases. The quantified peptides of these plasma proteins spanned 3 orders of magnitude (0.02-70 µM). In total, 60 peptides were used for absolute quantification and the majority of the peptides showed high robustness. The retained reproducibility was achieved by quantifying plasma proteins using spiked stable isotope standard recombinant proteins in a targeted proteomics workflow.

ProteaseGuru: A Tool for Protease Selection in Bottom-Up Proteomics.

Bottom-up proteomics is currently the dominant strategy for proteome analysis. It relies critically upon the use of a protease to digest proteins into peptides, which are then identified by liquid chromatography-mass spectrometry (LC-MS). The choice of protease(s) has a substantial impact upon the utility of the bottom-up results obtained. Protease selection determines the nature of the peptides produced, which in turn affects the ability to infer the presence and quantities of the parent proteins and post-translational modifications in the sample. We present here the software tool ProteaseGuru, which provides in silico digestions by candidate proteases, allowing evaluation of their utility for bottom-up proteomic experiments. This information is useful for both studies focused on a single or small number of proteins, and for analysis of entire complex proteomes. ProteaseGuru provides a convenient user interface, valuable peptide information, and data visualizations enabling the comparison of digestion results of different proteases. The information provided includes data tables of theoretical peptide sequences and their biophysical properties, results summaries outlining the numbers of shared and unique peptides per protease, histograms facilitating the comparison of proteome-wide proteolytic data, protein-specific summaries, and sequence coverage maps. Examples are provided of its use to inform analysis of variant-containing proteins in the human proteome, as well as for studies requiring the use of multiple proteomic databases such as a human:mouse xenograft model, and microbiome metaproteomics.

Improved Protein Inference from Multiple Protease Bottom-Up Mass Spectrometry Data.

Peptides detected by tandem mass spectrometry (MS/MS) in bottom-up proteomics serve as proxies for the proteins expressed in the sample. Protein inference is a process routinely applied to these peptides to generate a plausible list of candidate protein identifications. The use of multiple proteases for parallel protein digestions expands sequence coverage, provides additional peptide identifications, and increases the probability of identifying peptides that are unique to a single protein, which are all valuable for protein inference. We have developed and implemented a multi-protease protein inference algorithm in MetaMorpheus, a bottom-up search software program, which incorporates the calculation of protease-specific q-values and preserves the association of peptide sequences and their protease of origin. This integrated multi-protease protein inference algorithm provides more accurate results than either the aggregation of results from the separate analysis of the peptide identifications produced by each protease (separate approach) in MetaMorpheus, or results that are obtained using Fido, ProteinProphet, or DTASelect2. MetaMorpheus' integrated multi-protease data analysis decreases the ambiguity of the protein group list, reduces the frequency of erroneous identifications, and increases the number of post-translational modifications identified, while combining multi-protease search and protein inference into a single software program.

Preparation of polymer brushes grafted graphene oxide by atom transfer radical polymerization as a new support for trypsin immobilization and efficient proteome digestion.

Highly efficient protein digestion is one of the key issues in the "bottom-up" strategy-based proteomic studies. Compared with the time-consuming solution-based free protease digestion, immobilized protease digestion offers a promising alternative with obviously improved sample processing throughput. In this study, we proposed a new immobilized protease digestion strategy using two kinds of polymer-grafted graphene oxide (GO) conjugated trypsin. The polymer brush grafted GO was prepared using in situ polymer growth on initiator-functionalized GO using surface-initiated atom transfer radical polymerization (SI-ATRP) and characterized by AFM, TEM, TGA, and XPS. The polymer brush grafted GO supports three-dimensional trypsin immobilization, which not only increases the loading amount but also improves accessibility towards protein substrates. Both of the two types of immobilized trypsin provide 700 times shorter digestion time, while maintaining comparable protein/peptide identification scale compared with that of free trypsin digestion. More interestingly, combined application of the two types of immobilized trypsin with different surface-grafted polymers leads to at least 18.3/31.3% enhancement in protein/peptide identification compared with that obtained by digestion using a single type, indicating the potential of this digestion strategy for deeper proteome coverage using limited mass spectrometer machine hour. We expect these advantages may find valuable application in high throughput clinical proteomic studies, which often involve processing of a large number of samples. Graphical abstract Preparation of polymer brushes grafted and trypsin immobilized graphene oxide and its application in proteome digestion and mass spectrometry identification.

Full-Featured Search Algorithm for Negative Electron-Transfer Dissociation.

Negative electron-transfer dissociation (NETD) has emerged as a premier tool for peptide anion analysis, offering access to acidic post-translational modifications and regions of the proteome that are intractable with traditional positive-mode approaches. Whole-proteome scale characterization is now possible with NETD, but proper informatic tools are needed to capitalize on advances in instrumentation. Currently only one database search algorithm (OMSSA) can process NETD data. Here we implement NETD search capabilities into the Byonic platform to improve the sensitivity of negative-mode data analyses, and we benchmark these improvements using 90 min LC-MS/MS analyses of tryptic peptides from human embryonic stem cells. With this new algorithm for searching NETD data, we improved the number of successfully identified spectra by as much as 80% and identified 8665 unique peptides, 24 639 peptide spectral matches, and 1338 proteins in activated-ion NETD analyses, more than doubling identifications from previous negative-mode characterizations of the human proteome. Furthermore, we reanalyzed our recently published large-scale, multienzyme negative-mode yeast proteome data, improving peptide and peptide spectral match identifications and considerably increasing protein sequence coverage. In all, we show that new informatics tools, in combination with recent advances in data acquisition, can significantly improve proteome characterization in negative-mode approaches.

Isolation, identification and synthesis of four novel antioxidant peptides from rice residue protein hydrolyzed by multiple proteases.

Multiple proteases were optimized to hydrolyze the rice residue protein (RRP) to produce novel antioxidant peptides. An antioxidant peptide fraction (RRPB3) with IC50 of 0.25 mg/ml was purified from the RRP hydrolysate using membrane ultrafiltration followed by size exclusion chromatography and reversed-phase FPLC. RRPB3 was found to include four peptides (RRPB3 I-IV) and their amino acid sequences were RPNYTDA (835.9 Da), TSQLLSDQ (891.0 Da), TRTGDPFF (940.0 Da) and NFHPQ (641.7 Da), respectively. Furthermore, four peptides were chemically synthesized and their antioxidant activities were assessed by DPPH radical scavenging, ABTS radical scavenging assay and FRAP-Fe(3+) reducing assay, respectively. Both RRPB3 I and III showed synergistic antioxidant activity compared to each of them used alone. All four synthetic peptides showed excellent stability against simulated gastrointestinal proteases. Therefore, the peptides isolated from RRP may be used as potential antioxidants in the food and drug industries.