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Register a new userImproved mass spectrometry compatibility is afforded by ammoniacal silver staining
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Sequence coverage in MS analysis of protein digestion-derived peptides is a key issue for detailed characterization of proteins or identification at low quantities. In gel-based proteomics studies, the sequence coverage greatly depends on the protein detection method. It is shown here that ammoniacal silver detection methods offer improved sequence coverage over standard silver nitrate methods, while keeping the high sensitivity of silver staining. With the development of 2D-PAGE-based proteomics, another burden is placed on the detection methods used for protein detection on 2-D-gels. Besides the classical requirements of linearity, sensitivity, and homogeneity from one protein to another, detection methods must now take into account another aspect, namely their compatibility with MS. This compatibility is evidenced by two different and complementary aspects, which are (i) the absence of adducts and artefactual modifications on the peptides obtained after protease digestion of a protein detected and digested in - gel, and (ii) the quantitative yield of peptides recovered after digestion and analyzed by the mass spectrometer. While this quantitative yield is not very important per se, it is however a crucial parameter as it strongly influences the S/N of the mass spectrum and thus the number of peptides that can be detected from a given protein input, especially at low protein amounts. This influences in turn the sequence coverage and thus the detail of the analysis provided by the mass spectrometer.
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The mechanism by which silver staining of proteins in polyacrylamide gels interferes with mass spectrometry of peptides produced by proteolysis has been investigated. It was demonstrated that this interference increases with time between silver staining and gel processing, although the silver image is constant. This suggested an important role of the formaldehyde used in silver staining development in this interference process. Consequently, a formaldehyde-free staining protocol has been devised, using carbohydrazide as the developing agent. This protocol showed much increased peptide coverage and retained the sensitivity of silver staining. These results were however obtained at the expense of an increased background in the stained gels and of a reduced staining homogeneity.
Zhou et al. reported the discovery of RmYN02, a strain closely related to SARS-CoV-2, which is claimed to contain a natural PAA amino acid insertion at the S1/S2 junction of the spike protein at the same position of the PRRA insertion that has created a polybasic furin cleavage site in SARS-CoV-2. The authors support with their findings the theory that the furin cleavage site insertion present in SARS-CoV-2 is natural. Because no nucleotide alignment with closely related strains of the region coding for the supposed insertion is provided by Zhou et al., we have applied several alignment algorithms to search for the most parsimonious alignments. We conclude that RmYN02 does not contain an insertion at the S1/S2 junction when compared to its closest relatives at the nucleotide level, but rather a 6-nucleotide deletion and that the claimed PAA insertion is more likely to be the result of mutations. A close examination of RmYN02 sequencing records and assembly methods is wishful. In conclusion, SARS-CoV-2, with its 12-nucleotide insertion at the S1/S2 junction remains unique among its sarbecovirus relatives.
Motivation: Assigning statistical significance accurately has become increasingly important as meta data of many types, often assembled in hierarchies, are constructed and combined for further biological analyses. Statistical inaccuracy of meta data at any level may propagate to downstream analyses, undermining the validity of scientific conclusions thus drawn. From the perspective of mass spectrometry based proteomics, even though accurate statistics for peptide identification can now be achieved, accurate protein level statistics remain challenging. Results: We have constructed a protein ID method that combines peptide evidences of a candidate protein based on a rigorous formula derived earlier; in this formula the database $P$-value of every peptide is weighted, prior to the final combination, according to the number of proteins it maps to. We have also shown that this protein ID method provides accurate protein level $E$-value, eliminating the need of using empirical post-processing methods for type-I error control. Using a known protein mixture, we find that this protein ID method, when combined with the Soric formula, yields accurate values for the proportion of false discoveries. In terms of retrieval efficacy, the results from our method are comparable with other methods tested. Availability: The source code, implemented in C++ on a linux system, is available for download at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbp/qmbp_ms/RAId/RAId_Linux_64Bit
Summary: In anticipation of the individualized proteomics era and the need to integrate knowledge from disease studies, we have augmented our peptide identification software RAId DbS to take into account annotated single amino acid polymorphisms, post-translational modifications, and their documented disease associations while analyzing a tandem mass spectrum. To facilitate new discoveries, RAId DbS allows users to conduct searches permitting novel polymorphisms. Availability: The webserver link is http://www.ncbi.nlm.nih.gov/ /CBBResearch/qmbp/raid dbs/index.html. The relevant databases and binaries of RAId DbS for Linux, Windows, and Mac OS X are available from the same web page. Contact: [email protected]
In protein-protein interaction networks certain topological properties appear to be recurrent: networks maps are considered scale-free. It is possible that this topology is reflected in the protein structure. In this paper we investigate the role of protein disorder in the network topology. We find that the disorder of a protein (or of its neighbors) is independent of its number of protein-protein interactions. This result suggests that protein disorder does not play a role in the scale-free architecture of protein networks.
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