In silico digestion

PMF, currently the most common method used to identify proteins in a high throughpnt environment, is based on the comparison of a list of experimental peptide masses with theoretical peptide masses. The experimental masses are generated from the MS measurement of an enzymatically digested protein sample. The theoretical masses are obtained from an in silico digestion of all sequences in a database. The goal is to find the protein(s) whose peptide masses show the best match with the experimental fingerprint. The method can be divided into 3 steps. The first step is pe detection, i.e. the selection of the most relevant masses for protein identification from the mass spectra. Frequently, only few experimental peptide... 

Indeed, based on in silico digestion of all predicted tryptic peptides between reference proteomes of B. anthracis Sterne or B. cereus ATCC 14579 and other Bacillaceae strains with sequenced genomes, Dworzanski et al. (2010) formd that theoretical FSPs were substantially lower than their experimental values. For example, for a pair of theoretical proteomes with a calculated FSP of 0.7, the experimentally determined values were found in the range 0.83-0.89. However, in the case of tryptic peptides released from surface-exposed proteins of H. pylori strains J99 and 26695, Karlsson et al. (2012) found intraspecies FSPs between these and more than 20 other/f. pylori strains to be in the range of 0.65-0.82, that is, closer to the expected theoretical value. 

We the editors send our warmest regards to all the authors who had to put up with our constant nagging and repeated demands on several rounds of revisions. To some authors, it may have caused some anxiety attacks, for which we apologise and also commend them profusely for their patience and hard work. Last, but not the least, both Carsten Ehrhardt and Kwang-Jin Kim appreciate the expert review and digestion for the in silico chapters extended by their mutual friend, Ian Haworth, Ph.D., at the University of Southern California - School of Pharmacy. Finally, we also realise that the book is a live being that constantly requires updates and mending, as the science moves forward. Therefore, we can hardly wait for the next edition for that reason. 

Moreover, we have determined the false positive rate for this approach. Many tryptic peptides originated from different proteins can be attributed to a single mass (e.g. HQHPLQCVMEK 1364.63 Da and EADFINCVIWR 1364.65 Da AM < 20 ppm). Thereby false positive identification may occur. To evaluate the false positive rate, we have selected three common proteins which were not identified during the nanoLC-MS/MS analysis. These proteins (tubulin, actin and myosin) were digested in-silico, and the generated mass lists were compared to the LC-MALDI-MS peaklist. A total of only five masses were attributed to the three... 

Protein identification in a proteome by tiyptic digestion and RPLC-MS relies on the abihty of trypsin to achieve efficient digestion of the proteins in the nuxture, the ability of RPLC to separate the resulting peptides, and deliver them to nano-ESI-MS for mass analysis. The peptides in a complex mixture widely differ in their physicochemical properties, which in turn influence both LC and nano-ESI-MS performance. Data were compared for experimentally detected and in-silico predicted peptides from three different complex protein mixtmes [38]. The peptides detected actnally form only a small subset of the peptides present. 

These protein attributes are then submitted to a database search. This search identifies a protein by looking at the best match between experimental data and data obtained by in-silico processing and digestion of a protein sequence database. The identification and characterization procedures using bioinformatics tools will be the topic of Section 4.4. 

Fig. 8. Cloning tool. A construct can be digested in silico by one or more restriction enzymes, and any of the resulting fragments dragged to assemble into a new construct.

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