In silico analysis of accurate proteomics, complemented by selective isolation of peptides.

1. In silico analysis of accurate proteomics, complemented by selective isolation of peptides. Yasset Perez-Riverol yperez@ebi.ac.uk yasset.perez@biocomp.cigb.edu.cu Aniel Sanchez Puentes aniel.sanchez@cigb.edu.cu

2. Protein identification by mass spectrometry is mainly based on MS/MS spectra and the accuracy of molecular mass determination. However, the high complexity and dynamic ranges for any species of proteomic samples, surpasses the separation capacity and detection power of the most advanced multidimensional liquid chromatographs and mass spectrometers. Only a tiny portion of signals is selected for MS/MS experiments and a still considerable number of them do not provide reliable peptide identification. The approach is based on mass accuracy, isoelectric point (pI), retention time (tR) and N-terminal amino acid determination as protein identification criteria regardless of high quality MS/MS spectra. When the methodology was combined with the selective isolation methods, the number of unique peptides and identified proteins increases. Finally, to demonstrate the feasibility of the methodology, an OFFGEL-LC-MS/MS experiment was also implemented. Our results show that using the information provided by these features and selective isolation methods we could found the 93% of the high confidence protein identified by MS/MS with false-positive rate lower than 5%. Abstract

3. Drosophila cell ~~~~ K MS/MS spectra were interpreted by the X! Tandem software using the Flybase sequence database. The database search results were validated using PeptideProphet. This work analyzed only the four isoelectric focusing fractions with the lowest pI having the best agreement between the theoretical and experimental values, according to previous. In addition, these fractions cover 50% of the identified peptides. Also, we used only highly reliable peptide identifications, filtering out those with a PeptideProphet probability lower than 0.97 (FDR = 0.01) or with posttranslational modifications. For experimental tR analysis the acceptance error was set at 748.42 s, and mass tolerance was set at 10 ppm. ~~~~R ~~H~~K 764.974 ~~H~~R 100 b1 373RPEGENASYHLAYDKDR389 764.320 % 292.163 719.299 0 827.868 100 221DSSIVTHDNDIFR233 b1 yn-1 828.373 % 828.889 251.126 702.367 RH0 RH1 RH2 0 b1 373RPEGENASYHLAYDK389 110.025 100 (0) 292.138 % 1021.131 1140.388 1238.504 1333.778 1450.501 1577.739 Abs at 215 nm 1637.761 1762.484 (1+) 0 (2+, 3+) 30 40 20 10

5. Proteomic Research: N-Term Identification Anal Chem. 2010 Oct 15;82(20):8492-501.

6. Annotate peptides with theoretical rt, pI, mass, MW, N-Term Create a Insilico tryptic peptide database. Annotate experimental identified sequences from PeptideProphet output with probability more than 0.97. (rt, pI, N-term, MW, sequence) Search precursor masses of Experimental sequences on Insilco Database. Peptides out of the ppm range [Match only one sequence] [Not match any sequence] [Match with more than one sequence] Search in the input theoretical List of sequence the peptide By current property (pI, rt, MW, N-term) Peptides out of the error range for property [Not match any sequence] [Match with more than one sequence] [Match only one sequence] Compare with MS/MS sequence result [Not Match Insilco sequence with MS/MS sequence] [Match Insilco sequence with MS/MS sequence] Annotate as a False Positive Identification. Annotate as Peptide Identification A tree-based algorithm to identify unique peptides in the experimental set was constructed in a similar fashion to the one designed for theoretical analysis. The final list of unique peptides was validated by using the sequence predicted from PeptideProphet. In cases where the PeptideProphet sequences and the sequences identified by our approach did not match, the identifications achieved by our algorithm were considered as false positive identification.

8. The use of the information provided by some analytical tools could help to offset the information contained in the sequence of peptides, but it is more efficient when a prokaryote proteome is analyzed. Some drawbacks associated to precision (accuracy) that can be predicted are that the variables used may hinder the accurate mass proteomics analysis with the identification of false positive hints. The inclusion of some types of peptides and the reduction of complexity allows increasing the percent of unique peptides compared to normal analysis. The combination of several selective methods (RH0, RH1, and RH2) in the same sample could increase the percent of proteins with unique peptides. The theoretical analysis described in this paper does not exclude the possibility of combining it with the MS/MS information obtained in any proteomic experiment. Conclusion