ebi is an outstation of the european molecular biology laboratory. in silico analysis of accurate...
TRANSCRIPT
EBI is an Outstation of the European Molecular Biology Laboratory.
In silico analysis of accurate proteomics, complemented by selective isolation of peptides.
Yasset [email protected]
Aniel Sanchez [email protected]
ABSTRACTProtein 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%.
Drosophila cell
20 30 4010
Abs
at
215
nm
(0)
(1+)
(2+, 3+)
RH0
RH1
RH2
0
100
%
0
100
%
0
100
%
764.974
764.320292.163 719.299
827.868
251.126 702.367
828.373
828.889
110.025292.138
1762.4841637.7611577.7391450.5011333.7781238.5041140.3881021.131
y
n
-
1
b
1
b
1
b
1
373RPEGENASYHLAYDKDR389
221DSSIVTHDNDIFR233
373RPEGENASYHLAYDK389
~~~~ K
~~~~R
~~H~~K
~~H~~R
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.
Anal Chem. 2010 Oct 15;82(20):8492-501.
Proteomic Research: N-Term Identification
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 ofExperimental sequences on
Insilco Database.
Peptides out of the ppm range
[Not match any sequence]
Search in the input theoreticalList of sequence the peptide
By current property (pI, rt, MW, N-term)
[Match with more than one sequence]
Peptides out of the error range for
property
[Not match any sequence]
Compare with MS/MS sequence result
[Match only one sequence]
Annotate as PeptideIdentification
[Match Insilco sequence with MS/MS sequence]
Annotate as a False Positive Identification.
[Not Match Insilco sequence with MS/MS sequence]
[Match only one sequence]
[Match with more than one sequence]
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.
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