Improving Reproducibility and Sensitivity in Identifying Human Proteins by Shotgun Proteomics

Abstract

Identifying proteins in cell extracts by shotgun proteomics involves digesting the proteins, sequencing the resulting peptides by data-dependent mass spectrometry (MS/MS), and searching protein databases to identify the proteins from which the peptides are derived. Manual analysis and direct spectral comparison reveal that scores from two commonly used search programs (Sequest and Mascot) validate less than half of potentially identifiable MS/MS spectra (class positive) from shotgun analyses of the human erythroleukemia K562 cell line. Here we demonstrate increased sensitivity and accuracy using a focused search strategy along with a peptide sequence validation script that does not rely exclusively on XCorr or Mowse scores generated by Sequest or Mascot, but uses consensus between the search programs, along with chemical properties and scores describing the nature of the fragmentation spectrum (ion score and RSP). The approach yielded 4.2% false positive and 8% false negative frequencies in peptide assignments. The protein profile is then assembled from peptide assignments using a novel peptide-centric protein nomenclature that more accurately reports protein variants that contain identical peptide sequences. An Isoform Resolver algorithm ensures that the protein count is not inflated by variants in the protein database, eliminating approximately 25% of redundant proteins. Analysis of soluble proteins from a human K562 cells identified 5130 unique proteins, with approximately 100 false positive protein assignments.

Development and validation of a spectral library searching method for peptide identification from MS/MS

“…Studies have shown that different sequence search engines have different strengths and weaknesses, and often detect largely overlapping, but not identical sets of positive hits [30,44]. Since the library includes the high-confidence identifications by different sequence search engines, searching against such a library amounts to searching with multiple sequence search engines and combining the results, which is an approach championed by some to enhance the sensitivity and specificity of sequence searching methods [42,43]. In other words, spectral searching implicitly allows one to take advantage the strengths of several sequence search engines, without the added time and effort, to increase the number of positive hits.…”

Separation and identification of structural isomers by quadrupole collision-induced dissociation-hydrogen/deuterium exchange-infrared multiphoton dissociation (QCID-HDX-IRMPD)

“…Additional fragments represent the loss of leucine and a CO, additionally supporting that the m/z 417.21 fragment arises from a non-C-terminal [MH Ϫ H 2 O] ϩ ion structure; the C-terminus again must be intact for loss of the entire C-terminal residue. The dominance of the fragment at m/z 417.21 for the water loss ion is important to recognize because of the potential for error it generates for spectral prediction based on kinetic modeling [9,35]. Accounting for multiple structures would allow for the kinetics of the multiple fragmentation pathways to be modeled accurately.…”

“…While these models can help to predict many trends in peptide fragmentation, actual dissociation chemistry is much more complex, and the complete dissociation chemistry in an MS/MS spectrum cannot always be accurately predicted by using existing theoretical models. As a result, false and missed identifications frequently occur, with only a small percentage of the spectra being correctly assigned [7][8][9][10]. Incorporation of additional chemical information and fragmentation models into sequencing algorithms could depict fragmentation processes more completely, potentially allowing more accurate kinetic modeling of spectra to be possible, and the success of identification algorithms would likely improve.…”

A workflow to increase the detection rate of proteins from unsequenced organisms in high‐throughput proteomics experiments

“…The application of proteomics and related technologies for the analysis of plants is severely hampered by the lack of publicly available sequence information for most economically relevant crop plants, since only a few plant species are sequenced to date including Arabidopsis thaliana [1], rice (Oryza sativa L.) [2], and poplar (Populus trichocarpa) [3]. In order to circumvent this limitation, different strategies and tools were developed to make unsequenced organisms amenable to high-throughput proteomics [4][5][6][7][8][9][10][11]. However, an evaluation of their performance in an integrated proteomics strategy using high-throughput shotgun MS data is currently missing.…”