Analysis of Intrinsic Peptide Detectability via Integrated Label-Free and SRM-Based Absolute Quantitative Proteomics

Quantitative mass spectrometry-based proteomics of complex biological samples remains challenging in part due to the variability and charge competition arising during electrospray ionization (ESI) of peptides and the subsequent transfer and detection of ions. These issues preclude direct quantification from signal intensity alone in the absence of a standard. A deeper understanding of the governing principles of peptide ionization and exploitation of the inherent ionization and detection parameters of individual peptides is thus of great value. Here, using the yeast proteome as a model system, we establish the concept of peptide F-factor as a measure of detectability, closely related to ionization efficiency. F-factor is calculated by normalizing peptide precursor ion intensity by absolute abundance of the parent protein. We investigated F-factor characteristics in different shotgun proteomics experiments, including across multiple ESI-based LC–MS platforms. We show that F-factors mirror previously observed physicochemical predictors as peptide detectability but demonstrate a nonlinear relationship between hydrophobicity and peptide detectability. Similarly, we use F-factors to show how peptide ion coelution adversely affects detectability and ionization. We suggest that F-factors have great utility for understanding peptide detectability and gas-phase ion chemistry in complex peptide mixtures, selection of surrogate peptides in targeted MS studies, and for calibration of peptide ion signal in label-free workflows. Data are available via ProteomeXchange with identifier PXD003472

Phylogenetic analyses of COL1A2 sequences of eutherian mammals including the extinct South American native ungulates and two extinct ground sloths <i>Lestodon</i> and <i>Megatherium</i> in comparison to extant sloths <i>Bradypus</i> and <i>Choloepus</i> showing (A) Maximum Likelihood analysis of consensus peptide matches observed in the PMF, (B) Maximum Likelihood of peptide matches observed in the PMF from either specimen, (C) Maximum Likelihood of the alpha 2 (I) sequences only, using the Ensembl <i>Choloepus</i> sequence and (D) Bayes analysis of consensus peptide matches observed in the PMF.

<p>Phylogenetic analyses of COL1A2 sequences of eutherian mammals including the extinct South American native ungulates and two extinct ground sloths <i>Lestodon</i> and <i>Megatherium</i> in comparison to extant sloths <i>Bradypus</i> and <i>Choloepus</i> showing (A) Maximum Likelihood analysis of consensus peptide matches observed in the PMF, (B) Maximum Likelihood of peptide matches observed in the PMF from either specimen, (C) Maximum Likelihood of the alpha 2 (I) sequences only, using the Ensembl <i>Choloepus</i> sequence and (D) Bayes analysis of consensus peptide matches observed in the PMF.</p

MALDI-ToF mass spectra of collagen extracted from <i>Lestodon</i> and <i>Megatherium</i> digested with trypsin. *Note the clearly observable difference in deamidation as a marker for protein ageing due to the presence of a glutamine residue in this peptide.

<p>MALDI-ToF mass spectra of collagen extracted from <i>Lestodon</i> and <i>Megatherium</i> digested with trypsin. *Note the clearly observable difference in deamidation as a marker for protein ageing due to the presence of a glutamine residue in this peptide.</p

Proteomics information relating to the quality of the Mascot search results, including the False Decoy Rate (FDR), highest scoring false positive peptide (HFPS), the total protein score for sloth collagen, the number of peptide matches used for this score, the number of unique sequences and the percentage coverage.

<p>Proteomics information relating to the quality of the Mascot search results, including the False Decoy Rate (FDR), highest scoring false positive peptide (HFPS), the total protein score for sloth collagen, the number of peptide matches used for this score, the number of unique sequences and the percentage coverage.</p

Maximum Likelihood analysis of the concatenated COL1A1 and COL1A2 sequences of eutherian mammals including the proposed consensus sequences of the two extinct ground sloths <i>Lestodon</i> and <i>Megatherium</i>.

<p>Maximum Likelihood analysis of the concatenated COL1A1 and COL1A2 sequences of eutherian mammals including the proposed consensus sequences of the two extinct ground sloths <i>Lestodon</i> and <i>Megatherium</i>.</p

Number of amino acid variations detected through LC-based methods compared with number of most intense 100 peaks shared in MALDI fingerprints (numbers in brackets indicate observations confirmed in both fossil replicates for each species).

<p>Number of amino acid variations detected through LC-based methods compared with number of most intense 100 peaks shared in MALDI fingerprints (numbers in brackets indicate observations confirmed in both fossil replicates for each species).</p

Verification of a Parkinson’s Disease Protein Signature in T‑Lymphocytes by Multiple Reaction Monitoring

Diagnosis of Parkinson’s disease, the second most common neurodegenerative disease, is based on the appearance of motor symptoms. A panel of protein biomarkers in the T-lymphocyte proteome was previously proposed as a Parkinson’s disease signature. Here, we designed an LC–MS based method to quantitatively evaluate this protein signature by multiple reaction monitoring (MRM) in T-lymphocytes and peripheral blood mononuclear cells from a new cohort of nine patients with Parkinson’s disease and nine unaffected subjects. Patients were classified using the discriminant function obtained from two-dimensional electrophoresis and protein amounts measured by MRM, thus assigning seven controls out of nine as true negatives and nine patients out of nine as true positives. A good discriminant power was obtained by selecting a subset of peptides from the protein signature, with an area under the receiver operating characteristic curve of 0.877. A similar result is achieved by evaluating all peptides of a selected panel of proteins (gelsolin, moesin, septin-6, twinfilin-2, lymphocyte-specific protein 1, vimentin, transaldolase), with an area under the curve of 0.840. Conversely, the signature was not able to classify the enrolled subjects when evaluated in whole mononuclear cells. Overall, this report shows the portability of the proposed method to a large-scale clinical validation study