Algorithm for accurate similarity measurements of peptide mass fingerprints and its application

Monigatti, Flavio; Berndt, Peter

doi:10.1016/j.jasms.2004.09.013

Cited by 28 publications

(31 citation statements)

References 22 publications

(32 reference statements)

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“…Similarly, the presence of aspartic acid at P1¢ and P2¢ appears to discourage efficient trypsin cleavage, and also has an influence N-terminal to the tryptic site at P2. These observations are consistent with previously reported studies (Monigatti and Berndt, 2005;Thiede et al, 2000;Yen et al, 2006), which note the difficulty in observing efficient cleavage at dibasic sites (KK, KR, RR, and RK), and sites where there is potential to form a salt bridge between acidic side chains and the basic site at P1.…”

Section: Amino Acid Propensitiessupporting

confidence: 93%

Prediction of Missed Proteolytic Cleavages for the Selection of Surrogate Peptides for Quantitative Proteomics

Lawless

Hubbard

2012

OMICS: A Journal of Integrative Biology

105

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Quantitative proteomics experiments are usually performed using proteolytic peptides as surrogates for their parent proteins, inferring protein amounts from peptide-level quantitation. This process is frequently dependent on complete digestion of the parent protein to its limit peptides so that their signal is truly representative. Unfortunately, proteolysis is often incomplete, and missed cleavage peptides are frequently produced that are unlikely to be optimal surrogates for quantitation, particularly for label-mediated approaches seeking to derive absolute values. We have generated a predictive computational tool that is able to predict which candidate proteolytic peptide bonds are likely to be missed by the standard enzyme trypsin. Our cross-validated prediction tool uses support vector machines and achieves high accuracy in excess of 0.94 precision (PPV), with attendant high sensitivity of 0.79, across multiple proteomes. We believe this is a useful tool for selecting candidate quantotypic peptides, seeking to minimize likely loss owing to missed cleavage, which will be a boon for quantitative proteomic pipelines as well as other areas of proteomics. Our results are discussed in the context of recent results examining the kinetics of missed cleavages in proteomic digestion protocols, and show agreement with observed experimental trends. The software has been made available at http://king.smith.man.ac.uk/mcpred.

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Section: Amino Acid Propensitiessupporting

confidence: 93%

Prediction of Missed Proteolytic Cleavages for the Selection of Surrogate Peptides for Quantitative Proteomics

Lawless

Hubbard

2012

OMICS: A Journal of Integrative Biology

105

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“…For example, peptide DDQSIQK has an adjacent Phe residue, known to promote missed cleavages, in the native protein as well as the QconCAT resulting in similar digestion efficiencies and equal peptide release. 24 In the QconCAT, IAVSYQTK has an acidic Glu residue proximal to a tryptic cleavage site which is known to promote missed cleavages. 25 The cleavage sites in the full length protein are surrounded by neutral residues, not known to influence trypsin digestion efficiency, explaining the decreased yield observed for this peptide from the QconCAT.…”

Section: Digestion Condition Optimizationmentioning

confidence: 99%

Quantitative Performance of Internal Standard Platforms for Absolute Protein Quantification Using Multiple Reaction Monitoring-Mass Spectrometry

2015

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Stable-isotope-labeling mass spectrometry involves the addition of known quantities of stable-isotope labeled standards, which mimic native molecules, to biological samples. We evaluated three conventional internal standard platforms (synthetic peptides, QconCAT constructs, and recombinant proteins) for quantitative accuracy, precision, and inherent advantages and limitations. Internal standards for the absolute quantification of three human cytokine proteins (interferon gamma, interleukin-1 beta, and tumor necrosis factor alpha) were designed and verified. Multiple reaction monitoring assays, calibration curve construction, and regression analysis were used to assess quantitative performance of the internal standard platforms. We also investigated a strategy for methodological improvement to current platforms using natural flanking sequences. Data analysis revealed that full length protein standards have the broadest quantitative reliability with accuracy being peptide-dependent for QconCATs and synthetic peptides. Natural flanking sequences greatly improved the quantitative performance of both QconCAT and synthetic peptide standards.

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“…Additional parameters such as pI, molecular mass, post-modification, and chemical composition (7), methods such as a proteotypic peptide library (8) and integrated information (9) have been taken into account. The introduction of new parameters, such as spectrum similarity (10), mass spectra alignment (11), peak bagging (12), negative ionization (13), probability-based scoring function (14), peak intensity prediction (15,16), mass accuracy (17,18), mass tolerance (19), and a validation system (20) has improved the accuracy of PMF identification.…”

mentioning

confidence: 99%

Feature-matching Pattern-based Support Vector Machines for Robust Peptide Mass Fingerprinting

Hao

Zhang

et al. 2011

Molecular & Cellular Proteomics

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Peptide mass fingerprinting, regardless of becoming complementary to tandem mass spectrometry for protein identification, is still the subject of in-depth study because of its higher sample throughput, higher level of specificity for single peptides and lower level of sensitivity to unexpected post-translational modifications compared with tandem mass spectrometry. In this study, we propose, implement and evaluate a uniform approach using support vector machines to incorporate individual concepts and conclusions for accurate PMF.We focus on the inherent attributes and critical issues of the theoretical spectrum (peptides), the experimental spectrum (peaks) and spectrum (masses) alignment. Eighty-one feature-matching patterns derived from cleavage type, uniqueness and variable masses of theoretical peptides together with the intensity rank of experimental peaks were proposed to characterize the matching profile of the peptide mass fingerprinting procedure. We developed a new strategy including the participation of matched peak intensity redistribution to handle shared peak intensities and 440 parameters were generated to digitalize each feature-matching pattern. A high performance for an evaluation data set of 137 items was finally achieved by the optimal multi-criteria support vector machines approach, with 491 final features out of a feature vector of 35,640 normalized features through cross training and validating a publicly available "gold standard" peptide mass fingerprinting data set of 1733 items. Compared with the Mascot, MS-Fit, ProFound and Aldente algorithms commonly used for MS-based protein identification, the feature-matching patterns algorithm has a greater ability to clearly separate correct identifications and random matches with the highest values for sensitivity (82%), precision (97%) and F1-measure (89%) of protein identification.Several conclusions reached via this research make general contributions to MS-based protein identification. Firstly, inherent attributes showed comparable or even greater robustness than other explicit. As an inherent attribute of an experimental spectrum, peak intensity should receive considerable attention during protein identification. Secondly, alignment between intense experimental peaks and properly digested, unique or non-modified theoretical peptides is very likely to occur in positive peptide mass fingerprinting. Finally, normalization by several types of harmonic factors, including missed cleavages and mass modification, can make important contributions to the performance of the procedure. Molecular

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Algorithm for accurate similarity measurements of peptide mass fingerprints and its application

Cited by 28 publications

References 22 publications

Prediction of Missed Proteolytic Cleavages for the Selection of Surrogate Peptides for Quantitative Proteomics

Prediction of Missed Proteolytic Cleavages for the Selection of Surrogate Peptides for Quantitative Proteomics

Quantitative Performance of Internal Standard Platforms for Absolute Protein Quantification Using Multiple Reaction Monitoring-Mass Spectrometry

Feature-matching Pattern-based Support Vector Machines for Robust Peptide Mass Fingerprinting

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