Cited by 513Advanced Proteomics Techniques and ApplicationsMass Spectrometry Techniques and ApplicationsGenomics and Phylogenetic Studies

ProLuCID: An improved SEQUEST-like algorithm with enhanced sensitivity and specificity

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Xu, T., Park, S.K., Venable, J.D., Wohlschlegel, J.A., Diedrich, J.K., Cociorva, D., Lu, B., Liao, L. +8 more·2015·Journal of Proteomics

Abstract

ProLuCID, a new algorithm for peptide identification using tandem mass spectrometry and protein sequence databases has been developed. This algorithm uses a three tier scoring scheme. First, a binomial probability is used as a preliminary scoring scheme to select candidate peptides. The binomial probability scores generated by ProLuCID minimize molecular weight bias and are independent of database size. A modified cross-correlation score is calculated for each candidate peptide identified by the binomial probability. This cross-correlation scoring function models the isotopic distributions of fragment ions of candidate peptides which ultimately results in higher sensitivity and specificity than that obtained with the SEQUEST XCorr. Finally, ProLuCID uses the distribution of XCorr values for all of the selected candidate peptides to compute a Z score for the peptide hit with the highest XCorr. The ProLuCID Z score combines the discriminative power of XCorr and DeltaCN, the standard parameters for assessing the quality of the peptide identification using SEQUEST, and displays significant improvement in specificity over ProLuCID XCorr alone. ProLuCID is also able to take advantage of high resolution MS/MS spectra leading to further improvements in specificity when compared to low resolution tandem MS data. A comparison of filtered data searched with SEQUEST and ProLuCID using the same false discovery rate as estimated by a target-decoy database strategy, shows that ProLuCID was able to identify as many as 25% more proteins than SEQUEST. ProLuCID is implemented in Java and can be easily installed on a single computer or a computer cluster. This article is part of a Special Issue entitled: Computational Proteomics.

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Authors
Xu, T., Park, S. K., Venable, J. D., Wohlschlegel, J. A., Diedrich, J. K., Cociorva, D., Lu, B., Liao, L., Hewel, J., Han, X., Wong, C. C. L., Fonslow, B., Delahunty, C., Gao, Y., Shah, H., & Yates, J. R.
Affiliations
Scripps Research Institute (US)
Corresponding author
Yates, J.R.
Venue
Journal of Proteomics
Publisher
Elsevier BV
Published
2015-11
Volume
129
Pages
16-24
ISSN
1874-3919 (ISSN-L)
OpenAlex
W2182963873
Cited by
511
References
51
Citations by year
2026: 23 · 2025: 41 · 2024: 65 · 2023: 45 · 2022: 51 · 2021: 56 · 2020: 56 · 2019: 60 · 2018: 43 · 2017: 46 · 2016: 22 · 2015: 3
FWCI
16.92
Type
journal article
Language
en
Open access
Locations
  • Journal of Proteomics (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
Funders
  • NIH (5U01 DE016267-04, 5R01 HL079442-04, R01 MH067880, P41 GM103533)
  • NHLBI (HHSN268201000035C)
Topics
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
  • Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
peptide identification, tandem mass spectrometry, SEQUEST, Z-score
MeSH terms
Algorithms, Amino Acid Sequence, Molecular Sequence Data, Pattern Recognition, Automated, Peptide Mapping, Proteins, Sensitivity and Specificity, Software, Reproducibility of Results, Sequence Analysis, Protein, Databases, Protein, Tandem Mass Spectrometry, Data Mining

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