SAINT-MS1: protein-protein interaction scoring using label-free intensity data in affinity purification-mass spectrometry experiments
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We present a statistical method SAINT-MS1 for scoring protein-protein interactions based on the label-free MS1 intensity data from affinity purification-mass spectrometry (AP-MS) experiments. The method is an extension of Significance Analysis of INTeractome (SAINT), a model-based method previously developed for spectral count data. We reformulated the statistical model for log-transformed intensity data, including adequate treatment of missing observations, that is, interactions identified in some but not all replicate purifications. We demonstrate the performance of SAINT-MS1 using two recently published data sets: a small LTQ-Orbitrap data set with three replicate purifications of single human bait protein and control purifications and a larger drosophila data set targeting insulin receptor/target of rapamycin signaling pathway generated using an LTQ-FT instrument. Using the drosophila data set, we also compare and discuss the performance of SAINT analysis based on spectral count and MS1 intensity data in terms of the recovery of orthologous and literature-curated interactions. Given rapid advances in high mass accuracy instrumentation and intensity-based label-free quantification software, we expect that SAINT-MS1 will become a useful tool allowing improved detection of protein interactions in label-free AP-MS data, especially in the low abundance range.
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Many label-free quantification methods are tailored toward a specific biological question and are not suitable as generic tools for quantification at a proteome scale.
“Also, many methods are tailored toward a specific biological question, such as the detection of protein interactions (37), and are therefore not suitable as generic tools for quantification at a proteome scale.”
From Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ (2014) · cites this paper as [37]
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- Authors
- Choi, H., Glatter, T., Gstaiger, M., & Nesvizhskii, A. I.
- Affiliations
- National University of Singapore (SG)
- Institute for Molecular Systems Biology (CH)
- ETH Zurich (CH)
- University of Michigan (US)
- Corresponding author
- Nesvizhskii, Alexey I.
- Venue
- Journal of Proteome Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-03-02
- Volume
- 11
- Issue
- 4
- Pages
- 2619-2624
- ISSN
- 1535-3893 (ISSN-L)
- 1535-3907
- PMC
- PMC3744231
- OpenAlex
- W2078077201
- Cited by
- 68
- References
- 37
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 2 · 2023: 5 · 2022: 3 · 2021: 4 · 2020: 2 · 2019: 3 · 2018: 1 · 2017: 4 · 2016: 8 · 2015: 8 · 2014: 11 · 2013: 3 · 2012: 9
- FWCI
- 4.38
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Journal of Proteome Research (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- National University of Singapore (submittedVersion)
- Funders
- National Institute of General Medical Sciences (R01GM094231)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Keywords
- saints, affinity purification mass spectrometry, missing data, insulin receptor
- MeSH terms
- Animals, Chromatography, Affinity, Drosophila, Humans, Proteins, Mass Spectrometry, Logistic Models, Protein Interaction Mapping, Databases, Protein, Proteomics
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