Improved Methods Using Mass Spectrometry to Identify Cross-links in Proteins
- 详细技术说明
- This invention provides the best known computational algorithmfor identifying cross-linked peptides using mass spectrometry.
- *Abstract
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Chemical cross-linkingexperiments with subsequent analysis by mass spectrometry is an increasinglyimportant tool in understanding protein properties. While the setup to perform cross-linkingexperiments is reasonably straightforward, it is the subsequent processing ofdata to identify cross-linked peptides where great improvements are needed ascurrent methods suffer from low rates of identifying relevant crosslinks andhigh rates of false positives.
Dr. Yu and histeam have developed an iterative algorithm (coded as MaXLinker™) thatpredicts crosslinks with higher reliability when compared to a program currentlyused in many labs.
The methodemploys integrative analysis of data from all levels of MS spectra (MS1, MS2,and MS3) to identify cross-links. Thesecross-links are then filtered through a machine learning program with stringentvalidation steps involving the most comprehensive collection of experimentallyverified protein-protein interactions across several resources to eliminate falsepositives.
Potential Applications
Improved software for processing data from cross-linkingmass spectrometry.
Advantages
- State-of-the-art algorithm and software to identifycrosslinks
- Outperforms other available methods for identifying both thequality and quantity of crosslinks
- Novel machine learning-based quality control feature reducefalse positives
- Adaptable to different MS-cleavable crosslinkers
- *Licensing
- Phillip Owhpo62@cornell.edu1-607-254-4508
- 其他
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- 国家/地区
- 美国
