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Information extraction from native mass spectra
Author(s) -
Guan Shenheng
Publication year - 2016
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.30.1_supplement.819.14
Subject(s) - deconvolution , computer science , biological system , spectral line , noise (video) , inverse , algorithm , function (biology) , mass spectrum , chemistry , pattern recognition (psychology) , artificial intelligence , mass spectrometry , physics , mathematics , chromatography , biology , geometry , astronomy , evolutionary biology , image (mathematics)
Native MS spectra of large protein assemblies are typically characterized by low signal‐to‐noise ratios and broad peak shapes. Accurate peak detection is challenging due to additional issues such as high degrees of peak overlap and highly interleaved charge envelopes. A comprehensive algorithm has been developed to address peak identification, overlapping deconvolution, and charge state assignment in native mass spectra. Our open source software provides a novel and practical tool for those problems and is capable of extracting signals of large protein assemblies from heterogeneous, complex, and noisy native mass spectra. Nonspecifical binding of ligands to large protein complexes is commonly observed in native MS spectra. Here we also present a deconvolution model to discriminate specific bindings of a ligand to a multi‐protein complex target from the nonspecific interactions. Using the deconvolution algorithms with two level modeling steps, specific binding constant(s) can be effectively separated from the nonspecific ones. The nonspecific binding can be modeled by a simple power inverse function. An additional advantage of the method is that the target concentration can be more accurately obtained. Support or Funding Information This work was supported by grants from the National Institutes of Health (1S10OD016229, 8P41GM103481, GM49985, GM36659, AI‐21144, NS059690, GM109896, AG021601, AG002132, and AG010770).