title: High-accuracy peptide mass fingerprinting using peak intensity data with machine learning.
abstract: For MALDI-TOF mass spectrometry, we show that the intensity of a peptide-ion peak is directly correlated with its sequence, with the residues M, H, P, R, and L having the most substantial effect on ionization. We developed a machine learning approach that exploits this relationship to significantly improve peptide mass fingerprint (PMF) accuracy based on training data sets from both true-positive and false-positive PMF searches. The model's cross-validated accuracy in distinguishing real versus false-positive database search results is 91%, rivaling the accuracy of MS/MS-based protein identification.
authors.affiliation: Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.
authors.forename: Dongmei
authors.initials: D
authors.lastname: Yang
authors.forename: Kevin
authors.initials: K
authors.lastname: Ramkissoon
authors.forename: Eric
authors.initials: E
authors.lastname: Hamlett
authors.forename: Morgan C
authors.initials: MC
authors.lastname: Giddings
mesh_terms: D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D010449:Peptide Mapping / Q000379:methods*; D011506:Proteins / Q000032:analysis; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; D053719:Tandem Mass Spectrometry / Q000379:methods*
chemical_list: D011506:Proteins