Open AccessPeptide sequence determination from high-energy collision-induced dissociation spectra using artificial neural networks
Author(s) -
Randall E. Scarberry,
Zhen Zhang,
Daniel R. Knapp
Publication year - 1995
Publication title -
journal of the american society for mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.961
H-Index - 127
eISSN - 1879-1123
pISSN - 1044-0305
DOI - 10.1016/1044-0305(95)00477-u
Subject(s) - chemistry , dissociation (chemistry) , artificial neural network , collision , peptide fragment , peptide , collision induced dissociation , spectral line , sequence (biology) , analytical chemistry (journal) , mass spectrometry , computational chemistry , chromatography , artificial intelligence , tandem mass spectrometry , biochemistry , computer security , astronomy , computer science , physics
This paper reports a newly developed technique that uses artificial neural networks to aid in the automated interpretation of peptide sequence from high-energy collision-induced dissociation (CID) tandem mass spectra of peptides. Two artificial neural networks classify fragment ions before the commencement of an iterative sequencing algorithm. The first neural network provides an estimation of whether fragment ions belong to 1 of 11 specific categories, whereas the second network attempts to determine to which category each ion belongs. Based upon numerical results from the two networks, the program generates an idealized spectrum that contains only a single ion type. From this simplified spectrum, the program's sequencing module, which incorporates a small rule base of fragmentation knowledge, directly generates sequences in a stepwise fashion through a high-speed iterative process. The results with this prototype algorithm, in which the neural networks were trained on a set of reference spectra, suggest that this method is a viable approach to rapid computer interpretation of peptide CID spectra.