Peptide sequence determination from high-energy collision-induced dissociation spectra using artificial neural networks
peer-reviewed · Journal of the American Society for Mass Spectrometry · 1995
| Date | 1995-10-01 |
| Type | peer-reviewed |
| Venue | Journal of the American Society for Mass Spectrometry |
| Publisher | American Chemical Society (ACS) |
| Contribution | algorithm |
| DOI | 10.1016/1044-0305(95)00477-U |
| Citations (OpenAlex) | 11 |
| Venue 2-year citedness | 2.58 |
Abstract
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.
Methods and tools
- Neural-network CID peptide sequencing: Artificial-neural-network approach for peptide sequence determination from high-energy CID spectra.