Algorithms for Automatic Interpretation of High Resolution Mass Spectra

Abstract

Automated interpretation of high-resolution mass spectra in a reliable and efficient manner represents a highly challenging computational problem. This work aims at developing methods for reducing a high-resolution mass spectrum into its monoisotopic peak list, and automatically assigning observed masses to known fragment ion masses if the protein sequence is available. The methods are compiled into a suite of data reduction algorithms which is called MasSPIKE (Mass Spectrum Interpretation and Kernel Extraction). MasSPIKE includes modules for modeling noise across the spectrum, isotopic cluster identification, charge state determination, separation of overlapping isotopic distributions, picking isotopic peaks, aligning experimental and theoretical isotopic distributions for estimating a monoisotopic peak's location, generating the monoisotopic mass list, and assigning the observed monoisotopic masses to possible protein fragments. The method is tested against a complex top-down spectrum of bovine carbonic anhydrase. Results of each of the individual modules are compared with previously published work.