Improving protein identification from peptide mass fingerprinting through a parameterized multi-level scoring algorithm and an optimized peak detection.

摘要: We have developed a new algorithm to identify proteins by means of peptide mass fingerprinting. Starting from the matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) spectra and environmental data such as species, isoelectric point molecular weight, well chemical modifications or number missed cleavages protein, program performs fully automated identification protein. The first step is peak detection algorithm, which allows precise fast determination masses, even if peaks are low intensity they overlap. In second masses used search in protein sequence databases (SWISS-PROT and/or TrEMBL) for entries that match input data. Consequently, list candidate selected database, score calculation provides ranking according quality match. To define most discriminating scoring we analyzed respective role each parameter two directions. one based on filtering exploratory effects, while direction focuses levels where parameters intervene process. Thus, our analysis, all contribute score, however with different weights. Since it difficult estimate weights advance, been computed generic using training set 91 their tested resulting test ten compared results those other fingerprinting programs.