Peptide identification via constrained multi-objective optimization: Pareto-based genetic algorithms

摘要

Automatic peptide identification from collision-induced dissociation tandem mass spectrometry data using optimization techniques is made difficult by large plateaus in the fitness landscapes of scoring functions, by the fuzzy nature of constraints from noisy data and by the existence of diverse but equally justifiable probabilistic models of peak matching. Here, two different scoring functions are combined into a parallel multi-objective optimization framework. It is shown how multi-objective optimization can be used to empirically test for independence between distinct scoring functions. The loss of selection pressure during the evolution of a population of putative peptide sequences by a Pareto-driven genetic algorithm is addressed by alternating between two definitions of fitness according to a numerical threshold.