Towards Probabilistic Simulation of Tandem Mass Spectrometry Fragmentation Applied for Peptide Identification

Abstract

Peptide identification using mass spectrometry is an indispensable tool in the field of proteomics. There is already a wide range of approaches in the literature that attempt to infer peptides throughout various computational methods mixed with several biology properties. An important progress in the proteomics research has led a strong need for more efficient and accurate approaches for peptide identification. The accuracy and efficiency of these techniques is indispensable to ensure as many correctly identified peptides as possible. In this paper, we start by a comparison of database search and de novo peptide identification. We then present the main impact of cleavage distribution on intensity values during fragmentation process. Next, we present our proposed method of peptide identification by integrating intensity distribution in both database and de novo methods in order to improve the identification process. Other features have been taken into account in our calculation such as water and ammonia losses, and the correlation between amino acids. Then, we present the experiments and results applied to evaluate our approach in order to prove and ensure the effectiveness of our hypothesis. Finally, we propose our perspectives on future work by giving our thoughtful solutions for several problems that prevent to reach the correct identification.