A New Non-restrictive Enzymatic Proteomics Analytic Strategy With The De Novo Peptide Sequencing

Since the HPP(Human Protein Project)was launched,the high-resolution mass spectrometry(MS)has been widely used for proteomics.In traditional analytic strategy of proteomics,proteins are fragmented with restrictive enzymes and then analyzed in MS.This strategy is unsatisfactory in many cases because of the low spectral utilization,resulting in poor performance of identification.In this study,we proposed a new strategy that uses non-restrictive protein digestion to improve the protein-identification coverage and peptide identification efficiency.However,random digestion makes the database-search strategy impossible to be applied,because this would expand the theoretical database prohibitively.In contrast,the de novo peptide sequencing can resolve mass spectrum independent of any reference sequence,which is suitable for analyzing these MS data with non-restrictive digestible strategies.In this study,sequence-random-match estimation model and RNC-seq were combined to control the false discovery rate(FDR)of protein identification results from the de novo peptide sequencing.This new strategy not only identified proteins from Hep3 B,MHCC97H and MHCCLM3 cell lines,but also analyzed the non-restrictive digestion data of MHCC97 H cell line effectively.This new strategy improved the protein-identification sensitivity and spectra utilization drastically.Therefore,this RNC-seq aided de novo identification strategy will significantly promote proteomics.