Poster Presentation HUPO 2019 - 18th Human Proteome Organization World Congress

Enhancing middle-down proteomics data analysis of heavily modified peptides (#743)

Seungjin Na 1 , Eunok Paek 1
  1. Hanyang University, Seoul, SEOUL, South Korea

Middle-down proteomics, as an intermediate between bottom-up and top-down proteomics, has a great potential in specific applications such as chromatin biology and recombinant monoclonal antibodies. In chromatin biology studies, for instance, the analysis of intact histone N-terminal tails using middle-down mass spectrometry (MS) enables the detection of co-occurring multiple post-translational modifications (PTMs) or combinatorial histone PTMs involving biological states of chromatin. Widely used computational approaches, however, have been developed to deal with short tryptic peptides in bottom-up MS, and are thus neither efficient nor optimal for the identification of relatively long peptides in middle-down MS as well as the characterization of combinatorial PTMs. Here, we introduce a workflow that can accurately detect peptide features, perform deisotoping of MS/MS spectra, and allow any number of modifications at multiple sites per peptide, enhancing both the speed and accuracy of middle-down MS data analysis. We propose a new procedure to fast decide charge states of peptide features. For efficient identification of modified peptides with no limitation, we utilized a spectral alignment algorithm based on multiple sequence tags and dynamic programming. Our workflow can support more enhanced analysis given prior knowledge about modifications (e.g., prevalent acetylations and methylations in histones) of analyzing datasets. On experimental middle-down MS datasets of recombinant Bovine histones, our workflow improved the identifications by 2-fold over existing approaches. We could identify 50 differently modified peptides from H3 N-terminal tail in terms of the unique peptide mass and also identify hundreds of peptides with high charge states over 10+. Beyond middle-down MS data analysis, we argue that our method will be also applicable in identifying peptides in various approaches such as degradomic-peptidomic analyses.