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The results of copolymer characterization by coupling of chromatography and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) techniques and subsequent calculation of copolymer composition using a novel software tool MassChrom2D are presented. For high-resolution mass analysis copolymer samples were fractionated by means of liquid adsorption chromatography (LAC). These fractions were investigated off-line by MALDI-TOF MS. Various mono-n-butyl ethers of polyethylene oxide-polypropylene oxide copolymers (PEO-co-PPO) were investigated. As well as the copolymer composition presented in two-dimensional plots, the applied approach can give additional hints on specific structure-dependent separation conditions in chromatography.
Sequences and end groups of complex copolyesters were determined by fragmentation analysis by means of matrix-assisted laser desorption/ionization collision-induced dissociation tandem mass spectrometry (MALDI CID MS/MS). The complexity of the crude copolyester mixture was reduced by a chromatographic separation followed by a MALDI time-of-flight (TOF) investigation of fractions. Due to overlapping compositional and end-group information a clear assignment of end groups was very difficult. However, the fragmentation of suitable precursor ions resulted in typical fragment ion patterns and, therefore, enabled a fast and unambiguous determination of the end groups and composition of this important class of polymers.
Untargeted accurate strain-level classification of a priori unidentified organisms using tandem mass spectrometry is a challenging task. Reference databases often lack taxonomic depth, limiting peptide assignments to the species level. However, the extension with detailed strain information increases runtime and decreases statistical power. In addition, larger databases contain a higher number of similar proteomes. We present TaxIt, an iterative workflow to address the increasing search space required for MS/MS-based strain-level classification of samples with unknown taxonomic origin. TaxIt first applies reference sequence data for initial identification of species candidates, followed by automated acquisition of relevant strain sequences for low level classification. Furthermore, proteome similarities resulting in ambiguous taxonomic assignments are addressed with an abundance weighting strategy to increase the confidence in candidate taxa. For benchmarking the performance of our method, we apply our iterative workflow on several samples of bacterial and viral origin. In comparison to noniterative approaches using unique peptides or advanced abundance correction, TaxIt identifies microbial strains correctly in all examples presented (with one tie), thereby demonstrating the potential for untargeted and deeper taxonomic classification. TaxIt makes extensive use of public, unrestricted, and continuously growing sequence resources such as the NCBI databases and is available under open-source BSD license at https://gitlab.com/rki_bioinformatics/TaxIt.