Narrow Results

Tolyporphins are unusual tetrapyrrole macrocycles produced by the filamentous cyanobacterium–microbial community HT-58-2, the only known source to date. Numerous cyanobacterial samples have been collected worldwide but most have not been screened for secondary metabolites. Identification of tolyporphins typically has entailed lipophilic extraction followed by chromatographic fractionation and spectroscopic and/or mass spectrometric analysis. For quantitation, lengthy lipophilic extraction, sample processing and HPLC separation are needed. Examination by MALDI-TOF-MS (with the matrix 1,5-diaminonaphthalene) of lipophilic crude extracts of small-scale HT-58-2 samples (2 mL) without chromatographic fractionation enabled semi-quantitation of tolyporphin A over a 41-day growth period. Screening for tolyporphin A in intact or slightly sheared and vortexed HT-58-2 samples (no lipophilic extraction), and confirmation of identity by tandem MS, were carried out by IR-MALDESI-FTMS. Tolyporphin A was identified by the molecular ion and four characteristic fragments. The molecular ion of chlorophyll $a$ also was observed. The sheared and vortexed sample contained substantial numbers of intact cells as demonstrated by regrowth of the filamentous cyanobacterium–microbial culture. The semi-quantitative and rapid qualitative methods developed herein should facilitate examination of other tolyporphin-producing organisms among the vast worldwide strains of cyanobacteria as well as investigation of the biosynthesis of tolyporphins.

Tandem mass spectrometry (MS/MS) data have become widespread, and the exploitation of this technology has gone from laboratories to general users in industry. Processing the huge amount of MS/MS data turns out to be a major computational challenge. In the era of big data mining, faster transformation of MS/MS data into effective information has been one of the most significant challenges in bioinformatics. Meanwhile, high-performance computing (HPC) has advanced rapidly in various fields. Accordingly, application of HPC in bioinformatics to gain insight from MS/MS data efficiently has been emphasized in both industry and academia. In this work, we review HPC in MS/MS data processing, present our research work, and analyze the relevant work in this domain. To provide a useful and comprehensive perspective, we categorize the research by both the HPC in database search sequence analysis and the HPC in de novo sequence analysis.