Mass Spectrometry Databases

Introduction

Library based MS approaches for microbial identification require labeled sets of microbial mass spectra. Starting with version 0.82, MicrobeMS can work with experimental MALDI-TOF or LC-MS¹ mass spectra and their corresponding MS databases.
The RKI databases of microbial MALDI-TOF mass spectra contain mass spectra of highly pathogenic (biosafety level 3, BSL-3) bacteria such as Bacillus anthracis, Yersinia pestis, Burkholderia mallei, Burkholderia pseudomallei, Brucella melitensis and Francisella tularensis as well as a selection of MALDI-TOF mass spectra of their close and distant relatives. The RKI mass spectral databases can be used as a reference for the diagnosis of BSL-3 bacteria using proprietary and free software packages for MALDI-TOF MS-based microbial identification. The databases are distributed as zip archives and contain the original mass spectra in their native data format (Bruker Daltonics). The MALDI-TOF MS databases are updated on a regular basis.
The LC-MS¹ database is an in silico database compiled from Uni-Prot Knowledgebase resources (Uni-Prot/KB Swissprot and TrEMBL), for details see below).

MALDI-TOF MS databases

The different versions of RKI biosafety level 3 (BSL-3) MALDI-TOF MS database can be downloaded from the following locations:

Screenshot of the ZENODO MALDI-ToF MS database data v.4.1

Screenshot of the ZENODO MALDI-ToF MS database data v.1.0

 1. ZENODO database version 4.1 (20230306):
    Lasch P, Stämmler M & Schneider A, (2023). Version 4.1 (20230306) of the 
    MALDI-TOF Mass Spectrometry Database for Identification and Classification of
    Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). 
    Zenodo. https://doi.org/10.5281/zenodo.7990990
    Version Mar 06, 2023, creative commons CC BY-NC-SA 4.0 license

 2. ZENODO database version 3 (20181130):
    Lasch P, Stämmler M & Schneider A, (2018). Version 3 (20181130) of the 
    MALDI-TOF Mass Spectrometry Database for Identification and Classification of
    Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). 
    Zenodo. https://doi.org/10.5281/zenodo.1880975
    Version Nov 30, 2018, creative commons CC BY-NC-SA 4.0 license

 3. ZENODO database version 2 (20170523):
    Lasch P, Stämmler M & Schneider , (2017). Version 2 (20170523) of the 
    MALDI-TOF Mass Spectrometry Database for Identification and Classification of
    Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). 
    Zenodo. http://doi.org/10.5281/zenodo.582602
    Version May 23, 2017, creative commons CC BY-NC-SA 4.0 license

 4. ZENODO database version 1 (20161027):
    Lasch P, Stämmler M & Schneider A, (2016). 
    A MALDI-TOF Mass Spectrometry Database for Identification and Classification of
    Highly Pathogenic from the Robert Koch-Institute (RKI). 
    Zenodo. http://doi.org/10.5281/zenodo.163517
    Version October 27, 2016, creative commons CC BY-NC-SA 4.0 license

LC-MS¹ databases

The original concept of microbial identification by means of MALDI-TOF MS of cultivated microbial cells and spectral distance-based comparison with entries of a microorganism spectra library has been adapted for LC-MS¹ microbial identification, for details see

   Preprint: Lasch P, Schneider A, Blumenscheit C and Doellinger J.
   Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS¹) and in silico Peptide Mass Data, 
   bioRxiv (Dec 10, 2018), doi:10.1101/870089.

   Peer reviewed paper: Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger.
   Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS(1)) and in Silico Peptide Mass Libraries.
   Mol Cell Proteomics, 2020. 19(12): p. 2125-2139.

   Supplementary data - LC-MS¹ database and program code: Lasch P, Schneider A, Blumenscheit C, Doellinger J.
   In silico Database for Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS¹). 
   (ZENODO). https://doi.org/10.5281/zenodo.3573996
   Version December 13, 2019, creative commons CC BY-NC-SA 4.0 license 

   Tutorial: Identification analysis by means of LC-MS¹ and in silico databases