The success of a bacterial drug discovery program can be no greater than the phylogenetic diversity and capacity of those bacteria in the library to produce specialized metabolites (SM). However, the methods used to create bacterial strain libraries have seen little innovation in nearly 80 years. Current practice relies entirely on colony morphology and/or 16S rRNA gene sequencing analysis to decide which isolated strains to retain for addition to a drug discovery library. However, these practices create inefficient libraries plagued with a high degree of taxonomic and chemical redundancy by relying on physical characteristics that have limited correlation with strains’ SM, the foundation of drug discovery. Therefore, the development of a platform to rapidly prioritize unknown bacterial strains based on phylogeny and SM would greatly increase the efficiency of the front-end of microbial drug discovery. Our lab has recently developed such a platform, called IDBac, which uses in situ matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to analyze protein and specialized metabolite spectra of single bacterial colonies. Utilizing R and Shiny, alongside state-of-the-art packages and techniques in MALDI processing and data visualization, we created a stand-alone executable program for MALDI-TOF MS bacterial analysis. Using unsupervised learning methods and visualizations we have demonstrated IDBac’s capabilities by creating protein and specialized metabolite MS profiles, generating protein MS hierarchical groupings that accurately mirrored phylogenetic groupings and further distinguishing isolates based on inter- and intra-species differences in specialized metabolite production. With the ease of use of modern MALDI instrumentation and interactive, intuitive data exploration, IDBac can rapidly profile up to 384 bacteria in 4 hours. To our knowledge, IDBac is the first attempt to couple in situ MS analyses of protein content and specialized metabolite production and will enable laboratories with access to a MALDI-TOF MS the ability to rapidly create more efficient libraries for their drug discovery programs.
Chase Clark
University of Illinois
IDBac: A New Paradigm in Developing Microbial Libraries for Drug Discovery
The success of a bacterial drug discovery program can be no greater than the phylogenetic diversity and capacity of those bacteria in the library to produce specialized metabolites (SM). However, the methods used to create bacterial strain libraries have seen little innovation in nearly 80 years. Current practice relies entirely on colony morphology and/or 16S rRNA gene sequencing analysis to decide which isolated strains to retain for addition to a drug discovery library. However, these practices create inefficient libraries plagued with a high degree of taxonomic and chemical redundancy by relying on physical characteristics that have limited correlation with strains’ SM, the foundation of drug discovery. Therefore, the development of a platform to rapidly prioritize unknown bacterial strains based on phylogeny and SM would greatly increase the efficiency of the front-end of microbial drug discovery. Our lab has recently developed such a platform, called IDBac, which uses in situ matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to analyze protein and specialized metabolite spectra of single bacterial colonies. Utilizing R and Shiny, alongside state-of-the-art packages and techniques in MALDI processing and data visualization, we created a stand-alone executable program for MALDI-TOF MS bacterial analysis. Using unsupervised learning methods and visualizations we have demonstrated IDBac’s capabilities by creating protein and specialized metabolite MS profiles, generating protein MS hierarchical groupings that accurately mirrored phylogenetic groupings and further distinguishing isolates based on inter- and intra-species differences in specialized metabolite production. With the ease of use of modern MALDI instrumentation and interactive, intuitive data exploration, IDBac can rapidly profile up to 384 bacteria in 4 hours. To our knowledge, IDBac is the first attempt to couple in situ MS analyses of protein content and specialized metabolite production and will enable laboratories with access to a MALDI-TOF MS the ability to rapidly create more efficient libraries for their drug discovery programs.