ABSTRACT
Integration of flexible data-analysis tools with cheminformatics methods is a prerequisite for successful identification and validation of "hits" in high-throughput screening (HTS) campaigns. We have designed, developed, and implemented a suite of robust yet flexible cheminformatics tools to support HTS activities at the Broad Institute, three of which are described herein. The "hit-calling" tool allows a researcher to set a hit threshold that can be varied during downstream analysis. The results from the hit-calling exercise are reported to a database for record keeping and further data analysis. The "cherry-picking" tool enables creation of an optimized list of hits for confirmatory and follow-up assays from an HTS hit list. This tool allows filtering by computed chemical property and by substructure. In addition, similarity searches can be performed on hits of interest and sets of related compounds can be selected. The third tool, an "S/SAR viewer," has been designed specifically for the Broad Institute's diversity-oriented synthesis (DOS) collection. The compounds in this collection are rich in chiral centers and the full complement of all possible stereoisomers of a given compound are present in the collection. The S/SAR viewer allows rapid identification of both structure/activity relationships and stereo-structure/activity relationships present in HTS data from the DOS collection. Together, these tools enable the prioritization and analysis of hits from diverse compound collections, and enable informed decisions for follow-up biology and chemistry efforts.
Subject(s)
Drug Design , High-Throughput Screening Assays , Structure-Activity Relationship , Algorithms , Combinatorial Chemistry Techniques , Databases, Factual , HumansABSTRACT
Genome-scale stoichiometric modeling of metabolism has become a standard systems biology tool for modeling cellular physiology and growth. Extensions of this approach are emerging as a valuable avenue for predicting, understanding and designing microbial communities. Computation of microbial ecosystems in time and space (COMETS) extends dynamic flux balance analysis to generate simulations of multiple microbial species in molecularly complex and spatially structured environments. Here we describe how to best use and apply the most recent version of COMETS, which incorporates a more accurate biophysical model of microbial biomass expansion upon growth, evolutionary dynamics and extracellular enzyme activity modules. In addition to a command-line option, COMETS includes user-friendly Python and MATLAB interfaces compatible with the well-established COBRA models and methods, as well as comprehensive documentation and tutorials. This protocol provides a detailed guideline for installing, testing and applying COMETS to different scenarios, generating simulations that take from a few minutes to several days to run, with broad applicability to microbial communities across biomes and scales.
Subject(s)
Models, Biological , Systems Biology , MicrobiotaABSTRACT
Genome engineering technologies now enable precise manipulation of organism genotype, but can be limited in scalability by their design requirements. Here we describe Merlin ( http://merlincad.org ), an open-source web-based tool to assist biologists in designing experiments using multiplex automated genome engineering (MAGE). Merlin provides methods to generate pools of single-stranded DNA oligonucleotides (oligos) for MAGE experiments by performing free energy calculation and BLAST scoring on a sliding window spanning the targeted site. These oligos are designed not only to improve recombination efficiency, but also to minimize off-target interactions. The application further assists experiment planning by reporting predicted allelic replacement rates after multiple MAGE cycles, and enables rapid result validation by generating primer sequences for multiplexed allele-specific colony PCR. Here we describe the Merlin oligo and primer design procedures and validate their functionality compared to OptMAGE by eliminating seven AvrII restriction sites from the Escherichia coli genome.