Fast Substructure Search in Combinatorial Library Spaces.

We present an efficient algorithm for substructure search in combinatorial libraries defined by synthons, i.e., substructures with connection points. Our method improves on existing approaches by introducing powerful heuristics and fast fingerprint screening to quickly eliminate branches of nonmatching combinations of synthons. With this, we achieve typical response times of a few seconds on a standard desktop computer for searches in large combinatorial libraries like the Enamine REAL Space. We published the Java source as part of the OpenChemLib under the BSD license, and we implemented tools to enable substructure search in custom combinatorial libraries.

Model-based integration of genomics and metabolomics reveals SNP functionality in Mycobacterium tuberculosis.

Human tuberculosis is caused by members of the Mycobacterium tuberculosis complex (MTBC) that vary in virulence and transmissibility. While genome-wide association studies have uncovered several mutations conferring drug resistance, much less is known about the factors underlying other bacterial phenotypes. Variation in the outcome of tuberculosis infection and diseases has been attributed primarily to patient and environmental factors, but recent evidence indicates an additional role for the genetic diversity among MTBC clinical strains. Here, we used metabolomics to unravel the effect of genetic variation on the strain-specific metabolic adaptive capacity and vulnerability. To define the functionality of single-nucleotide polymorphisms (SNPs) systematically, we developed a constraint-based approach that integrates metabolomic and genomic data. Our model-based predictions correctly classify SNP effects in pyruvate kinase and suggest a genetic basis for strain-specific inherent baseline susceptibility to the antibiotic para-aminosalicylic acid. Our method is broadly applicable across microbial life, opening possibilities for the development of more selective treatment strategies.

A shuttle vector series for precise genetic engineering of Saccharomyces cerevisiae.

Shuttle vectors allow for an efficient transfer of recombinant DNA into yeast cells and are widely used in fundamental research and biotechnology. While available shuttle vectors are applicable in many experimental settings, their use in quantitative biology is hampered by insufficient copy number control. Moreover, they often have practical constraints, such as limited modularity and few unique restriction sites. We constructed the pRG shuttle vector series, consisting of single- and multi-copy integrative, centromeric and episomal plasmids with marker genes for the selection in all commonly used auxotrophic yeast strains. The vectors feature a modular design and a large number of unique restriction sites, enabling an efficient exchange of every vector part and expansion of the series. Integration into the host genome is achieved using a double-crossover recombination mechanism, resulting in stable single- and multi-copy modifications. As centromeric and episomal plasmids give rise to a heterogeneous cell population, an analysis of their copy number distribution and loss behaviour was performed. Overall, the shuttle vector series supports the efficient cloning of genes and their maintenance in yeast cells with improved copy number control.