Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life.

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The quantum nature of electrons and nuclei is manifested in countless biological events including the rearrangements of electrons in biochemical reactions, electron and proton tunneling, coupled proton−electron transfers, photoexcitations,

Precision oncology hinges on linking tumor genotype with druggable enzymatic dependencies1, however targeting the frequently dysregulated metabolic landscape of cancer has proven to be a major challenge2. Here we show that tissue context is the major determinant of NAD metabolic pathway dependence in cancer. By analyzing over 7000 tumors and 2600 matched normal samples of 19 tissue types, coupled with mathematical modeling and extensive in vitro and in vivo analyses, we identify a simple and actionable set of "rules". If the rate limiting enzyme of de novo NAD synthesis, NAPRT, is highly expressed in a normal tissue type, cancers that arise from that tissue will have a high frequency of NAPRT amplification and will be completely and irreversibly dependent on NAPRT for survival. Tumors arising from normal tissues that do not highly express NAPRT are entirely dependent on the NAD Salvage-pathway for survival. We identify the previously unknown enhancer that underlies this dependence. NAPRT amplification is demonstrated to generate an absolute, pharmacologically actionable tumor cell dependence for survival; dependence on NAMPT generated through enhancer remodeling is subject to resistance through NMRK1-dependent NAD synthesis. These results identify a central role for tissue context §

Rapid growth in size and complexity of biological data sets has led to the ‘Big Data to Knowledge' challenge. We develop advanced data integration methods for multi-level analysis of genomic, transcriptomic, ribosomal profiling, proteomic and fluxomic data. First, we show that pairwise integration of primary omics data reveals regularities that tie cellular processes together in Escherichia coli: the number of protein molecules made per mRNA transcript and the number of ribosomes required per translated protein molecule. Second, we show that genome-scale models, based on genomic and bibliomic data, enable quantitative synchronization of disparate data types. Integrating omics data with models enabled the discovery of two novel regularities: condition invariant in vivo turnover rates of enzymes and the correlation of protein structural motifs and translational pausing. These regularities can be formally represented in a computable format allowing for coherent interpretation and prediction of fitness and selection that underlies cellular physiology.

Here we present a biophysical, structural, and computational analysis of the directed evolution of the human DNA repair protein O 6 -alkylguanine-DNA alkyltransferase (hAGT) into SNAP-tag, a self-labeling protein tag. Evolution of hAGT led not only to increased protein activity but also to higher stability, especially of the alkylated protein, suggesting that the reactivity of the suicide enzyme can be influenced by stabilizing the product of the irreversible reaction. Whereas wild-type hAGT is rapidly degraded in cells after alkyl transfer, the high stability of benzylated SNAP-tag prevents proteolytic degradation. Our data indicate that the intrinstic stability of a key α helix is an important factor in triggering the unfolding and degradation of wild-type hAGT upon alkyl transfer, providing new insights into the structure−function relationship of the DNA repair protein.T he specific labeling of proteins with synthetic probes is a powerful approach for studying protein function. One way to achieve such a specific labeling is based on so-called selflabeling protein tags.1 In this approach, the protein of interest is expressed as a fusion protein with a peptide or protein (i.e., tag) whose role is to specifically bind to a synthetic probe in vitro or in vivo. A well-established example of a self-labeling protein tag is SNAP-tag.2 SNAP-tag specifically reacts with substituted O 6 -benzylguanine derivatives and thereby permits the labeling of SNAP-tag fusion proteins with a wide variety of different synthetic probes. Recent applications include its use for the analysis of protein complexes, 3 super-resolution microscopy, 4 the identification of protein−protein interactions, 5 drug target identification, 6 and the determination of protein half-life in animals. 7 The appeal of self-labeling tags such as SNAP-tag is the ease with which fusion proteins can be labeled with synthetic probes even in living cells. A conceptual limitation of the approach is the fact that the tag can affect the properties of its fusion partner. It is therefore important that the properties of the tag be as thoroughly characterized as possible.SNAP-tag was generated in a stepwise manner from human O 6 -alkylguanine-DNA alkyltransferase (hAGT) by introduction of a total of 19 point mutations (Figure 1) and deletion of 25 C-terminal residues. Saturation mutagenesis of four active-site residues followed by phage display and selection for activity against BG derivatives resulted in GE AGT, a mutant with 20-fold increased activity toward such substrates ( Figure 1B). 8Subsequent saturation mutagenesis of four additional residues involved in substrate binding followed by phage selection resulted in AGT-54, a mutant with 1.5-fold higher activity than GE AGT. To further optimize the protein for applications in protein labeling, mutations were introduced to suppress DNA binding and reactivity toward nucleosides, to remove nonessential cysteines, and to truncate the last 25 residues. 9The resulting mutant M AGT displayed relatively low activ...

Enzyme promiscuity toward substrates has been discussed in evolutionary terms as providing the flexibility to adapt to novel environments. In the present work, we describe an approach toward exploring such enzyme promiscuity in the space of a metabolic network. This approach leverages genome-scale models, which have been widely used for predicting growth phenotypes in various environments or following a genetic perturbation; however, these predictions occasionally fail. Failed predictions of gene essentiality offer an opportunity for targeting biological discovery, suggesting the presence of unknown underground pathways stemming from enzymatic cross-reactivity. We demonstrate a workflow that couples constraint-based modeling and bioinformatic tools with KO strain analysis and adaptive laboratory evolution for the purpose of predicting promiscuity at the genome scale. Three cases of genes that are incorrectly predicted as essential in Escherichia coli-aspC, argD, and gltA-are examined, and isozyme functions are uncovered for each to a different extent. Seven isozyme functions based on genetic and transcriptional evidence are suggested between the genes aspC and tyrB, argD and astC, gabT and puuE, and gltA and prpC. This study demonstrates how a targeted model-driven approach to discovery can systematically fill knowledge gaps, characterize underground metabolism, and elucidate regulatory mechanisms of adaptation in response to gene KO perturbations.underground metabolism | substrate promiscuity | systems biology | isozyme discovery | genome-scale modeling

SUMMARY Understanding the complex interactions that occur between heterologous and native biochemical pathways represents a major challenge in metabolic engineering and synthetic biology. We present a workflow that integrates metabolomics, proteomics, and genome-scale models of Escherichia coli metabolism to study the effects of introducing a heterologous pathway into a microbial host. This workflow incorporates complementary approaches from computational systems biology, metabolic engineering, and synthetic biology, provides molecular insight into how the host organism microenvironment changes due to pathway engineering, and demonstrates how biological mechanisms underlying strain variation can be exploited as an engineering strategy to increase product yield. As a proof-of-concept, we present the analysis of eight engineered strains producing three biofuels: isopentenol, limonene, and bisabolene. Application of this workflow identified the roles of candidate genes, pathways, and biochemical reactions in observed experimental phenomena and facilitated the construction of a mutant strain with improved productivity. The contributed workflow is available as an open-source tool in the form of iPython notebooks.