Elizabeth Brunk

Mixed Quantum Mechanical/Molecular Mechanical Molecular Dynamics Simulations of Biological Systems in Ground and Electronically Excited States

The current state of the art of Quantum Mechanical/molecular mechanical (QM/MM) molecular dynamics approaches in ground and electronically excited states and their applications to biological problems is reviewed. For a complete description of quantum phenomena, the quantum nature of both electrons and nuclei has to be taken into account. Most of the current QM/MM applications are based on adiabatic dynamics in the electronic ground state. However, for dynamics in electronically excited states, the coupling between states, which is mediated via the nuclear motion, can be sizable, and nonadiabatic effects have to be taken into account. Configuration Interaction Singles (CIS) is a popular method in QM/MM applications due to its computational expedience that allows for the treatment of several hundred atoms. Since the 1990s, the Modified Neglect of Differential Overlap (MNDO) method has been further extended to a d orbital basis. This MNDO/d extension allows for the treatment of heavier elements. By using feature selection algorithms348 to identify the most appropriate subset of relevant variables that describe a certain phenomenon, the high-dimensionality of QM/MM data can be reduced and used for further analysis with causal inference algorithms to establish unique cause-effect relationships.

Model-driven discovery of underground metabolic functions in Escherichia coli

Significance Organisms have evolved to take advantage of their environment. Enzymes drive this adaptability by displaying flexibility in terms of substrate specificity and catalytic promiscuity. This enzyme promiscuity has been observed in a limited number of laboratory experiments; however, a larger underground network of reactions may occur within a cell below the level of detection. It is not until a cell’s metabolic capabilities are probed that these novel functions come to light. In this study, a workflow is presented for probing promiscuous activity at the genome scale. This workflow combines genome-scale reconstructions of metabolic networks with gene KOs and adaptive laboratory evolution. Such tools become increasingly important when designing drugs targeting pathogenic bacteria or engineering enzymes and bacteria for biotechnology applications. 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.

Directed evolution of the suicide protein O⁶-alkylguanine-DNA alkyltransferase for increased reactivity results in an alkylated protein with exceptional stability

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.

Integrating computational methods to retrofit enzymes to synthetic pathways

Microbial production of desired compounds provides an efficient framework for the development of renewable energy resources. To be competitive to traditional chemistry, one requirement is to utilize the full capacity of the microorganism to produce target compounds with high yields and turnover rates. We use integrated computational methods to generate and quantify the performance of novel biosynthetic routes that contain highly optimized catalysts. Engineering a novel reaction pathway entails addressing feasibility on multiple levels, which involves handling the complexity of large‐scale biochemical networks while respecting the critical chemical phenomena at the atomistic scale. To pursue this multi‐layer challenge, our strategy merges knowledge‐based metabolic engineering methods with computational chemistry methods. By bridging multiple disciplines, we provide an integral computational framework that could accelerate the discovery and implementation of novel biosynthetic production routes. Using this approach, we have identified and optimized a novel biosynthetic route for the production of 3HP from pyruvate. Biotechnol. Bioeng. 2012; 109:572–582.

Systems biology of the structural proteome

BackgroundThe success of genome-scale models (GEMs) can be attributed to the high-quality, bottom-up reconstructions of metabolic, protein synthesis, and transcriptional regulatory networks on an organism-specific basis. Such reconstructions are biochemically, genetically, and genomically structured knowledge bases that can be converted into a mathematical format to enable a myriad of computational biological studies. In recent years, genome-scale reconstructions have been extended to include protein structural information, which has opened up new vistas in systems biology research and empowered applications in structural systems biology and systems pharmacology.ResultsHere, we present the generation, application, and dissemination of genome-scale models with protein structures (GEM-PRO) for Escherichia coli and Thermotoga maritima. We show the utility of integrating molecular scale analyses with systems biology approaches by discussing several comparative analyses on the temperature dependence of growth, the distribution of protein fold families, substrate specificity, and characteristic features of whole cell proteomes. Finally, to aid in the grand challenge of big data to knowledge, we provide several explicit tutorials of how protein-related information can be linked to genome-scale models in a public GitHub repository (https://github.com/SBRG/GEMPro/tree/master/GEMPro_recon/).ConclusionsTranslating genome-scale, protein-related information to structured data in the format of a GEM provides a direct mapping of gene to gene-product to protein structure to biochemical reaction to network states to phenotypic function. Integration of molecular-level details of individual proteins, such as their physical, chemical, and structural properties, further expands the description of biochemical network-level properties, and can ultimately influence how to model and predict whole cell phenotypes as well as perform comparative systems biology approaches to study differences between organisms. GEM-PRO offers insight into the physical embodiment of an organism’s genotype, and its use in this comparative framework enables exploration of adaptive strategies for these organisms, opening the door to many new lines of research. With these provided tools, tutorials, and background, the reader will be in a position to run GEM-PRO for their own purposes.

iML1515, a knowledgebase that computes Escherichia coli traits

iML1515, a knowledgebase that computes Escherichia coli traits To the Editor: Extracting knowledge from the many types of big data produced by high-throughput methods remains a challenge, even when data are from Escherichia coli, the best characterized bacterial species. Here, we present iML1515, the most complete genome-scale reconstruction of the metabolic network in E. coli K-12 MG1655 to date, and we demonstrate how it can be used to address this challenge. Enabling analysis of several data types, including transcriptomes, proteomes, and metabolomes, iML1515 accounts for 1,515 open reading frames and 2,719 metabolic reactions involving 1,192 unique metabolites. The iML1515 knowledgebase is linked to 1,515 protein structures to provide an integrated modeling framework bridging systems and structural biology. We apply iML1515 to build metabolic models of E. coli human gut microbiome strains from metagenomic sequencing data. We then use iML1515 to build metabolic models for E. coli clinical isolates and predict their metabolic capabilities. Finally, we use iML1515 to carry out a comparative structural proteome analysis of 1,122 E. coli strains and identify multi-strain sequence variations.

Recon3D enables a three-dimensional view of gene variation in human metabolism

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.

A streamlined ribosome profiling protocol for the characterization of microorganisms

Ribosome profiling is a powerful tool for characterizing in vivo protein translation at the genome scale, with multiple applications ranging from detailed molecular mechanisms to systems-level predictive modeling. Though highly effective, this intricate technique has yet to become widely used in the microbial research community. Here we present a streamlined ribosome profiling protocol with reduced barriers to entry for microbial characterization studies. Our approach provides simplified alternatives during harvest, lysis, and recovery of monosomes and also eliminates several time-consuming steps, in particular size-selection steps during library construction. Furthermore, the abundance of rRNAs and tRNAs in the final library is drastically reduced. Our streamlined workflow enables greater throughput, cuts the time from harvest to the final library in half (down to 3-4 days), and generates a high fraction of informative reads, all while retaining the high quality standards of the existing protocol.

A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism.

Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins. The integration of these molecular-level details, such as the physical, structural, and dynamical properties of proteins, notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network. Using this approach, we can understand how genetic variation, which impacts the structure and reactivity of a protein, influences both native and drug-induced metabolic states. As a proof-of-concept, we study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase) and their respective genetic variants which have clinically relevant associations. Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins (and their mutant variants) in complex with their respective native metabolites or drug molecules. We find that changes in a protein’s structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules, and inflicts large-scale changes in metabolism.

Computational, structural, and kinetic evidence that Vibrio vulnificus FrsA is not a cofactor-independent pyruvate decarboxylase

The fermentation-respiration switch (FrsA) protein in Vibrio vulnificus was recently reported to catalyze the cofactor-independent decarboxylation of pyruvate. We now report quantum mechanical/molecular mechenical calculations that examine the energetics of C-C bond cleavage for a pyruvate molecule bound within the putative active site of FrsA. These calculations suggest that the barrier to C-C bond cleavage in the bound substrate is 28 kcal/mol, which is similar to that estimated for the uncatalyzed decarboxylation of pyruvate in water at 25 °C. In agreement with the theoretical predictions, no pyruvate decarboxylase activity was detected for recombinant FrsA protein that could be crystallized and structurally characterized. These results suggest that the functional annotation of FrsA as a cofactor-independent pyruvate decarboxylase is incorrect.