Fundamental behaviors emerge from simulations of a living minimal cell.

We present a whole-cell fully dynamical kinetic model (WCM) of JCVI-syn3A, a minimal cell with a reduced genome of 493 genes that has retained few regulatory proteins or small RNAs. Cryo-electron tomograms provide the cell geometry and ribosome distributions. Time-dependent behaviors of concentrations and reaction fluxes from stochastic-deterministic simulations over a cell cycle reveal how the cell balances demands of its metabolism, genetic information processes, and growth, and offer insight into the principles of life for this minimal cell. The energy economy of each process including active transport of amino acids, nucleosides, and ions is analyzed. WCM reveals how emergent imbalances lead to slowdowns in the rates of transcription and translation. Integration of experimental data is critical in building a kinetic model from which emerges a genome-wide distribution of mRNA half-lives, multiple DNA replication events that can be compared to qPCR results, and the experimentally observed doubling behavior.

Atoms to Phenotypes: Molecular Design Principles of Cellular Energy Metabolism.

We report a 100-million atom-scale model of an entire cell organelle, a photosynthetic chromatophore vesicle from a purple bacterium, that reveals the cascade of energy conversion steps culminating in the generation of ATP from sunlight. Molecular dynamics simulations of this vesicle elucidate how the integral membrane complexes influence local curvature to tune photoexcitation of pigments. Brownian dynamics of small molecules within the chromatophore probe the mechanisms of directional charge transport under various pH and salinity conditions. Reproducing phenotypic properties from atomistic details, a kinetic model evinces that low-light adaptations of the bacterium emerge as a spontaneous outcome of optimizing the balance between the chromatophore's structural integrity and robust energy conversion. Parallels are drawn with the more universal mitochondrial bioenergetic machinery, from whence molecular-scale insights into the mechanism of cellular aging are inferred. Together, our integrative method and spectroscopic experiments pave the way to first-principles modeling of whole living cells.

Theoretical Description of the Primary Proton-Coupled Electron Transfer Reaction in the Cytochrome bc1 Complex.

The cytochrome bc1 complex is a transmembrane enzymatic protein complex that plays a central role in cellular energy production and is present in both photosynthetic and respiratory chain organelles. Its reaction mechanism is initiated by the binding of a quinol molecule to an active site, followed by a series of charge transfer reactions between the quinol and protein subunits. Previous work hypothesized that the primary reaction was a concerted proton-coupled electron transfer (PCET) reaction because of the apparent absence of intermediate states associated with single proton or electron transfer reactions. In the present study, the kinetics of the primary bc1 complex PCET reaction is investigated with a vibronically nonadiabatic PCET theory in conjunction with all-atom molecular dynamics simulations and electronic structure calculations. The computed rate constants and relatively high kinetic isotope effects are consistent with experimental measurements on related biomimetic systems. The analysis implicates a concerted PCET mechanism with significant hydrogen tunneling and nonadiabatic effects in the bc1 complex. Moreover, the employed theoretical framework is shown to serve as a general strategy for describing PCET reactions in bioenergetic systems.

NAMD goes quantum: an integrative suite for hybrid simulations.

Hybrid methods that combine quantum mechanics (QM) and molecular mechanics (MM) can be applied to studies of reaction mechanisms in locations ranging from active sites of small enzymes to multiple sites in large bioenergetic complexes. By combining the widely used molecular dynamics and visualization programs NAMD and VMD with the quantum chemistry packages ORCA and MOPAC, we created an integrated, comprehensive, customizable, and easy-to-use suite (http://www.ks.uiuc.edu/Research/qmmm). Through the QwikMD interface, setup, execution, visualization, and analysis are streamlined for all levels of expertise.

An in-silico human cell model reveals the influence of spatial organization on RNA splicing.

Spatial organization is a characteristic of all cells, achieved in eukaryotic cells by utilizing both membrane-bound and membrane-less organelles. One of the key processes in eukaryotes is RNA splicing, which readies mRNA for translation. This complex and highly dynamical chemical process involves assembly and disassembly of many molecules in multiple cellular compartments and their transport among compartments. Our goal is to model the effect of spatial organization of membrane-less organelles (specifically nuclear speckles) and of organelle heterogeneity on splicing particle biogenesis in mammalian cells. Based on multiple sources of complementary experimental data, we constructed a spatial model of a HeLa cell to capture intracellular crowding effects. We then developed chemical reaction networks to describe the formation of RNA splicing machinery complexes and splicing processes within nuclear speckles (specific type of non-membrane-bound organelles). We incorporated these networks into our spatially-resolved human cell model and performed stochastic simulations for up to 15 minutes of biological time, the longest thus far for a eukaryotic cell. We find that an increase (decrease) in the number of nuclear pore complexes increases (decreases) the number of assembled splicing particles; and that compartmentalization is critical for the yield of correctly-assembled particles. We also show that a slight increase of splicing particle localization into nuclear speckles leads to a disproportionate enhancement of mRNA splicing and a reduction in the noise of generated mRNA. Our model also predicts that the distance between genes and speckles has a considerable effect on the mRNA production rate, with genes located closer to speckles producing mRNA at higher levels, emphasizing the importance of genome organization around speckles. The HeLa cell model, including organelles and sub-compartments, provides a flexible foundation to study other cellular processes that are strongly modulated by spatiotemporal heterogeneity.

Protein-guided RNA dynamics during early ribosome assembly.

The assembly of 30S ribosomes requires the precise addition of 20 proteins to the 16S ribosomal RNA. How early binding proteins change the ribosomal RNA structure so that later proteins may join the complex is poorly understood. Here we use single-molecule fluorescence resonance energy transfer (FRET) to observe real-time encounters between Escherichia coli ribosomal protein S4 and the 16S 5' domain RNA at an early stage of 30S assembly. Dynamic initial S4-RNA complexes pass through a stable non-native intermediate before converting to the native complex, showing that non-native structures can offer a low free-energy path to protein-RNA recognition. Three-colour FRET and molecular dynamics simulations reveal how S4 changes the frequency and direction of RNA helix motions, guiding a conformational switch that enforces the hierarchy of protein addition. These protein-guided dynamics offer an alternative explanation for induced fit in RNA-protein complexes.

Generalized correlation-based dynamical network analysis: a new high-performance approach for identifying allosteric communications in molecular dynamics trajectories.

Molecular interactions are essential for regulation of cellular processes from the formation of multi-protein complexes to the allosteric activation of enzymes. Identifying the essential residues and molecular features that regulate such interactions is paramount for understanding the biochemical process in question, allowing for suppression of a reaction through drug interventions or optimization of a chemical process using bioengineered molecules. In order to identify important residues and information pathways within molecular complexes, the dynamical network analysis method was developed and has since been broadly applied in the literature. However, in the dawn of exascale computing, this method is frequently limited to relatively small biomolecular systems. In this work, we provide an evolution of the method, application, and interface. All data processing and analysis are conducted through Jupyter notebooks, providing automatic detection of important solvent and ion residues, an optimized and parallel generalized correlation implementation that is linear with respect to the number of nodes in the system, and subsequent community clustering, calculation of betweenness of contacts, and determination of optimal paths. Using the popular visualization program visual molecular dynamics (VMD), high-quality renderings of the networks over the biomolecular structures can be produced. Our new implementation was employed to investigate three different systems, with up to 2.5M atoms, namely, the OMP-decarboxylase, the leucyl-tRNA synthetase complexed with its cognate tRNA and adenylate, and respiratory complex I in a membrane environment. Our enhanced and updated protocol provides the community with an intuitive and interactive interface, which can be easily applied to large macromolecular complexes.

Scalable molecular dynamics on CPU and GPU architectures with NAMD.

NAMDis a molecular dynamics program designed for high-performance simulations of very large biological objects on CPU- and GPU-based architectures. NAMD offers scalable performance on petascale parallel supercomputers consisting of hundreds of thousands of cores, as well as on inexpensive commodity clusters commonly found in academic environments. It is written in C++ and leans on Charm++ parallel objects for optimal performance on low-latency architectures. NAMD is a versatile, multipurpose code that gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force fields. Here, we review the main features of NAMD that allow both equilibrium and enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe the underlying concepts utilized by NAMD and their implementation, most notably for handling long-range electrostatics; controlling the temperature, pressure, and pH; applying external potentials on tailored grids; leveraging massively parallel resources in multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical descriptions. We detail the variety of options offered by NAMD for enhanced-sampling simulations aimed at determining free-energy differences of either alchemical or geometrical transformations and outline their applicability to specific problems. Last, we discuss the roadmap for the development of NAMD and our current efforts toward achieving optimal performance on GPU-based architectures, for pushing back the limitations that have prevented biologically realistic billion-atom objects to be fruitfully simulated, and for making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD is distributed free of charge with its source code at www.ks.uiuc.edu.

Direction Matters: Monovalent Streptavidin/Biotin Complex under Load.

Novel site-specific attachment strategies combined with improvements of computational resources enable new insights into the mechanics of the monovalent biotin/streptavidin complex under load and forced us to rethink the diversity of rupture forces reported in the literature. We discovered that the mechanical stability of this complex depends strongly on the geometry in which force is applied. By atomic force microscopy-based single molecule force spectroscopy we found unbinding of biotin to occur beyond 400 pN at force loading rates of 10 nN/s when monovalent streptavidin was tethered at its C-terminus. This value is about twice as high than that for N-terminal attachment. Steered molecular dynamics simulations provided a detailed picture of the mechanics of the unbinding process in the corresponding force loading geometries. Using machine learning techniques, we connected findings from hundreds of simulations to the experimental results, identifying different force propagation pathways. Interestingly, we observed that depending on force loading geometry, partial unfolding of N-terminal region of monovalent streptavidin occurs before biotin is released from the binding pocket.

Multiscale modeling and cinematic visualization of photosynthetic energy conversion processes from electronic to cell scales.

Conversion of sunlight into chemical energy, namely photosynthesis, is the primary energy source of life on Earth. A visualization depicting this process, based on multiscale computational models from electronic to cell scales, is presented in the form of an excerpt from the fulldome show Birth of Planet Earth. This accessible visual narrative shows a lay audience, including children, how the energy of sunlight is captured, converted, and stored through a chain of proteins to power living cells. The visualization is the result of a multi-year collaboration among biophysicists, visualization scientists, and artists, which, in turn, is based on a decade-long experimental-computational collaboration on structural and functional modeling that produced an atomic detail description of a bacterial bioenergetic organelle, the chromatophore. Software advancements necessitated by this project have led to significant performance and feature advances, including hardware-accelerated cinematic ray tracing and instanced visualizations for efficient cell-scale modeling. The energy conversion steps depicted feature an integration of function from electronic to cell levels, spanning nearly 12 orders of magnitude in time scales. This atomic detail description uniquely enables a modern retelling of one of humanity's earliest stories-the interplay between light and life.

Mechanisms of Nanonewton Mechanostability in a Protein Complex Revealed by Molecular Dynamics Simulations and Single-Molecule Force Spectroscopy.

Can molecular dynamics simulations predict the mechanical behavior of protein complexes? Can simulations decipher the role of protein domains of unknown function in large macromolecular complexes? Here, we employ a wide-sampling computational approach to demonstrate that molecular dynamics simulations, when carefully performed and combined with single-molecule atomic force spectroscopy experiments, can predict and explain the behavior of highly mechanostable protein complexes. As a test case, we studied a previously unreported homologue from Ruminococcus flavefaciens called X-module-Dockerin (XDoc) bound to its partner Cohesin (Coh). By performing dozens of short simulation replicas near the rupture event, and analyzing dynamic network fluctuations, we were able to generate large simulation statistics and directly compare them with experiments to uncover the mechanisms involved in mechanical stabilization. Our single-molecule force spectroscopy experiments show that the XDoc-Coh homologue complex withstands forces up to 1 nN at loading rates of 105 pN/s. Our simulation results reveal that this remarkable mechanical stability is achieved by a protein architecture that directs molecular deformation along paths that run perpendicular to the pulling axis. The X-module was found to play a crucial role in shielding the adjacent protein complex from mechanical rupture. These mechanisms of protein mechanical stabilization have potential applications in biotechnology for the development of systems exhibiting shear enhanced adhesion or tunable mechanics.

Simulating biological processes: stochastic physics from whole cells to colonies.

The last few decades have revealed the living cell to be a crowded spatially heterogeneous space teeming with biomolecules whose concentrations and activities are governed by intrinsically random forces. It is from this randomness, however, that a vast array of precisely timed and intricately coordinated biological functions emerge that give rise to the complex forms and behaviors we see in the biosphere around us. This seemingly paradoxical nature of life has drawn the interest of an increasing number of physicists, and recent years have seen stochastic modeling grow into a major subdiscipline within biological physics. Here we review some of the major advances that have shaped our understanding of stochasticity in biology. We begin with some historical context, outlining a string of important experimental results that motivated the development of stochastic modeling. We then embark upon a fairly rigorous treatment of the simulation methods that are currently available for the treatment of stochastic biological models, with an eye toward comparing and contrasting their realms of applicability, and the care that must be taken when parameterizing them. Following that, we describe how stochasticity impacts several key biological functions, including transcription, translation, ribosome biogenesis, chromosome replication, and metabolism, before considering how the functions may be coupled into a comprehensive model of a 'minimal cell'. Finally, we close with our expectation for the future of the field, focusing on how mesoscopic stochastic methods may be augmented with atomic-scale molecular modeling approaches in order to understand life across a range of length and time scales.

Population FBA predicts metabolic phenotypes in yeast.

Using protein counts sampled from single cell proteomics distributions to constrain fluxes through a genome-scale model of metabolism, Population flux balance analysis (Population FBA) successfully described metabolic heterogeneity in a population of independent Escherichia coli cells growing in a defined medium. We extend the methodology to account for correlations in protein expression arising from the co-regulation of genes and apply it to study the growth of independent Saccharomyces cerevisiae cells in two different growth media. We find the partitioning of flux between fermentation and respiration predicted by our model agrees with recent 13C fluxomics experiments, and that our model largely recovers the Crabtree effect (the experimentally known bias among certain yeast species toward fermentation with the production of ethanol even in the presence of oxygen), while FBA without proteomics constraints predicts respirative metabolism almost exclusively. The comparisons to the 13C study showed improvement upon inclusion of the correlations and motivated a technique to systematically identify inconsistent kinetic parameters in the literature. The minor secretion fluxes for glycerol and acetate are underestimated by our method, which indicate a need for further refinements to the metabolic model. For yeast cells grown in synthetic defined (SD) medium, the calculated broad distribution of growth rates matches experimental observations from single cell studies, and we characterize several metabolic phenotypes within our modeled populations that make use of diverse pathways. Fast growing yeast cells are predicted to perform significant amount of respiration, use serine-glycine cycle and produce ethanol in mitochondria as opposed to slow growing cells. We use a genetic algorithm to determine the proteomics constraints necessary to reproduce the growth rate distributions seen experimentally. We find that a core set of 51 constraints are essential but that additional constraints are still necessary to recover the observed growth rate distribution in SD medium.

Effects of DNA replication on mRNA noise.

There are several sources of fluctuations in gene expression. Here we study the effects of time-dependent DNA replication, itself a tightly controlled process, on noise in mRNA levels. Stochastic simulations of constitutive and regulated gene expression are used to analyze the time-averaged mean and variation in each case. The simulations demonstrate that to capture mRNA distributions correctly, chromosome replication must be realistically modeled. Slow relaxation of mRNA from the low copy number steady state before gene replication to the high steady state after replication is set by the transcript's half-life and contributes significantly to the shape of the mRNA distribution. Consequently both the intrinsic kinetics and the gene location play an important role in accounting for the mRNA average and variance. Exact analytic expressions for moments of the mRNA distributions that depend on the DNA copy number, gene location, cell doubling time, and the rates of transcription and degradation are derived for the case of constitutive expression and subsequently extended to provide approximate corrections for regulated expression and RNA polymerase variability. Comparisons of the simulated models and analytical expressions to experimentally measured mRNA distributions show that they better capture the physics of the system than previous theories.

The Conformational Change in Elongation Factor Tu Involves Separation of Its Domains.

Elongation factor Tu (EF-Tu) is a highly conserved GTPase that is responsible for supplying the aminoacylated tRNA to the ribosome. Upon binding to the ribosome, EF-Tu undergoes GTP hydrolysis, which drives a major conformational change, triggering the release of aminoacylated tRNA to the ribosome. Using a combination of molecular simulation techniques, we studied the transition between the pre- and post-hydrolysis structures through two distinct pathways. We show that the transition free energy is minimal along a non-intuitive pathway that involves "separation" of the GTP binding domain (domain 1) from the OB folds (domains 2 and 3), followed by domain 1 rotation, and, eventually, locking the EF-Tu conformation in the post-hydrolysis state. The domain separation also leads to a slight extension of the linker connecting domain 1 to domain 2. Using docking tools and correlation-based analysis, we identified and characterized the EF-Tu conformations that release the tRNA. These calculations suggest that EF-Tu can release the tRNA before the domains separate and after domain 1 rotates by 25°. We also examined the EF-Tu conformations in the context of the ribosome. Given the high degrees of sequence similarity with other translational GTPases, we predict a similar separation mechanism is followed.

Combining in Vitro and in Silico Single-Molecule Force Spectroscopy to Characterize and Tune Cellulosomal Scaffoldin Mechanics.

Cellulosomes are polyprotein machineries that efficiently degrade cellulosic material. Crucial to their function are scaffolds consisting of highly homologous cohesin domains, which serve a dual role by coordinating a multiplicity of enzymes as well as anchoring the microbe to its substrate. Here we combined two approaches to elucidate the mechanical properties of the main scaffold ScaA of Acetivibrio cellulolyticus. A newly developed parallelized one-pot in vitro transcription-translation and protein pull-down protocol enabled high-throughput atomic force microscopy (AFM)-based single-molecule force spectroscopy (SMFS) measurements of all cohesins from ScaA with a single cantilever, thus promising improved relative force comparability. Albeit very similar in sequence, the hanging cohesins showed considerably lower unfolding forces than the bridging cohesins, which are subjected to force when the microbe is anchored to its substrate. Additionally, all-atom steered molecular dynamics (SMD) simulations on homology models offered insight into the process of cohesin unfolding under force. Based on the differences among the individual force propagation pathways and their associated correlation communities, we designed mutants to tune the mechanical stability of the weakest hanging cohesin. The proposed mutants were tested in a second high-throughput AFM SMFS experiment revealing that in one case a single alanine to glycine point mutation suffices to more than double the mechanical stability. In summary, we have successfully characterized the force induced unfolding behavior of all cohesins from the scaffoldin ScaA, as well as revealed how small changes in sequence can have large effects on force resilience in cohesin domains. Our strategy provides an efficient way to test and improve the mechanical integrity of protein domains in general.

Genome-Scale Metabolic Modeling of Archaea Lends Insight into Diversity of Metabolic Function.

Decades of biochemical, bioinformatic, and sequencing data are currently being systematically compiled into genome-scale metabolic reconstructions (GEMs). Such reconstructions are knowledge-bases useful for engineering, modeling, and comparative analysis. Here we review the fifteen GEMs of archaeal species that have been constructed to date. They represent primarily members of the Euryarchaeota with three-quarters comprising representative of methanogens. Unlike other reviews on GEMs, we specially focus on archaea. We briefly review the GEM construction process and the genealogy of the archaeal models. The major insights gained during the construction of these models are then reviewed with specific focus on novel metabolic pathway predictions and growth characteristics. Metabolic pathway usage is discussed in the context of the composition of each organism's biomass and their specific energy and growth requirements. We show how the metabolic models can be used to study the evolution of metabolism in archaea. Conservation of particular metabolic pathways can be studied by comparing reactions using the genes associated with their enzymes. This demonstrates the utility of GEMs to evolutionary studies, far beyond their original purpose of metabolic modeling; however, much needs to be done before archaeal models are as extensively complete as those for bacteria.

Genome-wide gene expression and RNA half-life measurements allow predictions of regulation and metabolic behavior in Methanosarcina acetivorans.

BACKGROUND: While a few studies on the variations in mRNA expression and half-lives measured under different growth conditions have been used to predict patterns of regulation in bacterial organisms, the extent to which this information can also play a role in defining metabolic phenotypes has yet to be examined systematically. Here we present the first comprehensive study for a model methanogen. RESULTS: We use expression and half-life data for the methanogen Methanosarcina acetivorans growing on fast- and slow-growth substrates to examine the regulation of its genes. Unlike Escherichia coli where only small shifts in half-lives were observed, we found that most mRNA have significantly longer half-lives for slow growth on acetate compared to fast growth on methanol or trimethylamine. Interestingly, half-life shifts are not uniform across functional classes of enzymes, suggesting the existence of a selective stabilization mechanism for mRNAs. Using the transcriptomics data we determined whether transcription or degradation rate controls the change in transcript abundance. Degradation was found to control abundance for about half of the metabolic genes underscoring its role in regulating metabolism. Genes involved in half of the metabolic reactions were found to be differentially expressed among the substrates suggesting the existence of drastically different metabolic phenotypes that extend beyond just the methanogenesis pathways. By integrating expression data with an updated metabolic model of the organism (iST807) significant differences in pathway flux and production of metabolites were predicted for the three growth substrates. CONCLUSIONS: This study provides the first global picture of differential expression and half-lives for a class II methanogen, as well as provides the first evidence in a single organism that drastic genome-wide shifts in RNA half-lives can be modulated by growth substrate. We determined which genes in each metabolic pathway control the flux and classified them as regulated by transcription (e.g. transcription factor) or degradation (e.g. post-transcriptional modification). We found that more than half of genes in metabolism were controlled by degradation. Our results suggest that M. acetivorans employs extensive post-transcriptional regulation to optimize key metabolic steps, and more generally that degradation could play a much greater role in optimizing an organism's metabolism than previously thought.

Ribosome biogenesis in replicating cells: Integration of experiment and theory.

Ribosomes-the primary macromolecular machines responsible for translating the genetic code into proteins-are complexes of precisely folded RNA and proteins. The ways in which their production and assembly are managed by the living cell is of deep biological importance. Here we extend a recent spatially resolved whole-cell model of ribosome biogenesis in a fixed volume [Earnest et al., Biophys J 2015, 109, 1117-1135] to include the effects of growth, DNA replication, and cell division. All biological processes are described in terms of reaction-diffusion master equations and solved stochastically using the Lattice Microbes simulation software. In order to determine the replication parameters, we construct and analyze a series of Escherichia coli strains with fluorescently labeled genes distributed evenly throughout their chromosomes. By measuring these cells' lengths and number of gene copies at the single-cell level, we could fit a statistical model of the initiation and duration of chromosome replication. We found that for our slow-growing (120 min doubling time) E. coli cells, replication was initiated 42 min into the cell cycle and completed after an additional 42 min. While simulations of the biogenesis model produce the correct ribosome and mRNA counts over the cell cycle, the kinetic parameters for transcription and degradation are lower than anticipated from a recent analytical time dependent model of in vivo mRNA production. Describing expression in terms of a simple chemical master equation, we show that the discrepancies are due to the lack of nonribosomal genes in the extended biogenesis model which effects the competition of mRNA for ribosome binding, and suggest corrections to parameters to be used in the whole-cell model when modeling expression of the entire transcriptome. © 2016 Wiley Periodicals, Inc. Biopolymers 105: 735-751, 2016.