Molecular dynamics simulation of an entire cell.

The ultimate microscope, directed at a cell, would reveal the dynamics of all the cell's components with atomic resolution. In contrast to their real-world counterparts, computational microscopes are currently on the brink of meeting this challenge. In this perspective, we show how an integrative approach can be employed to model an entire cell, the minimal cell, JCVI-syn3A, at full complexity. This step opens the way to interrogate the cell's spatio-temporal evolution with molecular dynamics simulations, an approach that can be extended to other cell types in the near future.

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.

Essential metabolism for a minimal cell.

JCVI-syn3A, a robust minimal cell with a 543 kbp genome and 493 genes, provides a versatile platform to study the basics of life. Using the vast amount of experimental information available on its precursor, Mycoplasma mycoides capri, we assembled a near-complete metabolic network with 98% of enzymatic reactions supported by annotation or experiment. The model agrees well with genome-scale in vivo transposon mutagenesis experiments, showing a Matthews correlation coefficient of 0.59. The genes in the reconstruction have a high in vivo essentiality or quasi-essentiality of 92% (68% essential), compared to 79% in silico essentiality. This coherent model of the minimal metabolism in JCVI-syn3A at the same time also points toward specific open questions regarding the minimal genome of JCVI-syn3A, which still contains many genes of generic or completely unclear function. In particular, the model, its comparison to in vivo essentiality and proteomics data yield specific hypotheses on gene functions and metabolic capabilities; and provide suggestions for several further gene removals. In this way, the model and its accompanying data guide future investigations of the minimal cell. Finally, the identification of 30 essential genes with unclear function will motivate the search for new biological mechanisms beyond metabolism.

One way that researchers can test whether they understand a biological system is to see if they can accurately recreate it as a computer model. The more they learn about living things, the more the researchers can improve their models and the closer the models become to simulating the original. In this approach, it is best to start by trying to model a simple system. Biologists have previously succeeded in creating 'minimal bacterial cells'. These synthetic cells contain fewer genes than almost all other living things and they are believed to be among the simplest possible forms of life that can grow on their own. The minimal cells can produce all the chemicals that they need to survive ­ in other words, they have a metabolism. Accurately recreating one of these cells in a computer is a key first step towards simulating a complete living system. Breuer et al. have developed a computer model to simulate the network of the biochemical reactions going on inside a minimal cell with just 493 genes. By altering the parameters of their model and comparing the results to experimental data, Breuer et al. explored the accuracy of their model. Overall, the model reproduces experimental results, but it is not yet perfect. The differences between the model and the experiments suggest new questions and tests that could advance our understanding of biology. In particular, Breuer et al. identified 30 genes that are essential for life in these cells but that currently have no known purpose. Continuing to develop and expand models like these to reproduce more complex living systems provides a tool to test current knowledge of biology. These models may become so advanced that they could predict how living things will respond to changing situations. This would allow scientists to test ideas sooner and make much faster progress in understanding life on Earth. Ultimately, these models could one day help to accelerate medical and industrial processes to save lives and enhance productivity.

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.

Spatially-resolved metabolic cooperativity within dense bacterial colonies.

BACKGROUND: The exchange of metabolites and the reprogramming of metabolism in response to shifting microenvironmental conditions can drive subpopulations of cells within colonies toward divergent behaviors. Understanding the interactions of these subpopulations-their potential for competition as well as cooperation-requires both a metabolic model capable of accounting for a wide range of environmental conditions, and a detailed dynamic description of the cells' shared extracellular space. RESULTS: Here we show that a cell's position within an in silico Escherichia coli colony grown on glucose minimal agar can drastically affect its metabolism: "pioneer" cells at the outer edge engage in rapid growth that expands the colony, while dormant cells in the interior separate two spatially distinct subpopulations linked by a cooperative form of acetate crossfeeding that has so far gone unnoticed. Our hybrid simulation technique integrates 3D reaction-diffusion modeling with genome-scale flux balance analysis (FBA) to describe the position-dependent metabolism and growth of cells within a colony. Our results are supported by imaging experiments involving strains of fluorescently-labeled E. coli. The spatial patterns of fluorescence within these experimental colonies identify cells with upregulated genes associated with acetate crossfeeding and are in excellent agreement with the predictions. Furthermore, the height-to-width ratios of both the experimental and simulated colonies are in good agreement over a growth period of 48 hours. CONCLUSIONS: Our modeling paradigm can accurately reproduce a number of known features of E. coli colony growth, as well as predict a novel one that had until now gone unrecognized. The acetate crossfeeding we see has a direct analogue in a form of lactate crossfeeding observed in certain forms of cancer, and we anticipate future application of our methodology to models of tissues and tumors.

Simulation of reaction diffusion processes over biologically relevant size and time scales using multi-GPU workstations.

Simulation of in vivo cellular processes with the reaction-diffusion master equation (RDME) is a computationally expensive task. Our previous software enabled simulation of inhomogeneous biochemical systems for small bacteria over long time scales using the MPD-RDME method on a single GPU. Simulations of larger eukaryotic systems exceed the on-board memory capacity of individual GPUs, and long time simulations of modest-sized cells such as yeast are impractical on a single GPU. We present a new multi-GPU parallel implementation of the MPD-RDME method based on a spatial decomposition approach that supports dynamic load balancing for workstations containing GPUs of varying performance and memory capacity. We take advantage of high-performance features of CUDA for peer-to-peer GPU memory transfers and evaluate the performance of our algorithms on state-of-the-art GPU devices. We present parallel e ciency and performance results for simulations using multiple GPUs as system size, particle counts, and number of reactions grow. We also demonstrate multi-GPU performance in simulations of the Min protein system in E. coli. Moreover, our multi-GPU decomposition and load balancing approach can be generalized to other lattice-based problems.

Leucyl-tRNA synthetase editing domain functions as a molecular rheostat to control codon ambiguity in Mycoplasma pathogens.

Mycoplasma leucyl-tRNA synthetases (LeuRSs) have been identified in which the connective polypeptide 1 (CP1) amino acid editing domain that clears mischarged tRNAs are missing (Mycoplasma mobile) or highly degenerate (Mycoplasma synoviae). Thus, these enzymes rely on a clearance pathway called pretransfer editing, which hydrolyzes misactivated aminoacyl-adenylate intermediate via a nebulous mechanism that has been controversial for decades. Even as the sole fidelity pathway for clearing amino acid selection errors in the pathogenic M. mobile, pretransfer editing is not robust enough to completely block mischarging of tRNA(Leu), resulting in codon ambiguity and statistical proteins. A high-resolution X-ray crystal structure shows that M. mobile LeuRS structurally overlaps with other LeuRS cores. However, when CP1 domains from different aminoacyl-tRNA synthetases and origins were fused to this common LeuRS core, surprisingly, pretransfer editing was enhanced. It is hypothesized that the CP1 domain evolved as a molecular rheostat to balance multiple functions. These include distal control of specificity and enzyme activity in the ancient canonical core, as well as providing a separate hydrolytic active site for clearing mischarged tRNA.

Capture and quality control mechanisms for adenosine-5'-triphosphate binding.

The catalytic events in members of the nucleotidylyl transferase superfamily are initiated by a millisecond binding of ATP in the active site. Through metadynamics simulations on a class I aminoacyl-tRNA synthetase (aaRSs), the largest group in the superfamily, we calculate the free energy landscape of ATP selection and binding. Mutagenesis studies and fluorescence spectroscopy validated the identification of the most populated intermediate states. The rapid first binding step involves formation of encounter complexes captured through a fly casting mechanism that acts upon the triphosphate moiety of ATP. In the slower nucleoside binding step, a conserved histidine in the HxxH motif orients the incoming ATP through base-stacking interactions resulting in a deep minimum in the free energy surface. Mutation of this histidine significantly decreases the binding affinity measured experimentally and computationally. The metadynamics simulations further reveal an intermediate quality control state that the synthetases and most likely other members of the superfamily use to select ATP over other nucleoside triphosphates.

Exit strategies for charged tRNA from GluRS.

For several class I aminoacyl-tRNA synthetases (aaRSs), the rate-determining step in aminoacylation is the dissociation of charged tRNA from the enzyme. In this study, the following factors affecting the release of the charged tRNA from aaRSs are computationally explored: the protonation states of amino acids and substrates present in the active site, and the presence and the absence of AMP and elongation factor Tu. Through molecular modeling, internal pK(a) calculations, and molecular dynamics simulations, distinct, mechanistically relevant post-transfer states with charged tRNA bound to glutamyl-tRNA synthetase from Thermus thermophilus (Glu-tRNA(Glu)) are considered. The behavior of these nonequilibrium states is characterized as a function of time using dynamical network analysis, local energetics, and changes in free energies to estimate transitions that occur during the release of the tRNA. The hundreds of nanoseconds of simulation time reveal system characteristics that are consistent with recent experimental studies. Energetic and network results support the previously proposed mechanism in which the transfer of amino acid to tRNA is accompanied by the protonation of AMP to H-AMP. Subsequent migration of proton to water reduces the stability of the complex and loosens the interface both in the presence and in the absence of AMP. The subsequent undocking of AMP or tRNA then proceeds along thermodynamically competitive pathways. Release of the tRNA acceptor stem is further accelerated by the deprotonation of the alpha-ammonium group on the charging amino acid. The proposed general base is Glu41, a residue binding the alpha-ammonium group that is conserved in both structure and sequence across nearly all class I aaRSs. This universal handle is predicted through pK(a) calculations to be part of a proton relay system for destabilizing the bound charging amino acid following aminoacylation. Addition of elongation factor Tu to the aaRS.tRNA complex stimulates the dissociation of the tRNA core and the tRNA acceptor stem.

Dynamics of Recognition between tRNA and elongation factor Tu.

Elongation factor Tu (EF-Tu) binds to all standard aminoacyl transfer RNAs (aa-tRNAs) and transports them to the ribosome while protecting the ester linkage between the tRNA and its cognate amino acid. We use molecular dynamics simulations to investigate the dynamics of the EF-Tu.guanosine 5'-triphosphate.aa-tRNA(Cys) complex and the roles played by Mg2+ ions and modified nucleosides on the free energy of protein.RNA binding. Individual modified nucleosides have pronounced effects on the structural dynamics of tRNA and the EF-Tu.Cys-tRNA(Cys) interface. Combined energetic and evolutionary analyses identify the coevolution of residues in EF-Tu and aa-tRNAs at the binding interface. Highly conserved EF-Tu residues are responsible for both attracting aa-tRNAs as well as providing nearby nonbonded repulsive energies that help fine-tune molecular attraction at the binding interface. In addition to the 3' CCA end, highly conserved tRNA nucleotides G1, G52, G53, and U54 contribute significantly to EF-Tu binding energies. Modification of U54 to thymine affects the structure of the tRNA common loop resulting in a change in binding interface contacts. In addition, other nucleotides, conserved within certain tRNA specificities, may be responsible for tuning aa-tRNA binding to EF-Tu. The trend in EF-Tu.Cys-tRNA(Cys) binding energies observed as the result of mutating the tRNA agrees with experimental observation. We also predict variations in binding free energies upon misacylation of tRNA(Cys) with d-cysteine or O-phosphoserine and upon changing the protonation state of l-cysteine. Principal components analysis in each case reveals changes in the communication network across the protein.tRNA interface and is the basis for the entropy calculations.

The evolutionary history of Cys-tRNACys formation.

The recent discovery of an alternate pathway for indirectly charging tRNA(Cys) has stimulated a re-examination of the evolutionary history of Cys-tRNA(Cys) formation. In the first step of the pathway, O-phosphoseryl-tRNA synthetase charges tRNA(Cys) with O-phosphoserine (Sep), a precursor of the cognate amino acid. In the following step, Sep-tRNA:Cys-tRNA synthase (SepCysS) converts Sep to Cys in a tRNA-dependent reaction. The existence of such a pathway raises several evolutionary questions, including whether the indirect pathway is a recent evolutionary invention, as might be implied from its localization to the Euryarchaea, or, as evidence presented here indicates, whether this pathway is more ancient, perhaps already in existence at the time of the last universal common ancestral state. A comparative phylogenetic approach is used, combining evolutionary information from protein sequences and structures, that takes both the signature of horizontal gene transfer and the recurrence of the full canonical phylogenetic pattern into account, to document the complete evolutionary history of cysteine coding and understand the nature of this process in the last universal common ancestral state. Resulting from the historical study of tRNA(Cys) aminoacylation and the integrative perspective of sequence, structure, and function are 3D models of O-phosphoseryl-tRNA synthetase and SepCysS, which provide experimentally testable predictions regarding the identity and function of key active-site residues in these proteins. The model of SepCysS is used to suggest a sulfhydrylation reaction mechanism, which is predicted to occur at the interface of a SepCysS dimer.

Evolutionary profiles from the QR factorization of multiple sequence alignments.

We present an algorithm to generate complete evolutionary profiles that represent the topology of the molecular phylogenetic tree of the homologous group. The method, based on the multidimensional QR factorization of numerically encoded multiple sequence alignments, removes redundancy from the alignments and orders the protein sequences by increasing linear dependence, resulting in the identification of a minimal basis set of sequences that spans the evolutionary space of the homologous group of proteins. We observe a general trend that these smaller, more evolutionarily balanced profiles have comparable and, in many cases, better performance in database searches than conventional profiles containing hundreds of sequences, constructed in an iterative and computationally intensive procedure. For more diverse families or superfamilies, with sequence identity <30%, structural alignments, based purely on the geometry of the protein structures, provide better alignments than pure sequence-based methods. Merging the structure and sequence information allows the construction of accurate profiles for distantly related groups. These structure-based profiles outperformed other sequence-based methods for finding distant homologs and were used to identify a putative class II cysteinyl-tRNA synthetase (CysRS) in several archaea that eluded previous annotation studies. Phylogenetic analysis showed the putative class II CysRSs to be a monophyletic group and homology modeling revealed a constellation of active site residues similar to that in the known class I CysRS.