Inferring assembly-curving trends of bacterial micro-compartment shell hexamers from crystal structure arrangements.

Bacterial microcompartments (BMC) are complex macromolecular assemblies that participate in varied chemical processes in about one fourth of bacterial species. BMC-encapsulated enzymatic activities are segregated from other cell contents by means of semipermeable shells, justifying why BMC are viewed as prototype nano-reactors for biotechnological applications. Herein, we undertook a comparative study of bending propensities of BMC hexamers (BMC-H), the most abundant shell constituents. Published data show that some BMC-H, like ß-carboxysomal CcmK, tend to assemble flat whereas other BMC-H often build curved objects. Inspection of available crystal structures presenting BMC-H in tiled arrangements permitted us to identify two major assembly modes with a striking connection with experimental trends. All-atom molecular dynamics (MD) supported that BMC-H bending is triggered robustly only from the arrangement adopted in crystals by BMC-H that experimentally form curved objects, leading to very similar arrangements to those found in structures of recomposed BMC shells. Simulations on triplets of planar-behaving hexamers, which were previously reconfigured to comply with such organization, confirmed that bending propensity is mostly defined by the precise lateral positioning of hexamers, rather than by BMC-H identity. Finally, an interfacial lysine was pinpointed as the most decisive residue in controlling PduA spontaneous curvature. Globally, results presented herein should contribute to improve our understanding of the variable mechanisms of biogenesis characterized for BMC, and of possible strategies to regulate BMC size and shape.

Predicting partner fitness based on spatial structuring in a light-driven microbial community.

Microbial communities have vital roles in systems essential to human health and agriculture, such as gut and soil microbiomes, and there is growing interest in engineering designer consortia for applications in biotechnology (e.g., personalized probiotics, bioproduction of high-value products, biosensing). The capacity to monitor and model metabolite exchange in dynamic microbial consortia can provide foundational information important to understand the community level behaviors that emerge, a requirement for building novel consortia. Where experimental approaches for monitoring metabolic exchange are technologically challenging, computational tools can enable greater access to the fate of both chemicals and microbes within a consortium. In this study, we developed an in-silico model of a synthetic microbial consortia of sucrose-secreting Synechococcus elongatus PCC 7942 and Escherichia coli W. Our model was built on the NUFEB framework for Individual-based Modeling (IbM) and optimized for biological accuracy using experimental data. We showed that the relative level of sucrose secretion regulates not only the steady-state support for heterotrophic biomass, but also the temporal dynamics of consortia growth. In order to determine the importance of spatial organization within the consortium, we fit a regression model to spatial data and used it to accurately predict colony fitness. We found that some of the critical parameters for fitness prediction were inter-colony distance, initial biomass, induction level, and distance from the center of the simulation volume. We anticipate that the synergy between experimental and computational approaches will improve our ability to design consortia with novel function.

Investigating Carboxysome Morphology Dynamics with a Rotationally Invariant Variational Autoencoder.

Carboxysomes are a class of bacterial microcompartments that form proteinaceous organelles within the cytoplasm of cyanobacteria and play a central role in photosynthetic metabolism by defining a cellular microenvironment permissive to CO2 fixation. Critical aspects of the assembly of the carboxysomes remain relatively unknown, especially with regard to the dynamics of this microcompartment. Progress in understanding carboxysome dynamics is impeded in part because analysis of the subtle changes in carboxysome morphology with microscopy remains a low-throughput and subjective process. Here we use deep learning techniques, specifically a Rotationally Invariant Variational Autoencoder (rVAE), to analyze fluorescence microscopy images of cyanobacteria bearing a carboxysome reporter and quantitatively evaluate how carboxysome shell remodelling impacts subtle trends in the morphology of the microcompartment over time. Toward this goal, we use a recently developed tool to control endogenous protein levels, including carboxysomal components, in the model cyanobacterium Synechococcous elongatus PCC 7942. By utilization of this system, proteins that compose the carboxysome can be tuned in real time as a method to examine carboxysome dynamics. We find that rVAEs are able to assist in the quantitative evaluation of changes in carboxysome numbers, shape, and size over time. We propose that rVAEs may be a useful tool to accelerate the analysis of carboxysome assembly and dynamics in response to genetic or environmental perturbation and may be more generally useful to probe regulatory processes involving a broader array of bacterial microcompartments.

Visualizing in Vivo Dynamics of Designer Nanoscaffolds.

Enzymes of natural biochemical pathways are routinely subcellularly organized in space and time in order to improve pathway efficacy and control. Designer scaffolding platforms are under development to confer similar benefits upon engineered pathways. Herein, we evaluate bacterial microcompartment shell (pfam0936-domain) proteins as modules for constructing well-defined nanometer scale scaffolds in vivo. We use a suite of visualization techniques to evaluate scaffold assembly and dynamics. We demonstrate recruitment of target cargo molecules onto assembled scaffolds by appending reciprocally interacting adaptor domains. These interactions can be refined by fine-tuning the scaffold expression level. Real-time observation of this system reveals a nucleation-limited step where multiple scaffolds initially form within a cell. Over time, nucleated scaffolds reorganize into a single intracellular assembly, likely due to interscaffold competition for protein subunits. Our results suggest design considerations for using self-assembling proteins as building blocks to construct nanoscaffolds, while also providing a platform to visualize scaffold-cargo dynamics in vivo.

Heterohexamers Formed by CcmK3 and CcmK4 Increase the Complexity of Beta Carboxysome Shells.

Bacterial microcompartments (BMCs) encapsulate enzymes within a selectively permeable, proteinaceous shell. Carboxysomes are BMCs containing ribulose-1,5-bisphosphate carboxylase oxygenase and carbonic anhydrase that enhance carbon dioxide fixation. The carboxysome shell consists of three structurally characterized protein types, each named after the oligomer they form: BMC-H (hexamer), BMC-P (pentamer), and BMC-T (trimer). These three protein types form cyclic homooligomers with pores at the center of symmetry that enable metabolite transport across the shell. Carboxysome shells contain multiple BMC-H paralogs, each with distinctly conserved residues surrounding the pore, which are assumed to be associated with specific metabolites. We studied the regulation of ß-carboxysome shell composition by investigating the BMC-H genes ccmK3 and ccmK4 situated in a locus remote from other carboxysome genes. We made single and double deletion mutants of ccmK3 and ccmK4 in Synechococcus elongatus PCC7942 and show that, unlike CcmK3, CcmK4 is necessary for optimal growth. In contrast to other CcmK proteins, CcmK3 does not form homohexamers; instead CcmK3 forms heterohexamers with CcmK4 with a 1:2 stoichiometry. The CcmK3-CcmK4 heterohexamers form stacked dodecamers in a pH-dependent manner. Our results indicate that CcmK3-CcmK4 heterohexamers potentially expand the range of permeability properties of metabolite channels in carboxysome shells. Moreover, the observed facultative formation of dodecamers in solution suggests that carboxysome shell permeability may be dynamically attenuated by "capping" facet-embedded hexamers with a second hexamer. Because ß-carboxysomes are obligately expressed, heterohexamer formation and capping could provide a rapid and reversible means to alter metabolite flux across the shell in response to environmental/growth conditions.

Self-Propulsion Enhances Polymerization.

Self-assembly is a spontaneous process through which macroscopic structures are formed from basic microscopic constituents (e.g., molecules or colloids). By contrast, the formation of large biological molecules inside the cell (such as proteins or nucleic acids) is a process more akin to self-organization than to self-assembly, as it requires a constant supply of external energy. Recent studies have tried to merge self-assembly with self-organization by analyzing the assembly of self-propelled (or active) colloid-like particles whose motion is driven by a permanent source of energy. Here we present evidence that points to the fact that self-propulsion considerably enhances the assembly of polymers: self-propelled molecules are found to assemble faster into polymer-like structures than non self-propelled ones. The average polymer length increases towards a maximum as the self-propulsion force increases. Beyond this maximum, the average polymer length decreases due to the competition between bonding energy and disruptive forces that result from collisions. The assembly of active molecules might have promoted the formation of large pre-biotic polymers that could be the precursors of the informational polymers we observe nowadays.

External strain-enhanced cysteine enantiomeric separation ability on alloyed stepped surfaces.

Using density functional theory with an accurate treatment of van der Waals interactions, we investigate the enantioselective recognition and separation of chiral molecules on stepped metal surfaces. Our calculations demonstrate that the separation ability of metal substrates can be significantly enhanced by surface decoration and external strain. For example, applying 2% tensile strain to the Ag-alloyed Au(532) surface leads to a dramatic increase (by 89%) in cysteine enantioselectivity as compared to that of pristine Au(532). Analysis on the computed binding energies shows that the interaction energy is the predominant factor that affects the separation efficiency in strongly bound systems. Our study presents a new strategy to modify the enantioselectivity of stepped metal surfaces and paves the way for exploring high efficiency chiral separation technology in pharmaceutical industry.

Direct atomic fabrication and dopant positioning in Si using electron beams with active real-time image-based feedback.

Semiconductor fabrication is a mainstay of modern civilization, enabling the myriad applications and technologies that underpin everyday life. However, while sub-10 nanometer devices are already entering the mainstream, the end of the Moore's law roadmap still lacks tools capable of bulk semiconductor fabrication on sub-nanometer and atomic levels, with probe-based manipulation being explored as the only known pathway. Here we demonstrate that the atomic-sized focused beam of a scanning transmission electron microscope can be used to manipulate semiconductors such as Si on the atomic level, inducing growth of crystalline Si from the amorphous phase, reentrant amorphization, milling, and dopant front motion. These phenomena are visualized in real-time with atomic resolution. We further implement active feedback control based on real-time image analytics to automatically control the e-beam motion, enabling shape control and providing a pathway for atom-by-atom correction of fabricated structures in the near future. These observations open a new epoch for atom-by-atom manufacturing in bulk, the long-held dream of nanotechnology.

Enhancing Enantiomeric Separation with Strain: The Case of Serine on Cu(531).

Serine has two enantiomers, d and l, which exhibit identical physical and chemical properties but have dramatically different physiological effects. For the pharmaceutical industry, it is very important to be able to separate both enantiomers. Here we study the enantioselectivity of the (531) surfaces of Cu, Ag, Au, and Pd using density functional theory with an accurate treatment of the van der Waals interactions. Among these surfaces, it is found that Cu(531) is the most efficient for energetically separating serine enantiomers. This greater efficiency is ultimately related to a conformational strain imposed in serine and most of all in the supporting substrate. Motivated by this, we decorated the step sites of Cu(531) with Ni atoms and showed that serine enantioselectivity increases by 36% as compared to that of pristine Cu(531). These results suggest that efficient enantiomeric separation of small chiral molecules could be achieved with bimetallic stepped surfaces for which strain, both in the surface and the molecule, increases significantly upon deposition.

Atomic-Level Sculpting of Crystalline Oxides: Toward Bulk Nanofabrication with Single Atomic Plane Precision.

The atomic-level sculpting of 3D crystalline oxide nanostructures from metastable amorphous films in a scanning transmission electron microscope (STEM) is demonstrated. Strontium titanate nanostructures grow epitaxially from the crystalline substrate following the beam path. This method can be used for fabricating crystalline structures as small as 1-2 nm and the process can be observed in situ with atomic resolution. The fabrication of arbitrary shape structures via control of the position and scan speed of the electron beam is further demonstrated. Combined with broad availability of the atomic resolved electron microscopy platforms, these observations suggest the feasibility of large scale implementation of bulk atomic-level fabrication as a new enabling tool of nanoscience and technology, providing a bottom-up, atomic-level complement to 3D printing.

Dynamic defrosting on nanostructured superhydrophobic surfaces.

Water suspended on chilled superhydrophobic surfaces exhibits delayed freezing; however, the interdrop growth of frost through subcooled condensate forming on the surface seems unavoidable in humid environments. It is therefore of great practical importance to determine whether facile defrosting is possible on superhydrophobic surfaces. Here, we report that nanostructured superhydrophobic surfaces promote the growth of frost in a suspended Cassie state, enabling its dynamic removal upon partial melting at low tilt angles (<15°). The dynamic removal of the melting frost occurred in two stages: spontaneous dewetting followed by gravitational mobilization. This dynamic defrosting phenomenon is driven by the low contact angle hysteresis of the defrosted meltwater relative to frost on microstructured superhydrophobic surfaces, which forms in the impaled Wenzel state. Dynamic defrosting on nanostructured superhydrophobic surfaces minimizes the time, heat, and gravitational energy required to remove frost from the surface, and is of interest for a variety of systems in cold and humid environments.

Extraction of interaction parameters from specular neutron reflectivity in thin films of diblock copolymers: an "inverse problem".

Diblock copolymers have been shown to undergo microphase separation due to an interplay of repulsive interactions between dissimilar monomers, which leads to the stretching of chains and entropic loss due to the stretching. In thin films, additional effects due to confinement and monomer-surface interactions make microphase separation much more complicated than in that in bulks (i.e., without substrates). Previously, physics-based models have been used to interpret and extract various interaction parameters from the specular neutron reflectivities of annealed thin films containing diblock copolymers (J. P. Mahalik, J. W. Dugger, S. W. Sides, B. G. Sumpter, V. Lauter and R. Kumar, Interpreting neutron reflectivity profiles of diblock copolymer nanocomposite thin films using hybrid particle-field simulations, Macromolecules, 2018, 51(8), 3116; J. P. Mahalik, W. Li, A. T. Savici, S. Hahn, H. Lauter, H. Ambaye, B. G. Sumpter, V. Lauter and R. Kumar, Dispersity-driven stabilization of coexisting morphologies in asymmetric diblock copolymer thin films, Macromolecules, 2021, 54(1), 450). However, extracting Flory-Huggins χ parameters characterizing monomer-monomer, monomer-substrate, and monomer-air interactions has been labor-intensive and prone to errors, requiring the use of alternative methods for practical purposes. In this work, we have developed such an alternative method by employing a multi-layer perceptron, an autoencoder, and a variational autoencoder. These neural networks are used to extract interaction parameters not only from neutron scattering length density profiles constructed using self-consistent field theory-based simulations, but also from a noisy ad hoc model constructed previously. In particular, the variational autoencoder is shown to be the most promising tool when it comes to the reconstruction and extraction of parameters from an ad hoc neutron scattering length density profile of a thin film containing a symmetric di-block copolymer (poly(deuterated styrene-b-n-butyl methacrylate)). This work paves the way for automated analysis of specular neutron reflectivities from thin films of copolymers using machine learning tools.

Competition between collapse and breakup in nanometer-sized thin rings using molecular dynamics and continuum modeling.

We consider nanometer-sized fluid annuli (rings) deposited on a solid substrate and ask whether these rings break up into droplets due to the instability of Rayleigh-Plateau-type modified by the presence of the substrate, or collapse to a central drop due to the presence of azimuthal curvature. The analysis is carried out by a combination of atomistic molecular dynamics simulations and a continuum model based on a long-wave limit of Navier-Stokes equations. We find consistent results between the two approaches, and demonstrate characteristic dimension regimes which dictate the assembly dynamics.

Formamide-based prebiotic synthesis of nucleobases: a kinetically accessible reaction route.

Synthesis of nucleobases in nonaqueous environments is an alternative way for the emergence of terrestrial life, which could solve the fundamental problem connected to the hydrolytic instability of nucleic acid components in an aqueous environment. In this contribution, we present a plausible reaction route for the prebiotic synthesis of nucleobases in formamide, which does not require participation of the formamide trimer and aminoimidazole-carbonitrile intermediates. The computed activation energy of the proposed pathway is noticeably higher than that of the HCN-based synthetic route, but it is still feasible under the experimental conditions of the Saladino synthesis. We show that, albeit both the pyrimidine and purine ring formation utilizes the undissociated form of formamide, the dehydration product of formamide, HCN, may also play a key role in the mechanism. The rate determining step of the entire reaction path is the cyclization of the diaza-pentanimine precursor. The subsequent formation of the imidazole ring proceeds with a moderate activation energy. Our calculations thus demonstrate that the experimentally suggested reaction path without the involvement of aminoimidazole-carbonitrile intermediates is also a viable alternative for the nonaqueous synthesis of nucleobases.

Video frame prediction of microbial growth with a recurrent neural network.

The recent explosion of interest and advances in machine learning technologies has opened the door to new analytical capabilities in microbiology. Using experimental data such as images or videos, machine learning, in particular deep learning with neural networks, can be harnessed to provide insights and predictions for microbial populations. This paper presents such an application in which a Recurrent Neural Network (RNN) was used to perform prediction of microbial growth for a population of two Pseudomonas aeruginosa mutants. The RNN was trained on videos that were acquired previously using fluorescence microscopy and microfluidics. Of the 20 frames that make up each video, 10 were used as inputs to the network which outputs a prediction for the next 10 frames of the video. The accuracy of the network was evaluated by comparing the predicted frames to the original frames, as well as population curves and the number and size of individual colonies extracted from these frames. Overall, the growth predictions are found to be accurate in metrics such as image comparison, colony size, and total population. Yet, limitations exist due to the scarcity of available and comparable data in the literature, indicating a need for more studies. Both the successes and challenges of our approach are discussed.

Prebiotic routes to nucleosides: a quantum chemical insight into the energetics of the multistep reaction pathways.

One of the most controversial questions of the RNA world theory is the formation of nucleosides through the reaction of nucleobases with ribose. The study presented herein discusses the thermodynamics of nucleoside formation under prebiotic conditions through the classical reaction route, which involves ribose and cytosine, as well as through the novel pathway suggested by Powner et al. [Nature 2009, 459, 239-242]. Our computations show that, in contrast to the classical pathway, the route proposed by Powner et al. perfectly satisfies all conditions of a typical metabolic pathway that occurs in living organisms. In addition, we reveal the reasons that render the reaction of ribose with nucleobases endothermic and, thereby, less plausible under prebiotic conditions. We show that phosphates may play an indispensable role in the glycosylation of nucleobases by making this endothermic reaction step exothermic. In addition, we describe the catalytic role of phosphate anions in the formation of 2-aminooxazole, which is one of the key steps of the synthetic route reported by Powner et al.

Theoretical modeling on the kinetics of the arsenate-ester hydrolysis: implications to the stability of As-DNA.

Quantum chemical calculations reveal that neither steric hindrance nor less polar solvent medium is able to reduce the otherwise high hydrolysis rate of arsenate-esters. These results question the stability of As-DNA not only in aqueous but also in non-aqueous environments.

Structural, dynamical, and electronic transport properties of modified DNA duplexes containing size-expanded nucleobases.

Among the distinct strategies proposed to expand the genetic alphabet, size-expanded nucleobases are promising for the development of modified DNA duplexes with improved biotechnological properties. In particular, duplexes built up by replacing canonical bases with the corresponding benzo-fused counterparts could be valuable as molecular nanowires. In this context, this study reports the results of classical molecular dynamics simulations carried out to examine the structural and dynamical features of size-expanded DNAs, including both hybrid duplexes containing mixed pairs of natural and benzo-fused bases (xDNA) and pure size-expanded (xxDNA) duplexes. Furthermore, the electronic structure of both natural and size-expanded duplexes is examined by means of density functional computations. The results confirm that the structural and flexibility properties of the canonical DNA are globally little affected by the presence of benzo-fused bases. The most relevant differences are found in the enhanced size of the grooves, and the reduction in the twist. However, the analysis also reveals subtle structural effects related to the nature and sequence of benzo-fused bases in the duplex. On the other hand, electronic structure calculations performed for xxDNAs confirm the reduction in the HOMO-LUMO gap predicted from the analysis of the natural bases and their size-expanded counterparts, which suggests that pure size-expanded DNAs can be good conductors. A more complex situation is found for xDNAs, where fluctuations in the electrostatic interaction between base pairs exerts a decisive influence on the modulation of the energy gap.

Application of Machine Learning Techniques to an Agent-Based Model of Pantoea.

Agent-based modeling (ABM) is a powerful simulation technique which describes a complex dynamic system based on its interacting constituent entities. While the flexibility of ABM enables broad application, the complexity of real-world models demands intensive computing resources and computational time; however, a metamodel may be constructed to gain insight at less computational expense. Here, we developed a model in NetLogo to describe the growth of a microbial population consisting of Pantoea. We applied 13 parameters that defined the model and actively changed seven of the parameters to modulate the evolution of the population curve in response to these changes. We efficiently performed more than 3,000 simulations using a Python wrapper, NL4Py. Upon evaluation of the correlation between the active parameters and outputs by random forest regression, we found that the parameters which define the depth of medium and glucose concentration affect the population curves significantly. Subsequently, we constructed a metamodel, a dense neural network, to predict the simulation outputs from the active parameters and found that it achieves high prediction accuracy, reaching an R 2 coefficient of determination value up to 0.92. Our approach of using a combination of ABM with random forest regression and neural network reduces the number of required ABM simulations. The simplified and refined metamodels may provide insights into the complex dynamic system before their transition to more sophisticated models that run on high-performance computing systems. The ultimate goal is to build a bridge between simulation and experiment, allowing model validation by comparing the simulated data to experimental data in microbiology.

Theoretical studies on the intermolecular interactions of potentially primordial base-pair analogues.

Recent experimental studies on the Watson-Crick type base pairing of triazine and aminopyrimidine derivatives suggest that acid/base properties of the constituent bases might be related to the duplex stabilities measured in solution. Herein we use high-level quantum chemical calculations and molecular dynamics simulations to evaluate the base pairing and stacking interactions of seven selected base pairs, which are common in that they are stabilized by two N-H...O hydrogen bonds separated by one N-H...N hydrogen bond. We show that neither the base pairing nor the base stacking interaction energies correlate with the reported pK(a) data of the bases and the melting points of the duplexes. This suggests that the experimentally observed correlation between the melting point data of the duplexes and the pK(a) values of the constituent bases is not rooted in the intrinsic base pairing and stacking properties. The physical chemistry origin of the observed experimental correlation thus remains unexplained and requires further investigations. In addition, since our calculations are carried out with extrapolation to the complete basis set of atomic orbitals and with inclusion of higher electron correlation effects, they provide reference data for stacking and base pairing energies of non-natural bases.