Challenges in structural approaches to cell modeling

3D models of fungal chromosomes to enhance visual integration of omics data

The functions of eukaryotic chromosomes and their spatial architecture in the nucleus are reciprocally dependent. Hi-C experiments are routinely used to study chromosome 3D organization by probing chromatin interactions. Standard representation of the data has relied on contact maps that show the frequency of interactions between parts of the genome. In parallel, it has become easier to build 3D models of the entire genome based on the same Hi-C data, and thus benefit from the methodology and visualization tools developed for structural biology. 3D modeling of entire genomes leverages the understanding of their spatial organization. However, this opportunity for original and insightful modeling is underexploited. In this paper, we show how seeing the spatial organization of chromosomes can bring new perspectives to omics data integration. We assembled state-of-the-art tools into a workflow that goes from Hi-C raw data to fully annotated 3D models and we re-analysed public omics datasets available for three fungal species. Besides the well-described properties of the spatial organization of their chromosomes (Rabl conformation, hypercoiling and chromosome territories), our results highlighted (i) in Saccharomyces cerevisiae, the backbones of the cohesin anchor regions, which were aligned all along the chromosomes, (ii) in Schizosaccharomyces pombe, the oscillations of the coiling of chromosome arms throughout the cell cycle and (iii) in Neurospora crassa, the massive relocalization of histone marks in mutants of heterochromatin regulators. 3D modeling of the chromosomes brings new opportunities for visual integration of omics data. This holistic perspective supports intuition and lays the foundation for building new concepts.

Bayesian methods in integrative structure modeling

There is a growing interest in characterizing the structure and dynamics of large biomolecular assemblies and their interactions within the cellular environment. A diverse array of experimental techniques allows us to study biomolecular systems on a variety of length and time scales. These techniques range from imaging with light, X-rays or electrons, to spectroscopic methods, cross-linking mass spectrometry and functional genomics approaches, and are complemented by AI-assisted protein structure prediction methods. A challenge is to integrate all of these data into a model of the system and its functional dynamics. This review focuses on Bayesian approaches to integrative structure modeling. We sketch the principles of Bayesian inference, highlight recent applications to integrative modeling and conclude with a discussion of current challenges and future perspectives.

Structure, energetics and dynamics in crowded amino acid solutions: a molecular dynamics study

A comprehensive understanding of crowding effects on biomolecular processes necessitates investigating the bulk thermodynamic and kinetic properties of the solutions with an accurate molecular representation of the crowded milieu. Recent studies have reparameterized the non-bonded dispersion interaction of solutes to precisely model intermolecular interactions, which would circumvent artificial aggregation as shown by the original force-fields. However, the performance of this reparameterization is yet to be assessed for concentrated crowded solutions in terms of investigating the hydration shell structure, energetics and dynamics. In this study, we perform molecular dynamics simulations of crowded aqueous solutions of five zwitterionic neutral amino acids (Gly, Ala, Thr, Pro, and Ser), mimicking the molecular crowding environment, using a modified AMBER ff99SB-ILDN force-field. We systematically examine and show that the reproducibility of the osmotic coefficients, density, viscosity and self-diffusivity of amino acids improves using the modified force-field in crowded concentrations. The modified force-field also improves the structuring of the solute solvation shells, solute interaction energy and convergence of tails of radial distribution functions, indicating reduction in the artificial aggregation. Our results also indicate that the hydrogen bonding network of water weakens and water molecules anomalously diffuse at small time scales in the crowded solutions. These results underscore the significance of examining the solution properties and anomalous hydration behaviour of water in crowded solutions, which have implications in shaping the structure and dynamics of biomolecules. The findings also illustrate the improvement in predicting bulk solution properties using the modified force-field, thereby providing an approach towards accurate modeling of crowded molecular solutions.

Size of the protein-protein energy funnel in crowded environment

Association of proteins to a significant extent is determined by their geometric complementarity. Large-scale recognition factors, which directly relate to the funnel-like intermolecular energy landscape, provide important insights into the basic rules of protein recognition. Previously, we showed that simple energy functions and coarse-grained models reveal major characteristics of the energy landscape. As new computational approaches increasingly address structural modeling of a whole cell at the molecular level, it becomes important to account for the crowded environment inside the cell. The crowded environment drastically changes protein recognition properties, and thus significantly alters the underlying energy landscape. In this study, we addressed the effect of crowding on the protein binding funnel, focusing on the size of the funnel. As crowders occupy the funnel volume, they make it less accessible to the ligands. Thus, the funnel size, which can be defined by ligand occupancy, is generally reduced with the increase of the crowders concentration. This study quantifies this reduction for different concentration of crowders and correlates this dependence with the structural details of the interacting proteins. The results provide a better understanding of the rules of protein association in the crowded environment.

Docking-based long timescale simulation of cell-size protein systems at atomic resolution

Advances in computational modeling have led to an increasing focus on larger biomolecular systems, up to the level of a cell. Protein interactions are a central component of cellular processes. Techniques for modeling protein interactions have been divided between two fields: protein docking (predicting the static structures of protein complexes) and molecular simulation (modeling the dynamics of protein association, for relatively short simulation times at atomic resolution). Our study combined the two approaches to reach very long simulation times. The study makes the model more adequate to the real cells, to explore cellular processes at atomic resolution to better understand molecular mechanisms of life, and to use this knowledge to improve our ability to treat diseases.

Computational methodologies are increasingly addressing modeling of the whole cell at the molecular level. Proteins and their interactions are the key component of cellular processes. Techniques for modeling protein interactions, thus far, have included protein docking and molecular simulation. The latter approaches account for the dynamics of the interactions but are relatively slow, if carried out at all-atom resolution, or are significantly coarse grained. Protein docking algorithms are far more efficient in sampling spatial coordinates. However, they do not account for the kinetics of the association (i.e., they do not involve the time coordinate). Our proof-of-concept study bridges the two modeling approaches, developing an approach that can reach unprecedented simulation timescales at all-atom resolution. The global intermolecular energy landscape of a large system of proteins was mapped by the pairwise fast Fourier transform docking and sampled in space and time by Monte Carlo simulations. The simulation protocol was parametrized on existing data and validated on a number of observations from experiments and molecular dynamics simulations. The simulation protocol performed consistently across very different systems of proteins at different protein concentrations. It recapitulated data on the previously observed protein diffusion rates and aggregation. The speed of calculation allows reaching second-long trajectories of protein systems that approach the size of the cells, at atomic resolution.

Building Structural Models of a Whole Mycoplasma Cell

Building structural models of entire cells has been a long-standing cross-discipline challenge for the research community, as it requires an unprecedented level of integration between multiple sources of biological data and enhanced methods for computational modeling and visualization. Here, we present the first 3D structural models of an entire Mycoplasma genitalium (MG) cell, built using the CellPACK suite of computational modeling tools. Our model recapitulates the data described in recent whole-cell system biology simulations and provides a structural representation for all MG proteins, DNA and RNA molecules, obtained by combining experimental and homology-modeled structures and lattice-based models of the genome. We establish a framework for gathering, curating and evaluating these structures, exposing current weaknesses of modeling methods and the boundaries of MG structural knowledge, and visualization methods to explore functional characteristics of the genome and proteome. We compare two approaches for data gathering, a manually-curated workflow and an automated workflow that uses homologous structures, both of which are appropriate for the analysis of mesoscale properties such as crowding and volume occupancy. Analysis of model quality provides estimates of the regularization that will be required when these models are used as starting points for atomic molecular dynamics simulations.

Integrative structural modelling and visualisation of a cellular organelle

Models of insulin secretory vesicles from pancreatic beta cells have been created using the cellPACK suite of tools to research, curate, construct and visualise the current state of knowledge. The model integrates experimental information from proteomics, structural biology, cryoelectron microscopy and X-ray tomography, and is used to generate models of mature and immature vesicles. A new method was developed to generate a confidence score that reconciles inconsistencies between three available proteomes using expert annotations of cellular localisation. The models are used to simulate soft X-ray tomograms, allowing quantification of features that are observed in experimental tomograms, and in turn, allowing interpretation of X-ray tomograms at the molecular level.

Understanding the Targeting Mechanisms of Multi-Specific Biologics in Immunotherapy with Multiscale Modeling

Immunotherapeutics are frequently associated with adverse side effects due to the elicitation of global immune modulation. To lower the risk of these side effects, recombinant DNA technology is employed to enhance the selectivity of cell targeting by genetically fusing different biomolecules, yielding new species referred to as multi-specific biologics. The design of new multi-specific biologics is a central challenge for the realization of new immunotherapies. To understand the molecular determinants responsible for regulating the binding between multi-specific biologics and surface-bound membrane receptors, we developed a multiscale computational framework that integrates various simulation approaches covering different timescales and spatial resolutions. Our model system of multi-specific biologics contains two natural ligands of immune receptors, which are covalently tethered by a peptide linker. Using this method, a number of interesting features of multi-specific biologics were identified. Our study therefore provides an important strategy to design the next-generation biologics for immunotherapy.

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Two proteins are connected by different linkers as a model of bispecific biologics

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Conformational dynamics of biologics are captured by microsecond MD simulations

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Coarse-grained simulations are used to test binding between biologics and receptors

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Biologics with long and flexible linkers are more efficient in targeting receptors

Challenges in protein docking

Current developments in protein docking aim at improvement of applicability, accuracy and utility of modeling macromolecular complexes. The challenges include the need for greater emphasis on protein docking to molecules of different types, proper accounting for conformational flexibility upon binding, new promising methodologies based on residue co-evolution and deep learning, affinity prediction, and further development of fully automated docking servers. Importantly, new developments increasingly focus on realistic modeling of protein interactions in vivo, including crowded environment inside a cell, which involves multiple transient encounters, and propagating the system in time. This opinion paper offers the author's perspective on these challenges in structural modeling of protein interactions and the future of protein docking.

Art and Science of the Cellular Mesoscale

Experimental information from microscopy, structural biology, and bioinformatics may be integrated to build structural models of entire cells with molecular detail. This integrative modeling is challenging in several ways: the intrinsic complexity of biology results in models with many closely packed and heterogeneous components; the wealth of available experimental data is scattered among multiple resources and must be gathered, reconciled, and curated; and computational infrastructure is only now gaining the capability of modeling and visualizing systems of this complexity. We present recent efforts to address these challenges, both with artistic approaches to depicting the cellular mesoscale, and development and application of methods to build quantitative models.

Application of docking methodologies to modeled proteins

Protein docking is essential for structural characterization of protein interactions. Besides providing the structure of protein complexes, modeling of proteins and their complexes is important for understanding the fundamental principles and specific aspects of protein interactions. The accuracy of protein modeling, in general, is still less than that of the experimental approaches. Thus, it is important to investigate the applicability of docking techniques to modeled proteins. We present new comprehensive benchmark sets of protein models for the development and validation of protein docking, as well as a systematic assessment of free and template-based docking techniques on these sets. As opposed to previous studies, the benchmark sets reflect the real case modeling/docking scenario where the accuracy of the models is assessed by the modeling procedure, without reference to the native structure (which would be unknown in practical applications). We also expanded the analysis to include docking of protein pairs where proteins have different structural accuracy. The results show that, in general, the template-based docking is less sensitive to the structural inaccuracies of the models than the free docking. The near-native docking poses generated by the template-based approach, typically, also have higher ranks than those produces by the free docking (although the free docking is indispensable in modeling the multiplicity of protein interactions in a crowded cellular environment). The results show that docking techniques are applicable to protein models in a broad range of modeling accuracy. The study provides clear guidelines for practical applications of docking to protein models.

Sharing Data from Molecular Simulations

Given the need for modern researchers to produce open, reproducible scientific output, the lack of standards and best practices for sharing data and workflows used to produce and analyze molecular dynamics (MD) simulations has become an important issue in the field. There are now multiple well-established packages to perform molecular dynamics simulations, often highly tuned for exploiting specific classes of hardware, each with strong communities surrounding them, but with very limited interoperability/transferability options. Thus, the choice of the software package often dictates the workflow for both simulation production and analysis. The level of detail in documenting the workflows and analysis code varies greatly in published work, hindering reproducibility of the reported results and the ability for other researchers to build on these studies. An increasing number of researchers are motivated to make their data available, but many challenges remain in order to effectively share and reuse simulation data. To discuss these and other issues related to best practices in the field in general, we organized a workshop in November 2018 ( https://bioexcel.eu/events/workshop-on-sharing-data-from-molecular-simulations/ ). Here, we present a brief overview of this workshop and topics discussed. We hope this effort will spark further conversation in the MD community to pave the way toward more open, interoperable, and reproducible outputs coming from research studies using MD simulations.

Whole-Cell Models and Simulations in Molecular Detail

Comprehensive data about the composition and structure of cellular components have enabled the construction of quantitative whole-cell models. While kinetic network-type models have been established, it is also becoming possible to build physical, molecular-level models of cellular environments. This review outlines challenges in constructing and simulating such models and discusses near- and long-term opportunities for developing physical whole-cell models that can connect molecular structure with biological function.

Molecular Graphics: Bridging Structural Biologists and Computer Scientists

Visualization of molecular structures is one of the most common tasks carried out by structural biologists, typically using software, such as Chimera, COOT, PyMOL, or VMD. In this Perspective article, we outline how past developments in computer graphics and data visualization have expanded the understanding of biomolecular function, and we summarize recent advances that promise to further transform structural biology. We also highlight how progress in molecular graphics has been impeded by communication barriers between two communities: the computer scientists driving these advances, and the structural and computational biologists who stand to benefit. By pointing to canonical papers and explaining technical progress underlying new graphical developments in simple terms, we aim to improve communication between these communities; this, in turn, would help shape future developments in molecular graphics.

Symmetry at the Cellular Mesoscale

Symmetry plays a functional role in the structure and action of biomolecules and their associations and interactions in living cells. This symmetry is a natural consequence of the evolutionary mechanisms that lead to the development of life, and it ranges from perfect point-group symmetry in protein oligomers to more approximate symmetries in the higher-order mesoscale structure of cellular environments.

Modeling Crowded Environment in Molecular Simulations

Biomolecules perform their various functions in living cells, namely in an environment that is crowded by many macromolecules. Thus, simulating the dynamics and interactions of biomolecules should take into account not only water and ions but also other binding partners, metabolites, lipids and macromolecules found in cells. In the last decade, research on how to model macromolecular crowders around proteins in order to simulate their dynamics in models of cellular environments has gained a lot of attention. In this mini-review we focus on the models of crowding agents that have been used in computer modeling studies of proteins and peptides, especially via molecular dynamics simulations.

Computational approaches to macromolecular interactions in the cell

Structural modeling of a cell is an evolving strategic direction in computational structural biology. It takes advantage of new powerful modeling techniques, deeper understanding of fundamental principles of molecular structure and assembly, and rapid growth of the amount of structural data generated by experimental techniques. Key modeling approaches to principal types of macromolecular assemblies in a cell already exist. The main challenge, along with the further development of these modeling approaches, is putting them together in a consistent, unified whole cell model. This opinion piece addresses the fundamental aspects of modeling macromolecular assemblies in a cell, and the state-of-the-art in modeling of the principal types of such assemblies.

Communicating Genome Architecture: Biovisualization of the Genome, from Data Analysis and Hypothesis Generation to Communication and Learning

Genome discoveries at the core of biology are made by visual description and exploration of the cell, from microscopic sketches and biochemical mapping to computational analysis and spatial modeling. We outline the experimental and visualization techniques that have been developed recently which capture the three-dimensional interactions regulating how genes are expressed. We detail the challenges faced in integration of the data to portray the components and organization and their dynamic landscape. The goal is more than a single data-driven representation as interactive visualization for de novo research is paramount to decipher insights on genome organization in space.

CellPAINT: Interactive Illustration of Dynamic Mesoscale Cellular Environments

CellPAINT allows nonexpert users to create interactive mesoscale illustrations that integrate a variety of biological data. Like popular digital painting software, scenes are created using a palette of molecular "brushes." The current release allows creation of animated scenes with an HIV virion, blood plasma, and a simplified T-cell.

From Atoms to Cells: Using Mesoscale Landscapes to Construct Visual Narratives

Modeling and visualization of the cellular mesoscale, bridging the nanometer scale of molecules to the micrometer scale of cells, is being studied by an integrative approach. Data from structural biology, proteomics, and microscopy are combined to simulate the molecular structure of living cells. These cellular landscapes are used as research tools for hypothesis generation and testing, and to present visual narratives of the cellular context of molecular biology for dissemination, education, and outreach.

Perspectives on Structural Molecular Biology Visualization: From Past to Present

Visualization has been a key technology in the progress of structural molecular biology for as long as the field has existed. This perspective describes the nature of the visualization process in structural studies, how it has evolved over the years, and its relationship to the changes in technology that have supported and driven it. It focuses on how technical advances have changed the way we look at and interact with molecular structure, and how structural biology has fostered and challenged that technology.

Integrating Structural Information to Study the Dynamics of Protein-Protein Interactions in Cells

The information of how two proteins interact is embedded in the atomic details of their binding interfaces. These interactions, spatial-temporally coordinating each other as a network in a variable cytoplasmic environment, dominate almost all biological functions. A feasible and reliable computational model is highly demanded to realistically simulate these cellular processes and unravel the complexities beneath them. We therefore present a multiscale framework that integrates simulations on two different scales. The higher-resolution model incorporates structural information of proteins and energetics of their binding, while the lower-resolution model uses a highly simplified representation of proteins to capture the long-time-scale dynamics of a system with multiple proteins. Through a systematic benchmark test and two practical applications of biomolecular systems with specific cellular functions, we demonstrated that this method could be a powerful approach to understand molecular mechanisms of dynamic interactions between biomolecules and their functional impacts with high computational efficiency.

Modeling and simulating networks of interdependent protein interactions

Protein interactions are fundamental building blocks of biochemical reaction systems underlying cellular functions. The complexity and functionality of these systems emerge not only from the protein interactions themselves but also from the dependencies between these interactions, as generated by allosteric effects or mutual exclusion due to steric hindrance. Therefore, formal models for integrating and utilizing information about interaction dependencies are of high interest. Here, we describe an approach for endowing protein networks with interaction dependencies using propositional logic, thereby obtaining constrained protein interaction networks ("constrained networks"). The construction of these networks is based on public interaction databases as well as text-mined information about interaction dependencies. We present an efficient data structure and algorithm to simulate protein complex formation in constrained networks. The efficiency of the model allows fast simulation and facilitates the analysis of many proteins in large networks. In addition, this approach enables the simulation of perturbation effects, such as knockout of single or multiple proteins and changes of protein concentrations. We illustrate how our model can be used to analyze a constrained human adhesome protein network, which is responsible for the formation of diverse and dynamic cell-matrix adhesion sites. By comparing protein complex formation under known interaction dependencies versus without dependencies, we investigate how these dependencies shape the resulting repertoire of protein complexes. Furthermore, our model enables investigating how the interplay of network topology with interaction dependencies influences the propagation of perturbation effects across a large biochemical system. Our simulation software CPINSim (for Constrained Protein Interaction Network Simulator) is available under the MIT license at http://github.com/BiancaStoecker/cpinsim and as a Bioconda package (https://bioconda.github.io).

Opportunities and Challenges in Building a Spatiotemporal Multi-scale Model of the Human Pancreatic β Cell

The construction of a predictive model of an entire eukaryotic cell that describes its dynamic structure from atomic to cellular scales is a grand challenge at the intersection of biology, chemistry, physics, and computer science. Having such a model will open new dimensions in biological research and accelerate healthcare advancements. Developing the necessary experimental and modeling methods presents abundant opportunities for a community effort to realize this goal. Here, we present a vision for creation of a spatiotemporal multi-scale model of the pancreatic β-cell, a relevant target for understanding and modulating the pathogenesis of diabetes.

Lattice Models of Bacterial Nucleoids

Mesoscale molecular modeling is providing a new window into the inner workings of living cells. Modeling of genomes, however, remains a technical challenge, due to their large size and complexity. We describe a lattice method for rapid generation of bacterial nucleoid models that integrates experimental data from a variety of biophysical techniques and provides a starting point for simulation and hypothesis generation. The current method builds models of a circular bacterial genome with supercoiled plectonemes, packed within the small space of the bacterial cell. Lattice models are generated for Mycoplasma genitalium and Escherichia coli nucleoids, and used to simulate interaction data. The method is rapid enough to allow generation of multiple models when analyzing structure/function relationships, and we demonstrate use of the lattice models in creation of an all-atom representation of an entire cell.

Instant Construction and Visualization of Crowded Biological Environments

We present the first approach to integrative structural modeling of the biological mesoscale within an interactive visual environment. These complex models can comprise up to millions of molecules with defined atomic structures, locations, and interactions. Their construction has previously been attempted only within a non-visual and non-interactive environment. Our solution unites the modeling and visualization aspect, enabling interactive construction of atomic resolution mesoscale models of large portions of a cell. We present a novel set of GPU algorithms that build the basis for the rapid construction of complex biological structures. These structures consist of multiple membrane-enclosed compartments including both soluble molecules and fibrous structures. The compartments are defined using volume voxelization of triangulated meshes. For membranes, we present an extension of the Wang Tile concept that populates the bilayer with individual lipids. Soluble molecules are populated within compartments distributed according to a Halton sequence. Fibrous structures, such as RNA or actin filaments, are created by self-avoiding random walks. Resulting overlaps of molecules are resolved by a forced-based system. Our approach opens new possibilities to the world of interactive construction of cellular compartments. We demonstrate its effectiveness by showcasing scenes of different scale and complexity that comprise blood plasma, mycoplasma, and HIV.

Protein-protein interactions in paralogues: Electrostatics modulates specificity on a conserved steric scaffold

An improved knowledge of protein-protein interactions is essential for better understanding of metabolic and signaling networks, and cellular function. Progress tends to be based on structure determination and predictions using known structures, along with computational methods based on evolutionary information or detailed atomistic descriptions. We hypothesized that for the case of interactions across a common interface, between proteins from a pair of paralogue families or within a family of paralogues, a relatively simple interface description could distinguish between binding and non-binding pairs. Using binding data for several systems, and large-scale comparative modeling based on known template complex structures, it is found that charge-charge interactions (for groups bearing net charge) are generally a better discriminant than buried non-polar surface. This is particularly the case for paralogue families that are less divergent, with more reliable comparative modeling. We suggest that electrostatic interactions are major determinants of specificity in such systems, an observation that could be used to predict binding partners.

Challenges for visualizing three-dimensional data in genomic browsers

Genomic interactions reveal the spatial organization of genomes and genomic domains, which is known to play key roles in cell function. Physical proximity can be represented as two-dimensional heat maps or matrices. From these, three-dimensional (3D) conformations of chromatin can be computed revealing coherent structures that highlight the importance of nonsequential relationships across genomic features. Mainstream genomic browsers have been classically developed to display compact, stacked tracks based on a linear, sequential, per-chromosome coordinate system. Genome-wide comparative analysis demands new approaches to data access and new layouts for analysis. The legibility can be compromised when displaying track-aligned second dimension matrices, which require greater screen space. Moreover, 3D representations of genomes defy vertical alignment in track-based genome browsers. Furthermore, investigation at previously unattainable levels of detail is revealing multiscale, multistate, time-dependent complexity. This article outlines how these challenges are currently handled in mainstream browsers as well as how novel techniques in visualization are being explored to address them. A set of requirements for coherent visualization of novel spatial genomic data is defined and the resulting potential for whole genome visualization is described.

Crowding in Cellular Environments at an Atomistic Level from Computer Simulations

The effects of crowding in biological environments on biomolecular structure, dynamics, and function remain not well understood. Computer simulations of atomistic models of concentrated peptide and protein systems at different levels of complexity are beginning to provide new insights. Crowding, weak interactions with other macromolecules and metabolites, and altered solvent properties within cellular environments appear to remodel the energy landscape of peptides and proteins in significant ways including the possibility of native state destabilization. Crowding is also seen to affect dynamic properties, both conformational dynamics and diffusional properties of macromolecules. Recent simulations that address these questions are reviewed here and discussed in the context of relevant experiments.

Cell polarity in plants: the Yin and Yang of cellular functions

Spatial organization is fundamental for the performance of living organisms and is reflected in a distinct distribution of structures and molecules down to the subcellular level. In particular, eukaryotic cells harbor a vast range of possibilities for distributing organelles, the cytoskeleton or the extracellular matrix in an active and highly regulated manner. An asymmetric or polar distribution is rather the rule than the exception and often reflects a particular position or orientation of a cell within a multicellular body. Here, we highlight recent insights into the regulation of cell polarity in plants and reveal the interactive nature of underlying molecular processes.

Three-dimensional ultrastructural analysis of cells in the periodontal ligament using focused ion beam/scanning electron microscope tomography

The accurate comprehension of normal tissue provides essential data to analyse abnormalities such as disease and regenerative processes. In addition, understanding the proper structure of the target tissue and its microenvironment may facilitate successful novel treatment strategies. Many studies have examined the nature and structure of periodontal ligaments (PDLs); however, the three-dimensional (3D) structure of cells in normal PDLs remains poorly understood. In this study, we used focused ion beam/scanning electron microscope tomography to investigate the whole 3D ultrastructure of PDL cells along with quantitatively analysing their structural properties and ascertaining their orientation to the direction of the collagen fibre. PDL cells were shown to be in contact with each other, forming a widespread mesh-like network between the cementum and the alveolar bone. The volume of the cells in the horizontal fibre area was significantly larger than in other areas, whereas the anisotropy of these cells was lower than in other areas. Furthermore, the orientation of cells to the PDL fibres was not parallel to the PDL fibres in each area. As similar evaluations are recognized as being challenging using conventional two-dimensional methods, these novel 3D findings may contribute necessary knowledge for the comprehensive understanding and analysis of PDLs.

Biomolecular interactions modulate macromolecular structure and dynamics in atomistic model of a bacterial cytoplasm

Biological macromolecules function in highly crowded cellular environments. The structure and dynamics of proteins and nucleic acids are well characterized in vitro, but in vivo crowding effects remain unclear. Using molecular dynamics simulations of a comprehensive atomistic model cytoplasm we found that protein-protein interactions may destabilize native protein structures, whereas metabolite interactions may induce more compact states due to electrostatic screening. Protein-protein interactions also resulted in significant variations in reduced macromolecular diffusion under crowded conditions, while metabolites exhibited significant two-dimensional surface diffusion and altered protein-ligand binding that may reduce the effective concentration of metabolites and ligands in vivo. Metabolic enzymes showed weak non-specific association in cellular environments attributed to solvation and entropic effects. These effects are expected to have broad implications for the in vivo functioning of biomolecules. This work is a first step towards physically realistic in silico whole-cell models that connect molecular with cellular biology.

Protein folding, binding, and droplet formation in cell-like conditions

The many bystander macromolecules in the crowded cellular environments present both steric repulsion and weak attraction to proteins undergoing folding or binding and hence impact the thermodynamic and kinetic properties of these processes. The weak but nonrandom binding with bystander macromolecules may facilitate subcellular localization and biological function. Weak binding also leads to the emergence of a protein-rich droplet phase, which has been implicated in regulating a variety of cellular functions. All these important problems can now be addressed by realistic modeling of intermolecular interactions. Configurational sampling of concentrated protein solutions is an ongoing challenge.