Recent advances in coarse-grained modeling of virus assembly

Hexagonal Lattices of HIV Capsid Proteins Explored by Simulations Based on a Thermodynamically Consistent Model

HIV capsid proteins (CAs) may self-assemble into a variety of shapes under in vivo and in vitro conditions. Here, we employed simulations based on a residue-level coarse-grained (CG) model with full conformational flexibility to investigate hexagonal lattices, which are the underlying structural pattern for CA aggregations. Facilitated by enhanced sampling simulations to rigorously calculate CA dimerization and polymerization affinities, we calibrated our model to reproduce the experimentally measured affinities. Using the calibrated model, we performed unbiased simulations on several large systems consisting of 1512 CA subunits, allowing reversible binding and unbinding of the CAs in a thermodynamically consistent manner. In one simulation, a preassembled hexagonal CA sheet developed spontaneous curvatures reminiscent of those observed in experiments, and the edges of the sheet exhibited local curvatures larger than those of the interior. In other simulations starting with randomly distributed CAs at different concentrations, existing CA assemblies grew by binding free capsomeres to the edges and by merging with other assemblies. At high CA concentrations, rapid establishment of predominant aggregates was followed by much slower adjustments toward more regular hexagonal lattices, with increasing numbers of intact CA hexamers and pentamers being formed. Our approach of adapting a general CG model to specific systems by using experimental binding data represents a practical and effective strategy for simulating and elucidating intricate protein aggregations.

Mechanisms of allostery at the viral surface through the eyes of molecular simulation

The outermost surface layer of any virus is formed by either a capsid shell or envelope. Such layers have traditionally been thought of as immovable structures, but it is becoming apparent that they cannot be viewed exclusively as static architectures protecting the viral genome. A limited number of proteins on the virion surface must perform a multitude of functions in order to orchestrate the viral life cycle, and allostery can regulate their structures at multiple levels of organization, spanning individual molecules, protomers, large oligomeric assemblies, or entire viral surfaces. Here, we review recent contributions from the molecular simulation field to viral surface allostery, with a particular focus on the trimeric spike glycoprotein emerging from the coronavirus surface, and the icosahedral flaviviral envelope complex. As emerging viral pathogens continue to pose a global threat, an improved understanding of viral dynamics and allosteric regulation will prove crucial in developing novel therapeutic strategies.

Cooperative Self-Assembly Process Involving Giant Toroidal Polyoxometalate as a Membrane Building Block in Nanoscale Vesicles

The self-assembly of organic amphiphilic species into various aggregates such as spherical or elongated micelles and cylinders up to the formation of lyotropic hexagonal or lamellar phases results from cooperative processes orchestrated by the hydrophobic effect, while those involving ionic inorganic polynuclear entities and nonionic organic components are still intriguing. Herein, we report on the supramolecular behavior of giant toroidal molybdenum blue-type polyoxometalate, namely, the {Mo154} species in the presence of n-octyl-β-glucoside (C8G1), widely used as a surfactant in biochemistry. Structural investigations were carried out using a set of complementary multiscale methods including single-crystal X-ray diffraction analysis supported by molecular modeling, small-angle X-ray scattering and cryo-TEM observations. In addition, liquid NMR, viscosimetry, surface tension measurement, and isothermal titration calorimetry provided further information to decipher the complex aggregation pathway. Elucidation of the assembly process reveals a rich scenario where the presence of the large {Mo154} anion disrupts the self-assembly of the C8G1, well-known to produce micelles, and induces striking successive phase transitions from fluid-to-gel and from gel-to-fluid. Herein, intimate organic-inorganic primary interactions arising from the superchaotropic nature of the {Mo154} lead to versatile nanoscopic hybrid C8G1-{Mo154} aggregates including crystalline discrete assemblies, smectic lamellar liquid crystals, and large uni- or multilamellar vesicles where the large torus {Mo154} acts a trans-membrane component.

Effect of ionic strength on the assembly of simian vacuolating virus capsid protein around poly(styrene sulfonate)

Virus-like particles (VLPs) are noninfectious nanocapsules that can be used for drug delivery or vaccine applications. VLPs can be assembled from virus capsid proteins around a condensing agent, such as RNA, DNA, or a charged polymer. Electrostatic interactions play an important role in the assembly reaction. VLPs assemble from many copies of capsid protein, with a combinatorial number of intermediates. Hence, the mechanism of the reaction is poorly understood. In this paper, we combined solution small-angle X-ray scattering (SAXS), cryo-transmission electron microscopy (TEM), and computational modeling to determine the effect of ionic strength on the assembly of Simian Vacuolating Virus 40 (SV40)-like particles. We mixed poly(styrene sulfonate) with SV40 capsid protein pentamers at different ionic strengths. We then characterized the assembly product by SAXS and cryo-TEM. To analyze the data, we performed Langevin dynamics simulations using a coarse-grained model that revealed incomplete, asymmetric VLP structures consistent with the experimental data. We found that close to physiological ionic strength, [Formula: see text] VLPs coexisted with VP1 pentamers. At lower or higher ionic strengths, incomplete particles coexisted with pentamers and [Formula: see text] particles. Including the simulated structures was essential to explain the SAXS data in a manner that is consistent with the cryo-TEM images.

Kinetic Growth of Multicomponent Microcompartment Shells

An important goal of systems and synthetic biology is to produce high value chemical species in large quantities. Microcompartments, which are protein nanoshells encapsulating catalytic enzyme cargo, could potentially function as tunable nanobioreactors inside and outside cells to generate these high value species. Modifying the morphology of microcompartments through genetic engineering of shell proteins is one viable strategy to tune cofactor and metabolite access to encapsulated enzymes. However, this is a difficult task without understanding how changing interactions between the many different types of shell proteins and enzymes affect microcompartment assembly and shape. Here, we use multiscale molecular dynamics and experimental data to describe assembly pathways available to microcompartments composed of multiple types of shell proteins with varied interactions. As the average interaction between the enzyme cargo and the multiple types of shell proteins is weakened, the shell assembly pathway transitions from (i) nucleating on the enzyme cargo to (ii) nucleating in the bulk and then binding the cargo as it grows to (iii) an empty shell. Atomistic simulations and experiments using the 1,2-propanediol utilization microcompartment system demonstrate that shell protein interactions are highly varied and consistent with our multicomponent, coarse-grained model. Furthermore, our results suggest that intrinsic bending angles control the size of these microcompartments. Overall, our simulations and experiments provide guidance to control microcomparmtent size and assembly by modulating the interactions between shell proteins.

Mechanical fatigue testing in silico: Dynamic evolution of material properties of nanoscale biological particles

Biological particles have evolved to possess mechanical characteristics necessary to carry out their functions. We developed a computational approach to "fatigue testing in silico", in which constant-amplitude cyclic loading is applied to a particle to explore its mechanobiology. We used this approach to describe dynamic evolution of nanomaterial properties and low-cycle fatigue in the thin spherical encapsulin shell, thick spherical Cowpea Chlorotic Mottle Virus (CCMV) capsid, and thick cylindrical microtubule (MT) fragment over 20 cycles of deformation. Changing structures and force-deformation curves enabled us to describe their damage-dependent biomechanics (strength, deformability, stiffness), thermodynamics (released and dissipated energies, enthalpy, and entropy) and material properties (toughness). Thick CCMV and MT particles experience material fatigue due to slow recovery and damage accumulation over 3-5 loading cycles; thin encapsulin shells show little fatigue due to rapid remodeling and limited damage. The results obtained challenge the existing paradigm: damage in biological particles is partially reversible owing to particle's partial recovery; fatigue crack may or may not grow with each loading cycle and may heal; and particles adapt to deformation amplitude and frequency to minimize the energy dissipated. Using crack size to quantitate damage is problematic as several cracks might form simultaneously in a particle. Dynamic evolution of strength, deformability, and stiffness, can be predicted by analyzing the cycle number (N) dependent damage, [Formula: see text] , where α is a power law and Nf is fatigue life. Fatigue testing in silico can now be used to explore damage-induced changes in the material properties of other biological particles. STATEMENT OF SIGNIFICANCE: Biological particles possess mechanical characteristics necessary to perform their functions. We developed "fatigue testing in silico" approach, which employes Langevin Dynamics simulations of constant-amplitude cyclic loading of nanoscale biological particles, to explore dynamic evolution of the mechanical, energetic, and material properties of the thin and thick spherical particles of encapsulin and Cowpea Chlorotic Mottle Virus, and the microtubule filament fragment. Our study of damage growth and fatigue development challenge the existing paradigm. Damage in biological particles is partially reversible as fatigue crack might heal with each loading cycle. Particles adapt to deformation amplitude and frequency to minimize energy dissipation. The evolution of strength, deformability, and stiffness, can be accurately predicted by analyzing the damage growth in particle structure.

Molecular Dynamics Simulations of Deformable Viral Capsomers

Most coarse-grained models of individual capsomers associated with viruses employ rigid building blocks that do not exhibit shape adaptation during self-assembly. We develop a coarse-grained general model of viral capsomers that incorporates their stretching and bending energies while retaining many features of the rigid-body models, including an overall trapezoidal shape with attractive interaction sites embedded in the lateral walls to favor icosahedral capsid assembly. Molecular dynamics simulations of deformable capsomers reproduce the rich self-assembly behavior associated with a general T=1 icosahedral virus system in the absence of a genome. Transitions from non-assembled configurations to icosahedral capsids to kinetically-trapped malformed structures are observed as the steric attraction between capsomers is increased. An assembly diagram in the space of capsomer-capsomer steric attraction and capsomer deformability reveals that assembling capsomers of higher deformability into capsids requires increasingly large steric attraction between capsomers. Increasing capsomer deformability can reverse incorrect capsomer-capsomer binding, facilitating transitions from malformed structures to symmetric capsids; however, making capsomers too soft inhibits assembly and yields fluid-like structures.

Self-assembly coupled to liquid-liquid phase separation

Liquid condensate droplets with distinct compositions of proteins and nucleic acids are widespread in biological cells. While it is known that such droplets, or compartments, can regulate irreversible protein aggregation, their effect on reversible self-assembly remains largely unexplored. In this article, we use kinetic theory and solution thermodynamics to investigate the effect of liquid-liquid phase separation on the reversible self-assembly of structures with well-defined sizes and architectures. We find that, when assembling subunits preferentially partition into liquid compartments, robustness against kinetic traps and maximum achievable assembly rates can be significantly increased. In particular, both the range of solution conditions leading to productive assembly and the corresponding assembly rates can increase by orders of magnitude. We analyze the rate equation predictions using simple scaling estimates to identify effects of liquid-liquid phase separation as a function of relevant control parameters. These results may elucidate self-assembly processes that underlie normal cellular functions or pathogenesis, and suggest strategies for designing efficient bottom-up assembly for nanomaterials applications.

Performance efficient macromolecular mechanics via sub-nanometer shape based coarse graining

Dimensionality reduction via coarse grain modeling is a valuable tool in biomolecular research. For large assemblies, ultra coarse models are often knowledge-based, relying on a priori information to parameterize models thus hindering general predictive capability. Here, we present substantial advances to the shape based coarse graining (SBCG) method, which we refer to as SBCG2. SBCG2 utilizes a revitalized formulation of the topology representing network which makes high-granularity modeling possible, preserving atomistic details that maintain assembly characteristics. Further, we present a method of granularity selection based on charge density Fourier Shell Correlation and have additionally developed a refinement method to optimize, adjust and validate high-granularity models. We demonstrate our approach with the conical HIV-1 capsid and heteromultimeric cofilin-2 bound actin filaments. Our approach is available in the Visual Molecular Dynamics (VMD) software suite, and employs a CHARMM-compatible Hamiltonian that enables high-performance simulation in the GPU-resident NAMD3 molecular dynamics engine.

Computer-aided drug design in seeking viral capsid modulators

Approved or licensed antiviral drugs have limited applications because of their drug resistance and severe adverse effects. By contrast, by stabilizing or destroying the viral capsid, compounds known as capsid modulators prevent viral replication by acting on new targets and, therefore, overcoming the problem of clinical drug resistance. For example. computer-aided drug design (CADD) methods, using strategies based on structures of biological targets (structure-based drug design; SBDD), such as docking, molecular dynamics (MD) simulations, and virtual screening (VS), have provided opportunities for fast and effective development of viral capsid modulators. In this review, we summarize the application of CADD in the discovery, optimization, and mechanism prediction of capsid-targeting small molecules, providing new insights into antiviral drug discovery modalities. Teaser: Computer-aided drug design will accelerate the development of viral capsid regulators, which brings new hope for the treatment of refractory viral diseases.

Single-particle studies of the effects of RNA-protein interactions on the self-assembly of RNA virus particles

Understanding the pathways by which simple RNA viruses self-assemble from their coat proteins and RNA is of practical and fundamental interest. Although RNA-protein interactions are thought to play a critical role in the assembly, our understanding of their effects is limited because the assembly process is difficult to observe directly. We address this problem by using interferometric scattering microscopy, a sensitive optical technique with high dynamic range, to follow the in vitro assembly kinetics of more than 500 individual particles of brome mosaic virus (BMV)-for which RNA-protein interactions can be controlled by varying the ionic strength of the buffer. We find that when RNA-protein interactions are weak, BMV assembles by a nucleation-and-growth pathway in which a small cluster of RNA-bound proteins must exceed a critical size before additional proteins can bind. As the strength of RNA-protein interactions increases, the nucleation time becomes shorter and more narrowly distributed, but the time to grow a capsid after nucleation is largely unaffected. These results suggest that the nucleation rate is controlled by RNA-protein interactions, while the growth process is driven less by RNA-protein interactions and more by protein-protein interactions and intraprotein forces. The nucleated pathway observed with the plant virus BMV is strikingly similar to that previously observed with bacteriophage MS2, a phylogenetically distinct virus with a different host kingdom. These results raise the possibility that nucleated assembly pathways might be common to other RNA viruses.

Multiscale Modeling of Hepatitis B Virus Capsid Assembly and Its Dimorphism

Hepatitis B virus (HBV) is an endemic, chronic virus that leads to 800000 deaths per year. Central to the HBV lifecycle, the viral core has a protein capsid assembled from many copies of a single protein. The capsid protein adopts different (quasi-equivalent) conformations to form icosahedral capsids containing 180 or 240 proteins: T = 3 or T = 4, respectively, in Caspar-Klug nomenclature. HBV capsid assembly has become an important target for recently developed antivirals; nonetheless, the assembly pathways and mechanisms that control HBV dimorphism remain unclear. We describe computer simulations of the HBV assembly, using a coarse-grained model that has parameters learned from all-atom molecular dynamics simulations of a complete HBV capsid and yet is computationally tractable. Dynamical simulations with the resulting model reproduce experimental observations of HBV assembly pathways and products. By constructing Markov state models and employing transition path theory, we identify pathways leading to T = 3, T = 4, and other experimentally observed capsid morphologies. The analysis shows that capsid polymorphism is promoted by the low HBV capsid bending modulus, where the key factors controlling polymorphism are the conformational energy landscape and protein-protein binding affinities.

Polymorphic self-assembly of helical tubules is kinetically controlled

In contrast to most self-assembling synthetic materials, which undergo unbounded growth, many biological self-assembly processes are self-limited. That is, the assembled structures have one or more finite dimensions that are much larger than the size scale of the individual monomers. In many such cases, the finite dimension is selected by a preferred curvature of the monomers, which leads to self-closure of the assembly. In this article, we study an example class of self-closing assemblies: cylindrical tubules that assemble from triangular monomers. By combining kinetic Monte Carlo simulations, free energy calculations, and simple theoretical models, we show that a range of programmable size scales can be targeted by controlling the intricate balance between the preferred curvature of the monomers and their interaction strengths. However, their assembly is kinetically controlled-the tubule morphology is essentially fixed shortly after closure, resulting in a distribution of tubule widths that is significantly broader than the equilibrium distribution. We develop a simple kinetic model based on this observation and the underlying free-energy landscape of assembling tubules that quantitatively describes the distributions. Our results are consistent with recent experimental observations of tubule assembly from triangular DNA origami monomers. The modeling framework elucidates design principles for assembling self-limited structures from synthetic components, such as artificial microtubules that have a desired width and chirality.

Microcompartment assembly around multicomponent fluid cargoes

This article describes dynamical simulations of the assembly of an icosahedral protein shell around a bicomponent fluid cargo. Our simulations are motivated by bacterial microcompartments, which are protein shells found in bacteria that assemble around a complex of enzymes and other components involved in certain metabolic processes. The simulations demonstrate that the relative interaction strengths among the different cargo species play a key role in determining the amount of each species that is encapsulated, their spatial organization, and the nature of the shell assembly pathways. However, the shell protein–shell protein and shell protein–cargo component interactions that help drive assembly and encapsulation also influence cargo composition within certain parameter regimes. These behaviors are governed by a combination of thermodynamic and kinetic effects. In addition to elucidating how natural microcompartments encapsulate multiple components involved within reaction cascades, these results have implications for efforts in synthetic biology to colocalize alternative sets of molecules within microcompartments to accelerate specific reactions. More broadly, the results suggest that coupling between self-assembly and multicomponent liquid–liquid phase separation may play a role in the organization of the cellular cytoplasm.

Thermodynamic Size Control in Curvature-Frustrated Tubules: Self-Limitation with Open Boundaries

We use computational modeling to investigate the assembly thermodynamics of a particle-based model for geometrically frustrated assembly, in which the local packing geometry of subunits is incompatible with uniform, strain-free large-scale assembly. The model considers discrete triangular subunits that drive assembly toward a closed, hexagonal-ordered tubule, but have geometries that locally favor negative Gaussian curvature. We use dynamical Monte Carlo simulations and enhanced sampling methods to compute the free energy landscape and corresponding self-assembly behavior as a function of experimentally accessible parameters that control assembly driving forces and the magnitude of frustration. The results determine the parameter range where finite-temperature self-limiting assembly occurs, in which the equilibrium assembly size distribution is sharply peaked around a well-defined finite size. The simulations also identify two mechanisms by which the system can escape frustration and assemble to unlimited size, and determine the particle-scale properties of subunits that suppress unbounded growth.

Molecular Modeling in Anion Exchange Membrane Research: A Brief Review of Recent Applications

Anion Exchange Membrane (AEM) fuel cells have attracted growing interest, due to their encouraging advantages, including high power density and relatively low cost. AEM is a polymer matrix, which conducts hydroxide (OH−) ions, prevents physical contact of electrodes, and has positively charged head groups (mainly quaternary ammonium (QA) groups), covalently bound to the polymer backbone. The chemical instability of the quaternary ammonium (QA)-based head groups, at alkaline pH and elevated temperature, is a significant threshold in AEMFC technology. This review work aims to introduce recent studies on the chemical stability of various QA-based head groups and transportation of OH− ions in AEMFC, via modeling and simulation techniques, at different scales. It starts by introducing the fundamental theories behind AEM-based fuel-cell technology. In the main body of this review, we present selected computational studies that deal with the effects of various parameters on AEMs, via a variety of multi-length and multi-time-scale modeling and simulation methods. Such methods include electronic structure calculations via the quantum Density Functional Theory (DFT), ab initio, classical all-atom Molecular Dynamics (MD) simulations, and coarse-grained MD simulations. The explored processing and structural parameters include temperature, hydration levels, several QA-based head groups, various types of QA-based head groups and backbones, etc. Nowadays, many methods and software packages for molecular and materials modeling are available. Applications of such methods may help to understand the transportation mechanisms of OH− ions, the chemical stability of functional head groups, and many other relevant properties, leading to a performance-based molecular and structure design as well as, ultimately, improved AEM-based fuel cell performances. This contribution aims to introduce those molecular modeling methods and their recent applications to the AEM-based fuel cells research community.

Local rules for the self-assembly of a non-quasi-equivalent viral capsid

The structures of many large bacteriophages, such as the P23-77 capsids, do not adhere strictly to the quasi-equivalence principle of viral architecture. Although the general architecture of the P23-77 capsids is classed as T=28d, it self-assembles from multiple copies of two types of coat protein subunits, and the resulting hexameric capsomers do not conform to the Caspar-Klug paradigm. There are two types of hexamers with distinct internal organization, that are located at specific positions in the capsid. It is an open problem which assembly mechanism can lead to such a complex capsid organization. Here we propose a simple set of local rules that can explain how such non-quasi-equivalent capsid structures can arise as a result of self-assembly.

Protein Cages: From Fundamentals to Advanced Applications

Proteins that self-assemble into polyhedral shell-like structures are useful molecular containers both in nature and in the laboratory. Here we review efforts to repurpose diverse protein cages, including viral capsids, ferritins, bacterial microcompartments, and designed capsules, as vaccines, drug delivery vehicles, targeted imaging agents, nanoreactors, templates for controlled materials synthesis, building blocks for higher-order architectures, and more. A deep understanding of the principles underlying the construction, function, and evolution of natural systems has been key to tailoring selective cargo encapsulation and interactions with both biological systems and synthetic materials through protein engineering and directed evolution. The ability to adapt and design increasingly sophisticated capsid structures and functions stands to benefit the fields of catalysis, materials science, and medicine.

Harnessing physicochemical properties of virus capsids for designing enzyme confined nanocompartments

Viruses have drawn significant scientific interest from a wide variety of disciplines beyond virology because of their elegant architectures and delicately balanced activities. A virus-like particle (VLP), a noninfectious protein cage derived from viruses or other cage-forming proteins, has been exploited as a nano-scale platform for bioinspired engineering and synthetic manipulation with a range of applications. Encapsulation of functional proteins, especially enzymes, is an emerging use of VLPs that is promising not only for developing efficient and robust catalytic materials, but also for providing fundamental insights into the effects of enzyme compartmentalization commonly observed in cells. This review highlights recent advances in employing VLPs as a container for confining enzymes. To accomplish larger and more controlled enzyme loading, various different enzyme encapsulation strategies have been developed; many of these strategies are inspired from assembly and genome loading mechanisms of viral capsids. Characterization of VLPs’ physicochemical properties, such as porosity, could lead to rational manipulation and a better understanding of the catalytic behavior of the materials.

Cargo Release from Nonenveloped Viruses and Virus-like Nanoparticles: Capsid Rupture or Pore Formation

Virus-like nanoparticles are protein shells similar to wild-type viruses, and both aim to deliver their content into a cell. Unfortunately, the release mechanism of their cargo/genome remains elusive. Pores on the symmetry axes were proposed to enable the slow release of the viral genome. In contrast, cryo-EM images showed that capsids of nonenveloped RNA viruses can crack open and rapidly release the genome. We combined in vitro cryo-EM observations of the genome release of three viruses with coarse-grained simulations of generic virus-like nanoparticles to investigate the cargo/genome release pathways. Simulations provided details on both slow and rapid release pathways, including the success rates of individual releases. Moreover, the simulated structures from the rapid release pathway were in agreement with the experiment. Slow release occurred when interactions between capsid subunits were long-ranged, and the cargo/genome was noncompact. In contrast, rapid release was preferred when the interaction range was short and/or the cargo/genome was compact. These findings indicate a design strategy of virus-like nanoparticles for drug delivery.

Molecular dynamics of the viral life cycle: progress and prospects

Molecular dynamics (MD) simulations across spatiotemporal resolutions are widely applied to study viruses and represent the central technique uniting the field of computational virology. We discuss the progress of MD in elucidating the dynamics of the viral life cycle, including the status of modeling intact extracellular virions and leveraging advanced simulations to mimic active life cycle processes. We further remark on the prospects of MD for continued contributions to the basic science characterization of viruses, especially given the increasing availability of high-quality experimental data and supercomputing power. Overall, integrative computational methods that are closely guided by experiments are unmatched in the level of detail they provide, enabling-now and in the future-new discoveries relevant to thwarting viral infection.

Engineering Endogenous Tumor-Associated Macrophage-Targeted Biomimetic Nano-RBC to Reprogram Tumor Immunosuppressive Microenvironment for Enhanced Chemo-Immunotherapy

Immunotherapy has shown encouraging results in various cancers, but the response rates are relatively low due to the complex tumor immunosuppressive microenvironment (TIME). The presence of tumor-associated macrophages (TAMs) and tumor hypoxia correlates significantly with potent immunosuppressive activity. Here, a hemoglobin-poly(ε-caprolactone) (Hb-PCL) conjugate self-assembled biomimetic nano red blood cell (nano-RBC) system (V(Hb)) is engineered to deliver chemotherapeutic doxorubicin (DOX) and oxygen for reprogramming TIME. The Hb moiety of V(Hb)@DOX can bind to endogenous plasma haptoglobin (Hp) and specifically target the M2-type TAMs via the CD163 surface receptor, and effectively kill the cells. In addition, the O₂ released by the Hb alleviates tumor hypoxia, which further augments the antitumor immune response by recruiting fewer M2-type macrophages. TAM-targeting depletion and hypoxia alleviation synergistically reprogram the TIME, which concurrently downregulate PD-L1 expression of tumor cells, decrease the levels of immunosuppressive cytokines such as IL-10 and TGF-β, elevate the immunostimulatory IFN-γ, enhance cytotoxic T lymphocyte (CTL) response, and boost a strong memory response. The ensuing TAM-targeted chemo-immunotherapeutic effects markedly inhibit tumor metastasis and recurrence. Taken together, the engineered endogenous TAM-targeted biomimetic nano-RBC system is a highly promising tool to reprogram TIME for cancer chemo-immunotherapy.

Coarse-grained simulations of phase separation driven by DNA and its sensor protein cGAS

The enzyme cGAS functions as a sensor that recognizes the cytosolic DNA from foreign pathogen. The activation of the protein triggers the transcription of inflammatory genes, leading into the establishment of an antipathogen state. An interesting new discovery is that the detection of DNA by cGAS induced the formation of liquid-like droplets. However how cells regulate the formation of these droplets is still not fully understood. In order to unravel the molecular mechanism beneath the DNA-mediated phase separation of cGAS, we developed a polymer-based coarse-grained model which takes into accounts the basic structural organization in DNA and cGAS, as well as the binding properties between these biomolecules. This model was further integrated into a hybrid simulation algorithm. With this computational method, a multi-step kinetic process of aggregation between cGAS and DNA was observed. Moreover, we systematically tested the model under different concentrations and binding parameters. Our simulation results show that phase separation requires both cGAS dimerization and protein-DNA interactions, whereas polymers can be kinetically trapped in small aggregates under strong binding affinities. Additionally, we demonstrated that supramolecular assembly can be facilitated by increasing the number of functional modules in protein or DNA polymers, suggesting that multivalency and intrinsic disordered regions play positive roles in regulating phase separation. This is consistent to previous experimental evidences. Taken together, this is, to the best of our knowledge, the first computational model to study condensation of cGAS-DNA complexes. While the method can reach the timescale beyond the capability of atomic-level MD simulations, it still includes information about spatial arrangement of functional modules in biopolymers that is missing in the mean-field theory. Our work thereby adds a useful dimension to a suite of existing experimental and computational techniques to study the dynamics of phase separation in biological systems.

Expanding our understanding of marine viral diversity through metagenomic analyses of biofilms

Recent metagenomics surveys have provided insights into the marine virosphere. However, these surveys have focused solely on viruses in seawater, neglecting those associated with biofilms. By analyzing 1.75 terabases of biofilm metagenomic data, 3974 viral sequences were identified from eight locations around the world. Over 90% of these viral sequences were not found in previously reported datasets. Comparisons between biofilm and seawater metagenomes identified viruses that are endemic to the biofilm niche. Analysis of viral sequences integrated within biofilm-derived microbial genomes revealed potential functional genes for trimeric autotransporter adhesin and polysaccharide metabolism, which may contribute to biofilm formation by the bacterial hosts. However, more than 70% of the genes could not be annotated. These findings show marine biofilms to be a reservoir of novel viruses and have enhanced our understanding of natural virus-bacteria ecosystems.

Small molecule therapeutics to destabilize the ACE2-RBD complex: A molecular dynamics study

The ongoing coronavirus disease 19 (COVID-19) pandemic has infected millions of people, claimed hundreds of thousands of lives, and made a worldwide health emergency. Understanding the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mechanism of infection is crucial in the development of potential therapeutics and vaccines. The infection process is triggered by direct binding of the SARS-CoV-2 receptor-binding domain (RBD) to the host-cell receptor angiotensin-converting enzyme 2 (ACE2). Many efforts have been made to design or repurpose therapeutics to deactivate the RBD or ACE2 and prevent the initial binding. In addition to direct inhibition strategies, small chemical compounds might be able to interfere and destabilize the metastable, prefusion complex of ACE2-RBD. This approach can be employed to prevent the further progress of virus infection at its early stages. In this study, molecular docking was employed to analyze the binding of two chemical compounds, SSAA09E2 and Nilotinib, with the druggable pocket of the ACE2-RBD complex. The structural changes as a result of the interference with the ACE2-RBD complex were analyzed by molecular dynamics simulations. Results show that both Nilotinib and SSAA09E2 can induce significant conformational changes in the ACE2-RBD complex, intervene with the hydrogen bonds, and influence the flexibility of proteins. Moreover, essential dynamics analysis suggests that the presence of small molecules can trigger large-scale conformational changes that may destabilize the ACE2-RBD complex.

Multiscale Models for Fibril Formation: Rare Events Methods, Microkinetic Models, and Population Balances

Amyloid fibrils are thought to grow by a two-step dock-lock mechanism. However, previous simulations of fibril formation (i) overlook the bi-molecular nature of the docking step and obtain rates with first-order units, or (ii) superimpose the docked and locked states when computing the potential of mean force for association and thereby muddle the docking and locking steps. Here, we developed a simple microkinetic model with separate locking and docking steps and with the appropriate concentration dependences for each step. We constructed a simple model comprised of chiral dumbbells that retains qualitative aspects of fibril formation. We used rare events methods to predict separate docking and locking rate constants for the model. The rate constants were embedded in the microkinetic model, with the microkinetic model embedded in a population balance model for “bottom-up” multiscale fibril growth rate predictions. These were compared to “top-down” results using simulation data with the same model and multiscale framework to obtain maximum likelihood estimates of the separate lock and dock rate constants. We used the same procedures to extract separate docking and locking rate constants from experimental fibril growth data. Our multiscale strategy, embedding rate theories, and kinetic models in conservation laws should help to extract docking and locking rate constants from experimental data or long molecular simulations with correct units and without compromising the molecular description.

From System Modeling to System Analysis: The Impact of Resolution Level and Resolution Distribution in the Computer-Aided Investigation of Biomolecules

The ever increasing computer power, together with the improved accuracy of atomistic force fields, enables researchers to investigate biological systems at the molecular level with remarkable detail. However, the relevant length and time scales of many processes of interest are still hardly within reach even for state-of-the-art hardware, thus leaving important questions often unanswered. The computer-aided investigation of many biological physics problems thus largely benefits from the usage of coarse-grained models, that is, simplified representations of a molecule at a level of resolution that is lower than atomistic. A plethora of coarse-grained models have been developed, which differ most notably in their granularity; this latter aspect determines one of the crucial open issues in the field, i.e. the identification of an optimal degree of coarsening, which enables the greatest simplification at the expenses of the smallest information loss. In this review, we present the problem of coarse-grained modeling in biophysics from the viewpoint of system representation and information content. In particular, we discuss two distinct yet complementary aspects of protein modeling: on the one hand, the relationship between the resolution of a model and its capacity of accurately reproducing the properties of interest; on the other hand, the possibility of employing a lower resolution description of a detailed model to extract simple, useful, and intelligible information from the latter.

Equilibrium mechanisms of self-limiting assembly

Self-assembly is a ubiquitous process in synthetic and biological systems, broadly defined as the spontaneous organization of multiple subunits (e.g. macromolecules, particles) into ordered multi-unit structures. The vast majority of equilibrium assembly processes give rise to two states: one consisting of dispersed disassociated subunits, and the other, a bulk-condensed state of unlimited size. This review focuses on the more specialized class of self-limiting assembly, which describes equilibrium assembly processes resulting in finite-size structures. These systems pose a generic and basic question, how do thermodynamic processes involving non-covalent interactions between identical subunits "measure" and select the size of assembled structures? In this review, we begin with an introduction to the basic statistical mechanical framework for assembly thermodynamics, and use this to highlight the key physical ingredients that ensure equilibrium assembly will terminate at finite dimensions. Then, we introduce examples of self-limiting assembly systems, and classify them within this framework based on two broad categories: self-closing assemblies and open-boundary assemblies. These include well-known cases in biology and synthetic soft matter - micellization of amphiphiles and shell/tubule formation of tapered subunits - as well as less widely known classes of assemblies, such as short-range attractive/long-range repulsive systems and geometrically-frustrated assemblies. For each of these self-limiting mechanisms, we describe the physical mechanisms that select equilibrium assembly size, as well as potential limitations of finite-size selection. Finally, we discuss alternative mechanisms for finite-size assemblies, and draw contrasts with the size-control that these can achieve relative to self-limitation in equilibrium, single-species assemblies.

Mechanisms of Scaffold-Mediated Microcompartment Assembly and Size Control

This article describes a theoretical and computational study of the dynamical assembly of a protein shell around a complex consisting of many cargo molecules and long, flexible scaffold molecules. Our study is motivated by bacterial microcompartments, which are proteinaceous organelles that assemble around a condensed droplet of enzymes and reactants. As in many examples of cytoplasmic liquid-liquid phase separation, condensation of the microcompartment interior cargo is driven by flexible scaffold proteins that have weak multivalent interactions with the cargo. Our results predict that the shell size, amount of encapsulated cargo, and assembly pathways depend sensitively on properties of the scaffold, including its length and valency of scaffold-cargo interactions. Moreover, the ability of self-assembling protein shells to change their size to accommodate scaffold molecules of different lengths depends crucially on whether the spontaneous curvature radius of the protein shell is smaller or larger than a characteristic elastic length scale of the shell. Beyond natural microcompartments, these results have important implications for synthetic biology efforts to target alternative molecules for encapsulation by microcompartments or viral shells. More broadly, the results elucidate how cells exploit coupling between self-assembly and liquid-liquid phase separation to organize their interiors.

The Electrostatic Features of Dengue Virus Capsid Assembly

Dengue virus causes serious diseases and deaths in the world. Understanding the fundamental mechanisms of dengue virus is highly demanded to develop treatments for dengue virus caused diseases. Here, we present a computational work which focused on the stability of dengue viral capsid. The interactions among E proteins on the dengue viral capsid were studied using several computational approaches. It was found that the electrostatic distribution on a single E protein monomer is highly inhomogeneous, which makes an E protein strongly binding with another E protein. This is the reason why all the E proteins form homodimers as the basic units on the whole dengue viral capsids. The pKa calculations of E proteins demonstrated that the folding energy of an E protein is low and stable in the range of pH 6–10, which is different from many other proteins that are stable at certain pH. The pH dependence of binding energy of E protein homodimer shows that the binding energy is low and independent from pH when the pH is also in the range of 6–10. This finding implies that the dengue virus can survive in a wide range of pH, which can explain why the dengue virus is so widely distributed in the world and spreads fast.

Effect of the charge distribution of virus coat proteins on the length of packaged RNAs

Single-stranded RNA viruses efficiently encapsulate their genome into a protein shell called the capsid. Electrostatic interactions between the positive charges in the capsid protein's N-terminal tail and the negatively charged genome have been postulated as the main driving force for virus assembly. Recent experimental results indicate that the N-terminal tail with the same number of charges and same lengths packages different amounts of RNA, which reveals that electrostatics alone cannot explain all the observed outcomes of the RNA self-assembly experiments. Using a mean-field theory, we show that the combined effect of genome configurational entropy and electrostatics can explain to some extent the amount of packaged RNA with mutant proteins where the location and number of charges on the tails are altered. Understanding the factors contributing to the virus assembly could promote the attempt to block viral infections or to build capsids for gene therapy applications.

Frustration and Direct-Coupling Analyses to Predict Formation and Function of Adeno-Associated Virus

Adeno-associated virus (AAV) is a promising gene therapy vector because of its efficient gene delivery and relatively mild immunogenicity. To improve delivery target specificity, researchers use combinatorial and rational library design strategies to generate novel AAV capsid variants. These approaches frequently propose high proportions of nonforming or noninfective capsid protein sequences that reduce the effective depth of synthesized vector DNA libraries, thereby raising the discovery cost of novel vectors. We evaluated two computational techniques for their ability to estimate the impact of residue mutations on AAV capsid protein-protein interactions and thus predict changes in vector fitness, reasoning that these approaches might inform the design of functionally enriched AAV libraries and accelerate therapeutic candidate identification. The Frustratometer computes an energy function derived from the energy landscape theory of protein folding. Direct-coupling analysis (DCA) is a statistical framework that captures residue coevolution within proteins. We applied the Frustratometer to select candidate protein residues predicted to favor assembled or disassembled capsid states, then predicted mutation effects at these sites using the Frustratometer and DCA. Capsid mutants were experimentally assessed for changes in virus formation, stability, and transduction ability. The Frustratometer-based metric showed a counterintuitive correlation with viral stability, whereas a DCA-derived metric was highly correlated with virus transduction ability in the small population of residues studied. Our results suggest that coevolutionary models may be able to elucidate complex capsid residue-residue interaction networks essential for viral function, but further study is needed to understand the relationship between protein energy simulations and viral capsid metastability.

Small Molecules to Destabilize the ACE2-RBD Complex: A Molecular Dynamics Study for Potential COVID-19 Therapeutics

The ongoing COVID-19 pandemic has infected millions of people, claimed hundreds of thousands of lives, and made a worldwide health emergency. Understanding the SARS-CoV-2 mechanism of infection is crucial in the development of potential therapeutics and vaccines. The infection process is triggered by direct binding of the SARS-CoV-2 receptor-binding domain (RBD) to the host cell receptor, Angiotensin-converting enzyme 2 (ACE2). Many efforts have been made to design or repurpose therapeutics to deactivate RBD or ACE2 and prevent the initial binding. In addition to direct inhibition strategies, small chemical compounds might be able to interfere and destabilize the meta-stable, pre-fusion complex of ACE2-RBD. This approach can be employed to prevent the further progress of virus infection at its early stages. In this study, Molecular docking is employed to analyze the binding of two chemical compounds, SSAA09E2 and Nilotinib, with the druggable pocket of the ACE2-RBD complex. The structural changes as a result of the interference with the ACE2-RBD complex are analyzed by molecular dynamics simulations. Results show that both Nilotinib and SSAA09E2 can induce significant conformational changes in the ACE2-RBD complex, intervene with the hydrogen bonds, and influence the flexibility of proteins. Moreover, essential dynamics analysis suggests that the presence of small molecules can trigger large-scale conformational changes that may destabilize the ACE2-RBD complex.

Spike Proteins of SARS-CoV and SARS-CoV-2 Utilize Different Mechanisms to Bind With Human ACE2

The ongoing outbreak of COVID-19 has been a serious threat to human health worldwide. The virus SARS-CoV-2 initiates its infection to the human body via the interaction of its spike (S) protein with the human Angiotensin-Converting Enzyme 2 (ACE2) of the host cells. Therefore, understanding the fundamental mechanisms of how SARS-CoV-2 S protein receptor binding domain (RBD) binds to ACE2 is highly demanded for developing treatments for COVID-19. Here we implemented multi-scale computational approaches to study the binding mechanisms of human ACE2 and S proteins of both SARS-CoV and SARS-CoV-2. Electrostatic features, including electrostatic potential, electric field lines, and electrostatic forces of SARS-CoV and SARS-CoV-2 were calculated and compared in detail. The results demonstrate that SARS-CoV and SARS-CoV-2 S proteins are both attractive to ACE2 by electrostatic forces even at different distances. However, the residues contributing to the electrostatic features are quite different due to the mutations between SARS-CoV S protein and SARS-CoV-2 S protein. Such differences are analyzed comprehensively. Compared to SARS-CoV, the SARS-CoV-2 binds with ACE2 using a more robust strategy: The electric field line related residues are distributed quite differently, which results in a more robust binding strategy of SARS-CoV-2. Also, SARS-CoV-2 has a higher electric field line density than that of SARS-CoV, which indicates stronger interaction between SARS-CoV-2 and ACE2, compared to that of SARS-CoV. Key residues involved in salt bridges and hydrogen bonds are identified in this study, which may help the future drug design against COVID-19.

Simple, General Criterion for Onset of Disclination Disorder on Curved Surfaces

Determining the positions of lattice defects on bounded elastic surfaces with Gaussian curvature is a nontrivial task of mechanical energy optimization. We introduce a simple way to predict the onset of disclination disorder from the shape of the surface. The criterion fixes the value of a weighted integral Gaussian curvature to a universal constant and proves accurate across a great variety of shapes. It provides improved understanding of the limitations to crystalline order in many natural and engineering contexts, such as the assembly of viral capsids.

Viral structural proteins as targets for antivirals

Viral structural proteins are emerging as effective targets for new antivirals. In a viral lifecycle, the capsid must assemble, disassemble, and respond to host proteins, all at the right time and place. These reactions work within a narrow range of conditions, making them susceptible to small molecule interference. In at least three specific viruses, this approach has had met with preliminary success. In rhinovirus and poliovirus, compounds like pleconaril bind capsid and block RNA release. Bevirimat binds to Gag protein in HIV, inhibiting maturation. In Hepatitis B virus, core protein allosteric modulators (CpAMs) promote spontaneous assembly of capsid protein leading to empty and aberrant particles. Despite the biological diversity between viruses and the chemical diversity between antiviral molecules, we observe common features in these antivirals' mechanisms of action. These approaches work by stabilizing protein-protein interactions.

Visualization of Single Molecules Building a Viral Capsid Protein Lattice through Stochastic Pathways

Direct visualization of pathways followed by single molecules while they spontaneously self-assemble into supramolecular biological machines may provide fundamental knowledge to guide molecular therapeutics and the bottom-up design of nanomaterials and nanodevices. Here, high-speed atomic force microscopy is used to visualize self-assembly of the bidimensional lattice of protein molecules that constitutes the framework of the mature human immunodeficiency virus capsid. By real-time imaging of the assembly reaction, individual transient intermediates and reaction pathways followed by single molecules could be revealed. As when assembling a jigsaw puzzle, the capsid protein lattice is randomly built. Lattice patches grow independently from separate nucleation events whereby individual molecules follow different paths. Protein subunits can be added individually, while others form oligomers before joining a lattice or are occasionally removed from the latter. Direct real-time imaging of supramolecular self-assembly has revealed a complex, chaotic process involving multiple routes followed by individual molecules that are inaccessible to bulk (averaging) techniques.

Shape selection and mis-assembly in viral capsid formation by elastic frustration

The successful assembly of a closed protein shell (or capsid) is a key step in the replication of viruses and in the production of artificial viral cages for bio/nanotechnological applications. During self-assembly, the favorable binding energy competes with the energetic cost of the growing edge and the elastic stresses generated due to the curvature of the capsid. As a result, incomplete structures such as open caps, cylindrical or ribbon-shaped shells may emerge, preventing the successful replication of viruses. Using elasticity theory and coarse-grained simulations, we analyze the conditions required for these processes to occur and their significance for empty virus self-assembly. We find that the outcome of the assembly can be recast into a universal phase diagram showing that viruses with high mechanical resistance cannot be self-assembled directly as spherical structures. The results of our study justify the need of a maturation step and suggest promising routes to hinder viral infections by inducing mis-assembly.

Temporally Coarse-Grained All-Atom Molecular Dynamics Achieved via Stochastic Padé Approximants

A Padé approximant scheme for realizing the discrete-time evolution of the state of a many-atom system is introduced. This temporal coarse-graining scheme accounts for the underlying Newtonian physics and avoids the need for construction of spatially coarse-grained variables. Newtonian physics is incorporated through short molecular dynamics simulations at the beginning of each of the large coarse-grained timesteps. The balance between stochastic and coherent dynamics expressed by many-atom systems is captured via incorporation of the Ito formula into a Padé approximant for the time dependence of individual atom positions over large timesteps. Since the time for a many-atom system to express a characteristic ensemble of atomic velocity fluctuations is typically short relative to the characteristic time of large-scale atomic displacements, a computationally efficient and accurate temporal coarse-graining of the atom-resolved Newtonian dynamics is formulated, denoted all-atom Padé-Ito molecular dynamics (APIMD). Evolution of the system over a time step much longer than that required for standard molecular dynamics (MD) is achieved via incorporation of information from the short MD simulations into a Padé approximant extrapolation in time. The extrapolated atomic configuration is subjected to energy minimization and, when needed, thermal equilibration so as to avoid occasional unphysical close encounters deriving from the Padé approximant extrapolation and to represent configurations appropriate for the temperature of interest. APIMD is implemented and tested via comparison with traditional MD simulations of five phenomena: (1) pertussis toxin subunit deformation, (2) structural transition in a T = 1 capsid-like structure of HPV16 L1 protein, (3) coalescence of argon nanodroplets, and structural transitions in dialanine in (4) vacuum, and (5) water. Accuracy of APIMD is demonstrated using semimicroscopic descriptors (rmsd, radius of gyration, residue-residue contact maps, and densities) and the free energy. Significant computational acceleration relative to traditional molecular dynamics is illustrated.

Computational simulations of TNF receptor oligomerization on plasma membrane

The interactions between tumor necrosis factors (TNFs) and their corresponding receptors (TNFRs) play a pivotal role in inflammatory responses. Upon ligand binding, TNFR receptors were found to form oligomers on cell surfaces. However, the underlying mechanism of oligomerization is not fully understood. In order to tackle this problem, molecular dynamics (MD) simulations have been applied to the complex between TNF receptor-1 (TNFR1) and its ligand TNF-α as a specific test system. The simulations on both all-atom (AA) and coarse-grained (CG) levels achieved the similar results that the extracellular domains of TNFR1 can undergo large fluctuations on plasma membrane, while the dynamics of TNFα-TNFR1 complex is much more constrained. Using the CG model with the Martini force field, we are able to simulate the systems that contain multiple TNFα-TNFR1 complexes with the timescale of microseconds. We found that complexes can aggregate into oligomers on the plasma membrane through the lateral interactions between receptors at the end of the CG simulations. We suggest that this spatial organization is essential to the efficiency of signal transduction for ligands that belong to the TNF superfamily. We further show that the aggregation of two complexes is initiated by the association between the N-terminal domains of TNFR1 receptors. Interestingly, the cis-interfaces between N-terminal regions of two TNF receptors have been observed in the previous X-ray crystallographic experiment. Therefore, we provide supportive evidence that cis-interface is of functional importance in triggering the receptor oligomerization. Taken together, our study brings insights to understand the molecular mechanism of TNF signaling.

Mathematical determination of the HIV-1 matrix shell structure and its impact on the biology of HIV-1

Since its discovery in the early 1980s, there has been significant progress in understanding the biology of type 1 human immunodeficiency virus (HIV-1). Structural biologists have made tremendous contributions to this challenge, guiding the development of current therapeutic strategies. Despite our efforts, there are unresolved structural features of the virus and consequently, significant knowledge gaps in our understanding. The superstructure of the HIV-1 matrix (MA) shell has not been elucidated. Evidence by various high-resolution microscopy techniques support a model composed of MA trimers arranged in a hexameric configuration consisting of 6 MA trimers forming a hexagon. In this manuscript we review the mathematical limitations of this model and propose a new model consisting of a 6-lune hosohedra structure, which aligns with available structural evidence. We used geometric and rotational matrix computation methods to construct our model and predict a new mechanism for viral entry that explains the increase in particle size observed during CD4 receptor engagement and the most common HIV-1 ellipsoidal shapes observed in cryo-EM tomograms. A better understanding of the HIV-1 MA shell structure is a key step towards better models for viral assembly, maturation and entry. Our new model will facilitate efforts to improve understanding of the biology of HIV-1.

A Multiscale Model for the Self-Assembly of Coat Proteins in Bacteriophage MS2

The self-assembly of viral capsids is an essential step to the formation of infectious viruses. Elucidating the kinetic mechanisms of how a capsid or virus-like particle assembles could advance our knowledge about the viral lifecycle, as well as the general principles in self-assembly of biomaterials. However, current understanding of capsid assembly remains incomplete for many viruses due to the fact that the transient intermediates along the assembling pathways are experimentally difficult to be detected. In this paper, we constructed a new multiscale computational framework to simulate the self-assembly of virus-like particles. We applied our method to the coat proteins of bacteriophage MS2 as a specific model system. This virus-like particle of bacteriophage MS2 has a unique feature that its 90 sequence-identical dimers can be classified into two structurally various groups: one is the symmetric CC dimer, and the other is the asymmetric AB dimer. The homotypic interactions between AB dimers result in a 5-fold symmetric contact, while the heterotypic interactions between AB and CC dimers result in 6-fold symmetric contact. We found that the assembly can be described as a physical process of phase transition that is regulated by various factors such as concentration and specific stoichiometry between AB and CC dimers. Our simulations also demonstrate that heterotypic and homotypic interfaces play distinctive roles in modulating the assembling kinetics. The interaction between AB and CC dimers is much more dynamic than that between two AB dimers. We therefore suggest that the alternate growth of viral capsid through the heterotypic dimer interactions dominates the assembling pathways. This is, to the best of our knowledge, the first multiscale model to simulate the assembling process of coat proteins in bacteriophage MS2. The generality of this approach opens the door to its further applications in assembly of other viral capsids, virus-like particles, and novel drug delivery systems.

Minimal coarse-grained models for molecular self-organisation in biology

The molecular machinery of life is largely created via self-organisation of individual molecules into functional assemblies. Minimal coarse-grained models, in which a whole macromolecule is represented by a small number of particles, can be of great value in identifying the main driving forces behind self-organisation in cell biology. Such models can incorporate data from both molecular and continuum scales, and their results can be directly compared to experiments. Here we review the state of the art of models for studying the formation and biological function of macromolecular assemblies in living organisms. We outline the key ingredients of each model and their main findings. We illustrate the contribution of this class of simulations to identifying the physical mechanisms behind life and diseases, and discuss their future developments.

Virus capsid assembly across different length scales inspire the development of virus-based biomaterials

In biology, there are an abundant number of self-assembled structures organized according to hierarchical levels of complexity. In some examples, the assemblies formed at each level exhibit unique properties and behaviors not present in individual components. Viruses are an example of such where first individual subunits come together to form a capsid structure, some utilizing a scaffolding protein to template or catalyze the capsid formation. Increasing the level of complexity, the viral capsids can then be used as building blocks of higher-level assemblies. This has inspired scientists to design and construct virus capsid-based functional nano-materials. This review provides some insight into the assembly of virus capsids across several length scales, and certain properties that arise at different levels, providing examples found in naturally occurring systems and those that are synthetically designed.

RNA-Mediated Virus Assembly: Mechanisms and Consequences for Viral Evolution and Therapy

Viruses, entities composed of nucleic acids, proteins, and in some cases lipids lack the ability to replicate outside their target cells. Their components self-assemble at the nanoscale with exquisite precision-a key to their biological success in infection. Recent advances in structure determination and the development of biophysical tools such as single-molecule spectroscopy and noncovalent mass spectrometry allow unprecedented access to the detailed assembly mechanisms of simple virions. Coupling these techniques with mathematical modeling and bioinformatics has uncovered a previously unsuspected role for genomic RNA in regulating formation of viral capsids, revealing multiple, dispersed RNA sequence/structure motifs [packaging signals (PSs)] that bind cognate coat proteins cooperatively. The PS ensemble controls assembly efficiency and accounts for the packaging specificity seen in vivo. The precise modes of action of the PSs vary between viral families, but this common principle applies across many viral families, including major human pathogens. These insights open up the opportunity to block or repurpose PS function in assembly for both novel antiviral therapy and gene/drug/vaccine applications.

The Roles of Electrostatic Interactions in Capsid Assembly Mechanisms of Giant Viruses

In the last three decades, many giant DNA viruses have been discovered. Giant viruses present a unique and essential research frontier for studies of self-assembly and regulation of supramolecular assemblies. The question on how these giant DNA viruses assemble thousands of proteins so accurately to form their protein shells, the capsids, remains largely unanswered. Revealing the mechanisms of giant virus assembly will help to discover the mysteries of many self-assembly biology problems. Paramecium bursaria Chlorella virus-1 (PBCV-1) is one of the most intensively studied giant viruses. Here, we implemented a multi-scale approach to investigate the interactions among PBCV-1 capsid building units called capsomers. Three binding modes with different strengths are found between capsomers around the relatively flat area of the virion surface at the icosahedral 2-fold axis. Furthermore, a capsomer structure manipulation package is developed to simulate the capsid assembly process. Using these tools, binding forces among capsomers were investigated and binding funnels were observed that were consistent with the final assembled capsid. In addition, total binding free energies of each binding mode were calculated. The results helped to explain previous experimental observations. Results and tools generated in this work established an initial computational approach to answer current unresolved questions regarding giant virus assembly mechanisms. Results will pave the way for studying more complicated process in other biomolecular structures.

Unraveling the Motions behind Enterovirus 71 Uncoating

Enterovirus 71 can be a severe pathogen in small children and immunocompromised adults. Virus uncoating is a critical step in the infection of the host cell; however, the mechanisms that control this process remain poorly understood. We applied normal mode analysis and perturbation response scanning to several complexes of the virus capsid and present a coarse-graining approach to analyze the full capsid. We show that our method offers an alternative to expressing the system as a set of rigid blocks and accounts for the interconnection between nodes within each subunit and protein interfaces across the capsid. In our coarse-grained approach, the modes associated with capsid expansion are captured in the first three nondegenerate modes and correspond to the changes observed in structural studies of the virus. We show that the resolution of the analysis may be modified without losing information on the global motions leading to uncoating. Perturbation response scanning revealed that a protomer cannot serve as a functional unit to explain deformations of the capsid. Instead, we define a pentamer as the minimum functional unit to investigate changes within the capsid. From the modal analysis and perturbation response scanning, we locate a hotspot region surrounding the fivefold axis. The range of the effect of these single, hotspot residues extend to 140 Å. The perturbation of internal capsid residues in this region displayed greatest propensity to capsid expansion, thus indicating the significant role that the RNA genome may play in triggering uncoating.