Insights into the Cooperative Nature of ATP Hydrolysis in Actin Filaments

K-Means Clustering Coarse-Graining (KMC-CG): A Next Generation Methodology for Determining Optimal Coarse-Grained Mappings of Large Biomolecules

Coarse-grained (CG) molecular dynamics (MD) has become a method of choice for simulating various large scale biomolecular processes; therefore, the systematic definition of the CG mappings for biomolecules remains an important topic. Appropriate CG mappings can significantly enhance the representability of a CG model and improve its ability to capture critical features of large biomolecules. In this work, we present a systematic and more generalized method called K-means clustering coarse-graining (KMC-CG), which builds on the earlier approach of essential dynamics coarse-graining (ED-CG). KMC-CG removes the sequence-dependent constraints of ED-CG, allowing it to explore a more extensive space and thus enabling the discovery of more physically optimal CG mappings. Furthermore, the implementation of the K-means clustering algorithm can variationally optimize the CG mapping with efficiency and stability. This new method is tested in three cases: ATP-bound G-actin, the HIV-1 CA pentamer, and the Arp2/3 complex. In these examples, the CG models generated by KMC-CG are seen to better capture the structural, dynamic, and functional domains. KMC-CG therefore provides a robust and consistent approach to generating CG models of large biomolecules that can then be more accurately parametrized by either bottom-up or top-down CG force fields.

OpenMSCG: A Software Tool for Bottom-Up Coarse-Graining

The "bottom-up" approach to coarse-graining, for building accurate and efficient computational models to simulate large-scale and complex phenomena and processes, is an important approach in computational chemistry, biophysics, and materials science. As one example, the Multiscale Coarse-Graining (MS-CG) approach to developing CG models can be rigorously derived using statistical mechanics applied to fine-grained, i.e., all-atom simulation data for a given system. Under a number of circumstances, a systematic procedure, such as MS-CG modeling, is particularly valuable. Here, we present the development of the OpenMSCG software, a modularized open-source software that provides a collection of successful and widely applied bottom-up CG methods, including Boltzmann Inversion (BI), Force-Matching (FM), Ultra-Coarse-Graining (UCG), Relative Entropy Minimization (REM), Essential Dynamics Coarse-Graining (EDCG), and Heterogeneous Elastic Network Modeling (HeteroENM). OpenMSCG is a high-performance and comprehensive toolset that can be used to derive CG models from large-scale fine-grained simulation data in file formats from common molecular dynamics (MD) software packages, such as GROMACS, LAMMPS, and NAMD. OpenMSCG is modularized in the Python programming framework, which allows users to create and customize modeling "recipes" for reproducible results, thus greatly improving the reliability, reproducibility, and sharing of bottom-up CG models and their applications.

Design rules for reciprocal coupling in chemically fueled assembly

Biology regulates the function and assembly of proteins through non-equilibrium reaction cycles. Reciprocally, the assembly of proteins can influence the reaction rates of these cycles. Such reciprocal coupling between assembly and reaction cycle is a prerequisite for behavior like dynamic instabilities, treadmilling, pattern formation, and oscillations between morphologies. While assemblies regulated by chemical reaction cycles gained traction, the concept of reciprocal coupling is under-explored. In this work, we provide two molecular design strategies to tweak the degree of reciprocal coupling between the assembly and reaction cycle. The strategies involve spacing the chemically active site away from the assembly or burying it into the assembly. We envision that design strategies facilitate the creation of reciprocally coupled and, by extension, dynamic supramolecular materials in the future.

Bending forces and nucleotide state jointly regulate F-actin structure

ATP-hydrolysis-coupled actin polymerization is a fundamental mechanism of cellular force generation1-3. In turn, force4,5 and actin filament (F-actin) nucleotide state6 regulate actin dynamics by tuning F-actin's engagement of actin-binding proteins through mechanisms that are unclear. Here we show that the nucleotide state of actin modulates F-actin structural transitions evoked by bending forces. Cryo-electron microscopy structures of ADP-F-actin and ADP-Pi-F-actin with sufficient resolution to visualize bound solvent reveal intersubunit interfaces bridged by water molecules that could mediate filament lattice flexibility. Despite extensive ordered solvent differences in the nucleotide cleft, these structures feature nearly identical lattices and essentially indistinguishable protein backbone conformations that are unlikely to be discriminable by actin-binding proteins. We next introduce a machine-learning-enabled pipeline for reconstructing bent filaments, enabling us to visualize both continuous structural variability and side-chain-level detail. Bent F-actin structures reveal rearrangements at intersubunit interfaces characterized by substantial alterations of helical twist and deformations in individual protomers, transitions that are distinct in ADP-F-actin and ADP-Pi-F-actin. This suggests that phosphate rigidifies actin subunits to alter the bending structural landscape of F-actin. As bending forces evoke nucleotide-state dependent conformational transitions of sufficient magnitude to be detected by actin-binding proteins, we propose that actin nucleotide state can serve as a co-regulator of F-actin mechanical regulation.

Bottom-up Coarse-Graining: Principles and Perspectives

Large-scale computational molecular models provide scientists a means to investigate the effect of microscopic details on emergent mesoscopic behavior. Elucidating the relationship between variations on the molecular scale and macroscopic observable properties facilitates an understanding of the molecular interactions driving the properties of real world materials and complex systems (e.g., those found in biology, chemistry, and materials science). As a result, discovering an explicit, systematic connection between microscopic nature and emergent mesoscopic behavior is a fundamental goal for this type of investigation. The molecular forces critical to driving the behavior of complex heterogeneous systems are often unclear. More problematically, simulations of representative model systems are often prohibitively expensive from both spatial and temporal perspectives, impeding straightforward investigations over possible hypotheses characterizing molecular behavior. While the reduction in resolution of a study, such as moving from an atomistic simulation to that of the resolution of large coarse-grained (CG) groups of atoms, can partially ameliorate the cost of individual simulations, the relationship between the proposed microscopic details and this intermediate resolution is nontrivial and presents new obstacles to study. Small portions of these complex systems can be realistically simulated. Alone, these smaller simulations likely do not provide insight into collectively emergent behavior. However, by proposing that the driving forces in both smaller and larger systems (containing many related copies of the smaller system) have an explicit connection, systematic bottom-up CG techniques can be used to transfer CG hypotheses discovered using a smaller scale system to a larger system of primary interest. The proposed connection between different CG systems is prescribed by (i) the CG representation (mapping) and (ii) the functional form and parameters used to represent the CG energetics, which approximate potentials of mean force (PMFs). As a result, the design of CG methods that facilitate a variety of physically relevant representations, approximations, and force fields is critical to moving the frontier of systematic CG forward. Crucially, the proposed connection between the system used for parametrization and the system of interest is orthogonal to the optimization used to approximate the potential of mean force present in all systematic CG methods. The empirical efficacy of machine learning techniques on a variety of tasks provides strong motivation to consider these approaches for approximating the PMF and analyzing these approximations.

Coarse-Grained Water Model Development for Accurate Dynamics and Structure Prediction

Several coarse-graining (CG) methods have been combined to develop a CG model of water capable of the accurate prediction of structure and dynamics properties. The multiscale coarse-graining (MS-CG) method based on force matching and the PDF-based coarse-graining method were used for accurate dynamics prediction. The iterative Boltzmann inversion (IBI) method was added for accurate structure representation. The approach is applied to bulk water, and the results show close reproduction of the CG structure when compared with the reference atomistic data. The combination of MS-CG and IBI methods facilitates the development of CG force fields at different temperatures based on a single MS-CG coarse-graining procedure. The dynamic properties of the CG water model closely match those obtained from the reference atomistic system. The general application of this approach to any existing coarse-graining methods is discussed.

An investigation of mechanisms underlying mouse blastocyst hatching: a ribonucleic acid sequencing study

OBJECTIVE

To investigate the regulatory mechanisms and signaling molecules underlying hatching in mouse embryos.

DESIGN

Experimental laboratory study using a mouse embryo model.

SETTING

University-based basic scientific research laboratory.

ANIMALS

A total of 40 B6C3F1 × B6D2F1 mouse embryos were used in this study.

INTERVENTION(S)

Frozen/thawed mouse embryos, at the 8-cell stage, were cultured in vitro for 2 days. The resulting hatching and prehatching blastocysts were then used for complementary deoxyribonucleic acid (cDNA) library preparation and ribonucleic acid (RNA) sequencing analysis (n = 8 for each group). Differentially expressed genes were then used for downstream functional analysis. In addition, a list of genes related to developmental progression in humans was used to identify genes that were potentially related to the hatching of human embryos.

MAIN OUTCOME MEASURE(S)

Differentially expressed genes, enriched Gene Ontology terms and canonical pathways, clustered gene networks, activated upstream regulators, and common genes between a gene list of hatching-related genes in mice and a gene list associated with developmental progression in humans.

RESULT(S)

A total 275 differentially expressed genes were identified between hatching and prehatching blastocysts: 230 up-regulated and 45 down-regulated genes. Functional enrichment analysis suggested that blastocyst hatching in vitro is an adenosine triphosphate (ATP)-dependent process that involves protein biosynthesis and organization of the cytoskeleton. Furthermore, by regulating cell motility, the RhoA signaling pathway (including Arpc2, Cfl1, Gsn, Pfn1, Tpi1, Grb2, Tmsb10, Enah, and Rnd3 genes) may be a crucial signaling pathway during hatching. We also identified a cluster of genes (Krt8, Krt7, Cldn4, and Aqp3) that exerted functional roles in cell-cell junctions and water homeostasis during hatching. Moreover, some growth factors (angiotensinogen and fibroblast growth factor 2) and endocrine factors (estrogen receptor and prolactin) were predicted to be involved in the regulation of embryo hatching. In addition, we identified 81 potential genes that are potentially involved in the hatching process in human embryos.

CONCLUSION(S)

Our analysis identified potential genes and molecular regulatory pathways involved in the blastocyst hatching process in mice; we also identified genes that may potentially regulate hatching in human embryos. Our findings enhance our knowledge of embryo development and provide useful information for further exploring the mechanisms underlying embryo hatching.

Reaction-Diffusion Model-Based Research on Formation Mechanism of Neuron Dendritic Spine Patterns

The pattern abnormalities of dendritic spine, tiny protrusions on neuron dendrites, have been found related to multiple nervous system diseases, such as Parkinson's disease and schizophrenia. The determination of the factors affecting spine patterns is of vital importance to explore the pathogenesis of these diseases, and further, search the treatment method for them. Although the study of dendritic spines is a hot topic in neuroscience in recent years, there is still a lack of systematic study on the formation mechanism of its pattern. This paper provided a reinterpretation of reaction-diffusion model to simulate the formation process of dendritic spine, and further, study the factors affecting spine patterns. First, all four classic shapes of spines, mushroom-type, stubby-type, thin-type, and branched-type were reproduced using the model. We found that the consumption rate of substrates by the cytoskeleton is a key factor to regulate spine shape. Moreover, we found that the density of spines can be regulated by the amount of an exogenous activator and inhibitor, which is in accordance with the anatomical results found in hippocampal CA1 in SD rats with glioma. Further, we analyzed the inner mechanism of the above model parameters regulating the dendritic spine pattern through Turing instability analysis and drew a conclusion that an exogenous inhibitor and activator changes Turing wavelength through which to regulate spine densities. Finally, we discussed the deep regulation mechanisms of several reported regulators of dendritic spine shape and densities based on our simulation results. Our work might evoke attention to the mathematic model-based pathogenesis research for neuron diseases which are related to the dendritic spine pattern abnormalities and spark inspiration in the treatment research for these diseases.

Dynamical properties across different coarse-grained models for ionic liquids

Room-temperature ionic liquids (RTILs) stand out among molecular liquids for their rich physicochemical characteristics, including structural and dynamic heterogeneity. The significance of electrostatic interactions in RTILs results in long characteristic length- and timescales, and has motivated the development of a number of coarse-grained (CG) simulation models. In this study, we aim to better understand the connection between certain CG parameterization strategies and the dynamical properties and transferability of the resulting models. We systematically compare five CG models: a model largely parameterized from experimental thermodynamic observables; a refinement of this model to increase its structural accuracy; and three models that reproduce a given set of structural distribution functions by construction, with varying intramolecular parameterizations and reference temperatures. All five CG models display limited structural transferability over temperature, and also result in various effective dynamical speedup factors, relative to a reference atomistic model. On the other hand, the structure-based CG models tend to result in more consistent cation-anion relative diffusion than the thermodynamic-based models, for a single thermodynamic state point. By linking short- and long-timescale dynamical behaviors, we demonstrate that the varying dynamical properties of the different CG models can be largely collapsed onto a single curve, which provides evidence for a route to constructing dynamically-consistent CG models of RTILs.

A multiscale coarse-grained model of the SARS-CoV-2 virion

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the COVID-19 pandemic. Computer simulations of complete viral particles can provide theoretical insights into large-scale viral processes including assembly, budding, egress, entry, and fusion. Detailed atomistic simulations are constrained to shorter timescales and require billion-atom simulations for these processes. Here, we report the current status and ongoing development of a largely "bottom-up" coarse-grained (CG) model of the SARS-CoV-2 virion. Data from a combination of cryo-electron microscopy (cryo-EM), x-ray crystallography, and computational predictions were used to build molecular models of structural SARS-CoV-2 proteins, which were then assembled into a complete virion model. We describe how CG molecular interactions can be derived from all-atom simulations, how viral behavior difficult to capture in atomistic simulations can be incorporated into the CG models, and how the CG models can be iteratively improved as new data become publicly available. Our initial CG model and the detailed methods presented are intended to serve as a resource for researchers working on COVID-19 who are interested in performing multiscale simulations of the SARS-CoV-2 virion.

Compressive and Tensile Deformations Alter ATP Hydrolysis and Phosphate Release Rates in Actin Filaments

Actin filament networks in eukaryotic cells are constantly remodeled through nucleotide state controlled interactions with actin binding proteins, leading to macroscopic structures such as bundled filaments, branched filaments, and so on. The nucleotide (ATP) hydrolysis, phosphate release, and polymerization/depolymerization reactions that lead to the formation of these structures are correlated with the conformational fluctuations of the actin subunits at the molecular scale. The resulting structures generate and experience varying levels of force and impart cells with several functionalities such as their ability to move, divide, transport cargo, etc. Models that explicitly connect the structure to reactions are essential to elucidate a fundamental level of understanding of these processes. In this regard, a bottom-up Ultra-Coarse-Grained (UCG) model of actin filaments that can simulate ATP hydrolysis, inorganic phosphate release (Pi), and depolymerization reactions is presented in this work. In this model, actin subunits are represented using coarse-grained particles that evolve in time and undergo reactions depending on the conformations sampled. The reactions are represented through state transitions, with each state represented by a unique effective coarse-grained potential. Effects of compressive and tensile strains on the rates of reactions are then analyzed. Compressive strains tend to unflatten the actin subunits, reduce the rate of ATP hydrolysis, and increase the Pi release rate. On the other hand, tensile strain flattens subunits, increases the rate of ATP hydrolysis, and decrease the Pi release rate. Incorporating these predictions into a Markov State Model highlighted that strains alter the steady-state distribution of subunits with ADPPi and ADP nucleotide, thus identifying possible additional factors underlying the cooperative binding of regulatory proteins to actin filaments.

Coarse-Grained Simulation of Mechanical Properties of Single Microtubules With Micrometer Length

Microtubules are one of the most important components in the cytoskeleton and play a vital role in maintaining the shape and function of cells. Because single microtubules are some micrometers long, it is difficult to simulate such a large system using an all-atom model. In this work, we use the newly developed convolutional and K-means coarse-graining (CK-CG) method to establish an ultra-coarse-grained (UCG) model of a single microtubule, on the basis of the low electron microscopy density data of microtubules. We discuss the rationale of the micro-coarse-grained microtubule models of different resolutions and explore microtubule models up to 12-micron length. We use the devised microtubule model to quantify mechanical properties of microtubules of different lengths. Our model allows mesoscopic simulations of micrometer-level biomaterials and can be further used to study important biological processes related to microtubule function.

Moltemplate: A Tool for Coarse-Grained Modeling of Complex Biological Matter and Soft Condensed Matter Physics

Coarse-grained models have long been considered indispensable tools in the investigation of biomolecular dynamics and assembly. However, the process of simulating such models is arduous because unconventional force fields and particle attributes are often needed, and some systems are not in thermal equilibrium. Although modern molecular dynamics programs are highly adaptable, software designed for preparing all-atom simulations typically makes restrictive assumptions about the nature of the particles and the forces acting on them. Consequently, the use of coarse-grained models has remained challenging. Moltemplate is a file format for storing coarse-grained molecular models and the forces that act on them, as well as a program that converts moltemplate files into input files for LAMMPS, a popular molecular dynamics engine. Moltemplate has broad scope and an emphasis on generality. It accommodates new kinds of forces as they are developed for LAMMPS, making moltemplate a popular tool with thousands of users in computational chemistry, materials science, and structural biology. To demonstrate its wide functionality, we provide examples of using moltemplate to prepare simulations of fluids using many-body forces, coarse-grained organic semiconductors, and the motor-driven supercoiling and condensation of an entire bacterial chromosome.

Reciprocal Coupling in Chemically Fueled Assembly: A Reaction Cycle Regulates Self-Assembly and Vice Versa

In biology, self-assembly of proteins and energy-consuming reaction cycles are intricately coupled. For example, tubulin is activated and deactivated for assembly by a guanosine triphosphate (GTP)-driven reaction cycle, and the emerging microtubules catalyze this reaction cycle by changing the microenvironment of the activated tubulin. Recently, synthetic analogs of chemically fueled assemblies have emerged, but examples in which assembly and reaction cycles are reciprocally coupled remain rare. In this work, we report a peptide that can be activated and deactivated for self-assembly. The emerging assemblies change the microenvironment of their building blocks, which consequently accelerate the rates of building block deactivation and reactivation. We quantitatively understand the mechanisms at play, and we are thus able to tune the catalysis by molecular design of the peptide precursor.

Structural basis for polarized elongation of actin filaments

Actin filaments elongate and shorten much faster at their barbed end than their pointed end, but the molecular basis of this difference has not been understood. We use all-atom molecular dynamics simulations to investigate the properties of subunits at both ends of the filament. The terminal subunits tend toward conformations that resemble actin monomers in solution, while contacts with neighboring subunits progressively flatten the conformation of internal subunits. At the barbed end the terminal subunit is loosely tethered by its DNase-1 loop to the third subunit, because its monomer-like conformation precludes stabilizing contacts with the penultimate subunit. The motions of the terminal subunit make the partially flattened penultimate subunit accessible for binding monomers. At the pointed end, unique contacts between the penultimate and terminal subunits are consistent with existing cryogenic electron microscopic (cryo-EM) maps, limit binding to incoming monomers, and flatten the terminal subunit, which likely promotes ATP hydrolysis and rapid phosphate release. These structures explain the distinct polymerization kinetics of the two ends.

A Burst of Genetic Innovation in Drosophila Actin-Related Proteins for Testis-Specific Function

Many cytoskeletal proteins perform fundamental biological processes and are evolutionarily ancient. For example, the superfamily of actin-related proteins (Arps) specialized early in eukaryotic evolution for diverse cellular roles in the cytoplasm and the nucleus. Despite its strict conservation across eukaryotes, we find that the Arp superfamily has undergone dramatic lineage-specific diversification in Drosophila. Our phylogenomic analyses reveal four independent Arp gene duplications that occurred in the common ancestor of the obscura group of Drosophila and have been mostly preserved in this lineage. All four obscura-specific Arp paralogs are predominantly expressed in the male germline and have evolved under positive selection. We focus our analyses on the divergent Arp2D paralog, which arose via a retroduplication event from Arp2, a component of the Arp2/3 complex that polymerizes branched actin networks. Computational modeling analyses suggest that Arp2D can replace Arp2 in the Arp2/3 complex and bind actin monomers. Together with the signature of positive selection, our findings suggest that Arp2D may augment Arp2's functions in the male germline. Indeed, we find that Arp2D is expressed during and following male meiosis, where it localizes to distinct locations such as actin cones-specialized cytoskeletal structures that separate bundled spermatids into individual mature sperm. We hypothesize that this unprecedented burst of genetic innovation in cytoskeletal proteins may have been driven by the evolution of sperm heteromorphism in the obscura group of Drosophila.

Temperature and Phase Transferable Bottom-up Coarse-Grained Models

Despite the high fidelity of bottom-up coarse-grained (CG) approaches to recapitulate the structural correlations in atomistic simulations, the general use of bottom-up CG methods is limited because of the nontransferable nature of these CG models under different thermodynamic conditions. Because bottom-up CG potentials usually correspond to configuration-dependent free energies of the system, recent studies have focused on adjusting enthalpic or entropic contributions to account for issues with transferability. However, these approaches can require a manual adjustment of the CG interaction a priori and are usually limited to constant volume ensembles. To overcome these limitations, we construct temperature and phase transferable CG models under constant pressure by developing the ultra-coarse-graining (UCG) methodology in the mean-field limit. In the mean-field ansatz, an embedded semi-global order parameter recapitulates global changes to the system by automatically adjusting the effective CG interactions, thus bridging free energy decompositions with UCG theory. The method presented is designed to faithfully capture structural correlations under different thermodynamic conditions, using a single UCG model. Specifically, we test the applicability of the developed theory in three distinct cases: (1) different temperatures at constant pressure in liquids, (2) different temperatures across thermodynamic phases, and (3) liquid/vapor interfaces. We demonstrate that the systematic construction of both temperature and phase transferable bottom-up CG models is possible using this generalized UCG theory. Based on our findings, this approach significantly extends the transferability and applicability of the bottom-up CG theory and method.

A Multiscale Coarse-grained Model of the SARS-CoV-2 Virion

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the COVID-19 pandemic. Computer simulations of complete viral particles can provide theoretical insights into large-scale viral processes including assembly, budding, egress, entry, and fusion. Detailed atomistic simulations, however, are constrained to shorter timescales and require billion-atom simulations for these processes. Here, we report the current status and on-going development of a largely "bottom-up" coarse-grained (CG) model of the SARS-CoV-2 virion. Structural data from a combination of cryo-electron microscopy (cryo-EM), x-ray crystallography, and computational predictions were used to build molecular models of structural SARS-CoV-2 proteins, which were then assembled into a complete virion model. We describe how CG molecular interactions can be derived from all-atom simulations, how viral behavior difficult to capture in atomistic simulations can be incorporated into the CG models, and how the CG models can be iteratively improved as new data becomes publicly available. Our initial CG model and the detailed methods presented are intended to serve as a resource for researchers working on COVID-19 who are interested in performing multiscale simulations of the SARS-CoV-2 virion.

Significance Statement

This study reports the construction of a molecular model for the SARS-CoV-2 virion and details our multiscale approach towards model refinement. The resulting model and methods can be applied to and enable the simulation of SARS-CoV-2 virions.

Density Functional Theory-Based Quantum Mechanics/Coarse-Grained Molecular Mechanics: Theory and Implementation

Quantum mechanics/molecular mechanics (QM/MM) is a standard computational tool for describing chemical reactivity in systems with many degrees of freedom, including polymers, enzymes, and reacting molecules in complex solvents. However, QM/MM is less suitable for systems with complex MM dynamics due to associated long relaxation times, the high computational cost of QM energy evaluations, and expensive long-range electrostatics. Recently, a systematic coarse graining of the MM part was proposed to overcome these QM/MM limitations in the form of the quantum mechanics/coarse-grained molecular mechanics (QM/CG-MM) approach. Herein, we recast QM/CG-MM in the density functional theory formalism and, by employing the force-matching variational principle, assess the method performance for the two model systems: QM CCl₄ in the MM CCl₄ liquid and the reaction of tert-butyl hypochlorite with the benzyl radical in the MM CCl₄ solvent. We find that density functional theory (DFT)-QM/CG-MM accurately reproduces DFT-QM/MM radial distribution functions and three-body correlations between the QM and CG-MM subsystems. The free-energy profile of the reaction is also described well, with an error <1-2 kcal/mol. DFT-QM/CG-MM is a general, systematic, and computationally efficient approach to include chemical reactivity in coarse-grained molecular models.

Nampt-mediated spindle sizing secures a post-anaphase increase in spindle speed required for extreme asymmetry

Meiotic divisions in oocytes are extremely asymmetric and require pre- and post-anaphase-onset phases of spindle migration. The latter induces membrane protrusion that is moulded around the spindle thereby reducing cytoplasmic loss. Here, we find that depleting the NAD biosynthetic enzyme, nicotinamide phosphoribosyl-transferase (Nampt), in mouse oocytes results in markedly longer spindles and compromises asymmetry. By analysing spindle speed in live oocytes, we identify a striking and transient acceleration after anaphase-onset that is severely blunted following Nampt-depletion. Slow-moving midzones of elongated spindles induce cortical furrowing deep within the oocyte before protrusions can form, altogether resulting in larger oocyte fragments being cleaved off. Additionally, we find that Nampt-depletion lowers NAD and ATP levels and that reducing NAD using small molecule Nampt inhibitors also compromises asymmetry. These data show that rapid midzone displacement is critical for extreme asymmetry by delaying furrowing to enable protrusions to form and link metabolic status to asymmetric division.

Meiotic cell division in oocytes is asymmetric and requires microtubule spindle migration after anaphase-onset. Here, the authors show that Nampt, an enzyme of the Nicotinamide adenine dinucleotide (NAD) biosynthetic pathway, contributes to post-anaphase spindle migration and oocyte division asymmetry by controlling spindle length.

Charge-dependent interactions of monomeric and filamentous actin with lipid bilayers

The cytoskeletal protein actin polymerizes into filaments that are essential for the mechanical stability of mammalian cells. In vitro experiments showed that direct interactions between actin filaments and lipid bilayers are possible and that the net charge of the bilayer as well as the presence of divalent ions in the buffer play an important role. In vivo, colocalization of actin filaments and divalent ions are suppressed, and cells rely on linker proteins to connect the plasma membrane to the actin network. Little is known, however, about why this is the case and what microscopic interactions are important. A deeper understanding is highly beneficial, first, to obtain understanding in the biological design of cells and, second, as a possible basis for the building of artificial cortices for the stabilization of synthetic cells. Here, we report the results of coarse-grained molecular dynamics simulations of monomeric and filamentous actin in the vicinity of differently charged lipid bilayers. We observe that charges on the lipid head groups strongly determine the ability of actin to adsorb to the bilayer. The inclusion of divalent ions leads to a reversal of the binding affinity. Our in silico results are validated experimentally by reconstitution assays with actin on lipid bilayer membranes and provide a molecular-level understanding of the actin-membrane interaction.

Computational Structural Biology: Successes, Future Directions, and Challenges

Computational biology has made powerful advances. Among these, trends in human health have been uncovered through heterogeneous 'big data' integration, and disease-associated genes were identified and classified. Along a different front, the dynamic organization of chromatin is being elucidated to gain insight into the fundamental question of genome regulation. Powerful conformational sampling methods have also been developed to yield a detailed molecular view of cellular processes. when combining these methods with the advancements in the modeling of supramolecular assemblies, including those at the membrane, we are finally able to get a glimpse into how cells' actions are regulated. Perhaps most intriguingly, a major thrust is on to decipher the mystery of how the brain is coded. Here, we aim to provide a broad, yet concise, sketch of modern aspects of computational biology, with a special focus on computational structural biology. We attempt to forecast the areas that computational structural biology will embrace in the future and the challenges that it may face. We skirt details, highlight successes, note failures, and map directions.

Correlation Between Allosteric and Orthosteric Sites

Correlation between an allosteric site and its orthosteric site refers to the phenomenon that perturbations like ligand binding, mutation, or posttranslational modifications at the allosteric site leverage variation in the orthosteric site. Understanding this kind of correlation not only helps to disclose how information is transmitted in allosteric regulation but also provides clues for allosteric drug discovery. This chapter starts with an overview of correlation studies on allosteric and orthosteric sites and then introduces recent progress in evolutionary and simulation-based dynamic studies. Discussions and perspectives on future directions are also given.

Multiscale simulation of actin filaments and actin-associated proteins

Actin is an important cytoskeletal protein that serves as a building block to form filament networks that span across the cell. These networks are orchestrated by a myriad of other cytoskeletal entities including the unbranched filament-forming protein formin and branched network-forming protein complex Arp2/3. Computational models have been able to provide insights into many important structural transitions that are involved in forming these networks, and into the nature of interactions essential for actin filament formation and for regulating the behavior of actin-associated proteins. In this review, we summarize a subset of such models that focus on the atomistic features and those that can integrate atomistic features into a larger picture in a multiscale fashion.

Advances in coarse-grained modeling of macromolecular complexes

Recent progress in coarse-grained (CG) molecular modeling and simulation has facilitated an influx of computational studies on biological macromolecules and their complexes. Given the large separation of length-scales and time-scales that dictate macromolecular biophysics, CG modeling and simulation are well-suited to bridge the microscopic and mesoscopic or macroscopic details observed from all-atom molecular simulations and experiments, respectively. In this review, we first summarize recent innovations in the development of CG models, which broadly include structure-based, knowledge-based, and dynamics-based approaches. We then discuss recent applications of different classes of CG models to explore various macromolecular complexes. Finally, we conclude with an outlook for the future in this ever-growing field of biomolecular modeling.