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The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we propose how to set the exchange rate a priori to properly balance this trade-off. We focus on gentle ensemble refinement, where the difference between the potential energy surfaces of the reference and refined models is small on a thermal scale. By relating the variance of this energy difference to the Kullback–Leibler divergence between the respective Boltzmann distributions, one can encode prior knowledge about energy uncertainties, i.e., force-field errors, in the exchange rate. The energy uncertainty is defined in the space of observables and depends on their type and number and on the thermodynamic state. We highlight the relation of gentle refinement to free energy perturbation theory. A balanced encoding of prior knowledge increases the quality and transparency of ensemble refinement. Our findings extend to non-Boltzmann distributions, where the uncertainty in energy becomes an uncertainty in information. [ABSTRACT FROM AUTHOR]

Intrinsically disordered regions (IDRs) play a pivotal role in organellar remodeling. They transduce signals across membranes, scaffold signaling complexes, and mediate vesicular traffic. Their functions are regulated by constraining conformational ensembles through specific intra- and intermolecular interactions, physical tethering, and posttranslational modifications. The endoplasmic reticulum (ER)-phagy receptor FAM134B/RETREG1, known for its reticulon homology domain (RHD), includes a substantial C-terminal IDR housing the LC3 interacting motif. Beyond engaging the autophagic machinery, the function of the FAM134B-IDR is unclear. Here, we investigate the characteristics of the FAM134B-IDR by extensive modeling and molecular dynamics simulations. We present detailed structural models for the IDR, mapping its conformational landscape in solution and membrane-anchored configurations. Our analysis reveals that depending on the membrane anchor, the IDRs collapse onto the membrane and induce positive membrane curvature to varying degrees. The charge patterns underlying this Janus-like behavior are conserved across other ER-phagy receptors. We found that IDRs alone are sufficient to sense curvature. When combined with RHDs, they intensify membrane remodeling and drive efficient protein clustering, leading to faster budding, thereby amplifying RHD remodeling functions. Our simulations provide a perspective on IDRs of FAM134B, their Janus-like membrane interactions, and the resulting modulatory functions during large-scale ER remodeling. [ABSTRACT FROM AUTHOR]

The conformational dynamics of single-stranded nucleic acids are fundamental for nucleic acid folding and function. However, their elementary chain dynamics have been difficult to resolve experimentally. Here we employ a combination of single-molecule Förster resonance energy transfer, nanosecond fluorescence correlation spectroscopy, and nanophotonic enhancement to determine the conformational ensembles and rapid chain dynamics of short single-stranded nucleic acids in solution. To interpret the experimental results in terms of end-to-end distance dynamics, we utilize the hierarchical chain growth approach, simple polymer models, and refinement with Bayesian inference to generate structural ensembles that closely align with the experimental data. The resulting chain reconfiguration times are exceedingly rapid, in the 10-ns range. Solvent viscosity-dependent measurements indicate that these dynamics of single-stranded nucleic acids exhibit negligible internal friction and are thus dominated by solvent friction. Our results provide a detailed view of the conformational distributions and rapid dynamics of single-stranded nucleic acids. By combining single-molecule spectroscopy, nanophotonic enhancement, and molecular simulations, the authors reveal the extremely rapid chain dynamics of single-stranded nucleic acids. [ABSTRACT FROM AUTHOR]

The interior of living cells is densely filled with proteins and their complexes, which perform multitudes of biological functions. We use coarse-grained simulations to reach the system sizes and time scales needed to study protein complexes and their dense solutions and to interpret experiments. To take full advantage of coarse-graining, the models have to be efficiently implemented in simulation engines that are easy to use, modify, and extend. Here, we introduce the Complexes++ simulation software to simulate a residue-level coarse-grained model for proteins and their complexes, applying a Markov chain Monte Carlo engine to sample configurations. We designed a parallelization scheme for the energy evaluation capable of simulating both dilute and dense systems efficiently. Additionally, we designed the software toolbox pycomplexes to easily set up complex topologies of multi-protein complexes and their solutions in different thermodynamic ensembles and in replica-exchange simulations, to grow flexible polypeptide structures connecting ordered protein domains, and to automatically visualize structural ensembles. Complexes++ simulations can easily be modified and they can be used for efficient explorations of different simulation systems and settings. Thus, the Complexes++ software is well suited for the integration of experimental data and for method development. [ABSTRACT FROM AUTHOR]

Lipid membranes are integral building blocks of living cells and perform a multitude of biological functions. Currently, molecular simulations of cellular-scale membrane remodeling processes at atomic resolution are extremely difficult, due to their size, complexity, and the large times-scales on which these processes occur. Instead, elastic membrane models are used to simulate membrane shapes and transitions between them and to infer their properties and functions. Unfortunately, an efficiently parallelized open-source simulation code to do so has been lacking. Here, we present TriMem, a parallel hybrid Monte Carlo simulation engine for triangulated lipid membranes. The kernels are efficiently coded in C++ and wrapped with Python for ease-of-use. The parallel implementation of the energy and gradient calculations and of Monte Carlo flip moves of edges in the triangulated membrane enable us to simulate large and highly curved membrane structures. For validation, we reproduce phase diagrams of vesicles with varying surface-to-volume ratios and area difference. We also compute the density of states to verify correct Boltzmann sampling. The software can be used to tackle a range of large-scale membrane remodeling processes as a step toward cell-scale simulations. Additionally, extensive documentation make the software accessible to the broad biophysics and computational cell biology communities. [ABSTRACT FROM AUTHOR]

Human feline leukaemia virus subgroup C receptor-related proteins 1 and 2 (FLVbibr1 and FLVbibr2) are members of the major facilitator superfamily1. Their dysfunction is linked to several clinical disorders, including PCARP, HSAN and Fowler syndrome2–7. Earlier studies concluded that FLVbibr1 may function as a haem exporter8–12, whereas FLVbibr2 was suggested to act as a haem importer13, yet conclusive biochemical and detailed molecular evidence remained elusive for the function of both transporters14–16. Here, we show that FLVbibr1 and FLVbibr2 facilitate the transport of choline and ethanolamine across the plasma membrane, using a concentration-driven substrate translocation process. Through structural and computational analyses, we have identified distinct conformational states of FLVCRs and unravelled the coordination chemistry underlying their substrate interactions. Fully conserved tryptophan and tyrosine residues form the binding pocket of both transporters and confer selectivity for choline and ethanolamine through cation–π interactions. Our findings clarify the mechanisms of choline and ethanolamine transport by FLVbibr1 and FLVbibr2, enhance our comprehension of disease-associated mutations that interfere with these vital processes and shed light on the conformational dynamics of these major facilitator superfamily proteins during the transport cycle.Structural analysis of the human choline and ethanolamine transporters FLVCR1 and FLVCR2 clarifies the mechanisms of transport, the conformational dynamics of these proteins and the disease-associated mutations that interfere with these processes. [ABSTRACT FROM AUTHOR]

We used EPR spectroscopy to characterize the structure of RNA duplexes and their internal twist, stretch and bending motions. We prepared eight 20‐base‐pair‐long RNA duplexes containing the rigid spin‐label Çm, a cytidine analogue, at two positions and acquired orientation‐selective PELDOR/DEER data. By using different frequency bands (X‐, Q‐, G‐band), detailed information about the distance and orientation of the labels was obtained and provided insights into the global conformational dynamics of the RNA duplex. We used 19F Mims ENDOR experiments on three singly Çm‐ and singly fluorine‐labeled RNA duplexes to determine the exact position of the Çm spin label in the helix. In a quantitative comparison to MD simulations of RNA with and without Çm spin labels, we found that state‐of‐the‐art force fields with explicit parameterization of the spin label were able to describe the conformational ensemble present in our experiments. The MD simulations further confirmed that the Çm spin labels are excellent mimics of cytidine inducing only small local changes in the RNA structure. Çm spin labels are thus ideally suited for high‐precision EPR experiments to probe the structure and, in conjunction with MD simulations, motions of RNA. [ABSTRACT FROM AUTHOR]

We used EPR spectroscopy to characterize the structure of RNA duplexes and their internal twist, stretch and bending motions. We prepared eight 20‐base‐pair‐long RNA duplexes containing the rigid spin‐label Çm, a cytidine analogue, at two positions and acquired orientation‐selective PELDOR/DEER data. By using different frequency bands (X‐, Q‐, G‐band), detailed information about the distance and orientation of the labels was obtained and provided insights into the global conformational dynamics of the RNA duplex. We used 19F Mims ENDOR experiments on three singly Çm‐ and singly fluorine‐labeled RNA duplexes to determine the exact position of the Çm spin label in the helix. In a quantitative comparison to MD simulations of RNA with and without Çm spin labels, we found that state‐of‐the‐art force fields with explicit parameterization of the spin label were able to describe the conformational ensemble present in our experiments. The MD simulations further confirmed that the Çm spin labels are excellent mimics of cytidine inducing only small local changes in the RNA structure. Çm spin labels are thus ideally suited for high‐precision EPR experiments to probe the structure and, in conjunction with MD simulations, motions of RNA. [ABSTRACT FROM AUTHOR]

Visual proteomics attempts to build atlases of the molecular content of cells but the automated annotation of cryo electron tomograms remains challenging. Template matching (TM) and methods based on machine learning detect structural signatures of macromolecules. However, their applicability remains limited in terms of both the abundance and size of the molecular targets. Here we show that the performance of TM is greatly improved by using template-specific search parameter optimization and by including higher-resolution information. We establish a TM pipeline with systematically tuned parameters for the automated, objective and comprehensive identification of structures with confidence 10 to 100-fold above the noise level. We demonstrate high-fidelity and high-confidence localizations of nuclear pore complexes, vaults, ribosomes, proteasomes, fatty acid synthases, lipid membranes and microtubules, and individual subunits inside crowded eukaryotic cells. We provide software tools for the generic implementation of our method that is broadly applicable towards realizing visual proteomics. Enabling visual proteomics with high-confidence 3D template matching (TM) in CryoET. 3D TM precisely localizes macromolecular complexes, individual subunits, and different functional states in situ, revealing molecular interactions within cells. [ABSTRACT FROM AUTHOR]

In response to pathogen infection, gasdermin (GSDM) proteins form membrane pores that induce a host cell death process called pyroptosis1–3. Studies of human and mouse GSDM pores have revealed the functions and architectures of assemblies comprising 24 to 33 protomers4–9, but the mechanism and evolutionary origin of membrane targeting and GSDM pore formation remain unknown. Here we determine a structure of a bacterial GSDM (bGSDM) pore and define a conserved mechanism of pore assembly. Engineering a panel of bGSDMs for site-specific proteolytic activation, we demonstrate that diverse bGSDMs form distinct pore sizes that range from smaller mammalian-like assemblies to exceptionally large pores containing more than 50 protomers. We determine a cryo-electron microscopy structure of a Vitiosangium bGSDM in an active ‘slinky’-like oligomeric conformation and analyse bGSDM pores in a native lipid environment to create an atomic-level model of a full 52-mer bGSDM pore. Combining our structural analysis with molecular dynamics simulations and cellular assays, our results support a stepwise model of GSDM pore assembly and suggest that a covalently bound palmitoyl can leave a hydrophobic sheath and insert into the membrane before formation of the membrane-spanning β-strand regions. These results reveal the diversity of GSDM pores found in nature and explain the function of an ancient post-translational modification in enabling programmed host cell death.Cryo-electron microscopy and molecular dynamics studies of a Vitiosangium gasdermin pore reveal insights into the assembly of this large and diverse family of membrane pore-forming proteins. [ABSTRACT FROM AUTHOR]

Among other improvements, the Martini 3 coarse-grained force field provides a more accurate description of the solvation of protein pockets and channels through the consistent use of various bead types and sizes. Here, we show that the representation of Na+ and Cl− ions as "tiny" (TQ5) beads limits the accessible time step to 25 fs. By contrast, with Martini 2, time steps of 30–40 fs were possible for lipid bilayer systems without proteins. This limitation is relevant for systems that require long equilibration times. We derive a quantitative kinetic model of time-integration instabilities in molecular dynamics (MD) as a function of the time step, ion concentration and mass, system size, and simulation time. We demonstrate that ion–water interactions are the main source of instability at physiological conditions, followed closely by ion–ion interactions. We show that increasing the ionic masses makes it possible to use time steps up to 40 fs with minimal impact on static equilibrium properties and dynamical quantities, such as lipid and solvent diffusion coefficients. Increasing the size of the bead representing the ions (and thus changing their hydration) also permits longer time steps. For a soluble protein, we find that increasing the mass of tiny beads also on the protein permits simulations with 30-fs time steps. The use of larger time steps in Martini 3 results in a more efficient exploration of configuration space. The kinetic model of MD simulation crashes can be used to determine the maximum allowed time step upfront for an efficient use of resources and whenever sampling efficiency is critical. [ABSTRACT FROM AUTHOR]

Proton-powered c-ring rotation in mitochondrial ATP synthase is crucial to convert the transmembrane protonmotive force into torque to drive the synthesis of adenosine triphosphate (ATP). Capitalizing on recent cryo-EM structures, we aim at a structural and energetic understanding of how functional directional rotation is achieved. We performed multi-microsecond atomistic simulations to determine the free energy profiles along the c-ring rotation angle before and after the arrival of a new proton. Our results reveal that rotation proceeds by dynamic sliding of the ring over the a-subunit surface, during which interactions with conserved polar residues stabilize distinct intermediates. Ordered water chains line up for a Grotthuss-type proton transfer in one of these intermediates. After proton transfer, a high barrier prevents backward rotation and an overall drop in free energy favors forward rotation, ensuring the directionality of c-ring rotation required for the thermodynamically disfavored ATP synthesis. The essential arginine of the a-subunit stabilizes the rotated configuration through a salt bridge with the c-ring. Overall, we describe a complete mechanism for the rotation step of the ATP synthase rotor, thereby illuminating a process critical to all life at atomic resolution. [ABSTRACT FROM AUTHOR]

Apoptosis linked Gene-2 (ALG-2) is a multifunctional intracellular Ca2+ sensor and the archetypal member of the penta-EF hand protein family. ALG-2 functions in the repair of damage to both the plasma and lysosome membranes and in COPII-dependent budding at endoplasmic reticulum exit sites (ERES). In the presence of Ca2+, ALG-2 binds to ESCRT-I and ALIX in membrane repair and to SEC31A at ERES. ALG-2 also binds directly to acidic membranes in the presence of Ca2+ by a combination of electrostatic and hydrophobic interactions. By combining giant unilamellar vesicle-based experiments and molecular dynamics simulations, we show that charge-reversed mutants of ALG-2 at these locations disrupt membrane recruitment. ALG-2 membrane binding mutants have reduced or abrogated ERES localization in response to Thapsigargin-induced Ca2+ release but still localize to lysosomes following lysosomal Ca2+ release. In vitro reconstitution shows that the ALG-2 membrane-binding defect can be rescued by binding to ESCRT-I. These data thus reveal the nature of direct Ca2+-dependent membrane binding and its interplay with Ca2+-dependent protein binding in the cellular functions of ALG-2. [ABSTRACT FROM AUTHOR]

Single-molecule localization microscopy allows practitioners to locate and track labeled molecules in biological systems. When extracting diffusion coefficients from the resulting trajectories, it is common practice to perform a linear fit on mean-squared-displacement curves. However, this strategy is suboptimal and prone to errors. Recently, it was shown that the increments between the observed positions provide a good estimate for the diffusion coefficient, and their statistics are well-suited for likelihood-based analysis methods. Here, we revisit the problem of extracting diffusion coefficients from single-particle tracking experiments subject to static noise and dynamic motion blur using the principle of maximum likelihood. Taking advantage of an efficient real-space formulation, we extend the model to mixtures of subpopulations differing in their diffusion coefficients, which we estimate with the help of the expectation–maximization algorithm. This formulation naturally leads to a probabilistic assignment of trajectories to subpopulations. We employ the theory to analyze experimental tracking data that cannot be explained with a single diffusion coefficient. We test how well a dataset conforms to the assumptions of a diffusion model and determine the optimal number of subpopulations with the help of a quality factor of known analytical distribution. To facilitate use by practitioners, we provide a fast open-source implementation of the theory for the efficient analysis of multiple trajectories in arbitrary dimensions simultaneously. [ABSTRACT FROM AUTHOR]

Despite the impending flattening of Moore's law, the system size, complexity, and length of molecular dynamics (MD) simulations keep on increasing, thanks to effective code parallelization and optimization combined with algorithmic developments. Going forward, exascale computing poses new challenges to the efficient execution and management of MD simulations. The diversity and rapid developments of hardware architectures, software environments, and MD engines make it necessary that users can easily run benchmarks to optimally set up simulations, both with respect to time-to-solution and overall efficiency. To this end, we have developed the software MDBenchmark to streamline the setup, submission, and analysis of simulation benchmarks and scaling studies. The software design is open and as such not restricted to any specific MD engine or job queuing system. To illustrate the necessity and benefits of running benchmarks and the capabilities of MDBenchmark, we measure the performance of a diverse set of 23 MD simulation systems using GROMACS 2018. We compare the scaling of simulations with the number of nodes for central processing unit (CPU)-only and mixed CPU–graphics processing unit (GPU) nodes and study the performance that can be achieved when running multiple simulations on a single node. In all these cases, we optimize the numbers of message passing interface (MPI) ranks and open multi-processing (OpenMP) threads, which is crucial to maximizing performance. Our results demonstrate the importance of benchmarking for finding the optimal system and hardware specific simulation parameters. Running MD simulations with optimized settings leads to a significant performance increase that reduces the monetary, energetic, and environmental costs of MD simulations. [ABSTRACT FROM AUTHOR]

In molecular dynamics simulations under periodic boundary conditions, particle positions are typically wrapped into a reference box. For diffusion coefficient calculations using the Einstein relation, the particle positions need to be unwrapped. Here, we show that a widely used heuristic unwrapping scheme is not suitable for long simulations at constant pressure. Improper accounting for box-volume fluctuations creates, at long times, unphysical trajectories and, in turn, grossly exaggerated diffusion coefficients. We propose an alternative unwrapping scheme that resolves this issue. At each time step, we add the minimal displacement vector according to periodic boundary conditions for the instantaneous box geometry. Here and in another paper [J. T. Bullerjahn, S. von Bülow, and G. Hummer, J. Chem. Phys. 153, 024116 (2020)], we apply the new unwrapping scheme to extensive molecular dynamics and Brownian dynamics simulation data. We provide practitioners with a formula to assess if and by how much earlier results might have been affected by the widely used heuristic unwrapping scheme. [ABSTRACT FROM AUTHOR]

Translational diffusion coefficients are routinely estimated from molecular dynamics simulations. Linear fits to mean squared displacement (MSD) curves have become the de facto standard, from simple liquids to complex biomacromolecules. Nonlinearities in MSD curves at short times are handled with a wide variety of ad hoc practices, such as partial and piece-wise fitting of the data. Here, we present a rigorous framework to obtain reliable estimates of the self-diffusion coefficient and its statistical uncertainty. We also assess in a quantitative manner if the observed dynamics is, indeed, diffusive. By accounting for correlations between MSD values at different times, we reduce the statistical uncertainty of the estimator and, thereby, increase its efficiency. With a Kolmogorov–Smirnov test, we check for possible anomalous diffusion. We provide an easy-to-use Python data analysis script for the estimation of self-diffusion coefficients. As an illustration, we apply the formalism to molecular dynamics simulation data of pure TIP4P-D water and a single ubiquitin protein. In another paper [S. von Bülow, J. T. Bullerjahn, and G. Hummer, J. Chem. Phys. 153, 021101 (2020)], we demonstrate its ability to recognize deviations from regular diffusion caused by systematic errors in a common trajectory "unwrapping" scheme that is implemented in popular simulation and visualization software. [ABSTRACT FROM AUTHOR]

Intrinsically disordered regions (IDRs) are essential for membrane receptor regulation but often remain unresolved in structural studies. TRPV4, a member of the TRP vanilloid channel family involved in thermo- and osmosensation, has a large N-terminal IDR of approximately 150 amino acids. With an integrated structural biology approach, we analyze the structural ensemble of the TRPV4 IDR and the network of antagonistic regulatory elements it encodes. These modulate channel activity in a hierarchical lipid-dependent manner through transient long-range interactions. A highly conserved autoinhibitory patch acts as a master regulator by competing with PIP2 binding to attenuate channel activity. Molecular dynamics simulations show that loss of the interaction between the PIP2-binding site and the membrane reduces the force exerted by the IDR on the structured core of TRPV4. This work demonstrates that IDR structural dynamics are coupled to TRPV4 activity and highlights the importance of IDRs for TRP channel function and regulation. An integrated structural biology approach uncovers the structural complexity of the intrinsically disordered region (IDR) within the TRPV4 ion channel. Multiple stimulatory and inhibitory elements were identified within the IDR that modulate channel activity in a lipid-dependent manner. [ABSTRACT FROM AUTHOR]

Membrane-shaping proteins characterized by reticulon homology domains play an important part in the dynamic remodelling of the endoplasmic reticulum (ER). An example of such a protein is FAM134B, which can bind LC3 proteins and mediate the degradation of ER sheets through selective autophagy (ER-phagy)1. Mutations in FAM134B result in a neurodegenerative disorder in humans that mainly affects sensory and autonomic neurons2. Here we report that ARL6IP1, another ER-shaping protein that contains a reticulon homology domain and is associated with sensory loss3, interacts with FAM134B and participates in the formation of heteromeric multi-protein clusters required for ER-phagy. Moreover, ubiquitination of ARL6IP1 promotes this process. Accordingly, disruption of Arl6ip1 in mice causes an expansion of ER sheets in sensory neurons that degenerate over time. Primary cells obtained from Arl6ip1-deficient mice or from patients display incomplete budding of ER membranes and severe impairment of ER-phagy flux. Therefore, we propose that the clustering of ubiquitinated ER-shaping proteins facilitates the dynamic remodelling of the ER during ER-phagy and is important for neuronal maintenance.The membrane-shaping protein ARL6IP1 is involved in the selective degradation of the endoplasmic reticulum, and this process depends on its ubiquitination and interaction with other membrane-shaping proteins such as FAM134B. [ABSTRACT FROM AUTHOR]

The endoplasmic reticulum (ER) undergoes continuous remodelling via a selective autophagy pathway, known as ER-phagy1. ER-phagy receptors have a central role in this process2, but the regulatory mechanism remains largely unknown. Here we report that ubiquitination of the ER-phagy receptor FAM134B within its reticulon homology domain (RHD) promotes receptor clustering and binding to lipidated LC3B, thereby stimulating ER-phagy. Molecular dynamics (MD) simulations showed how ubiquitination perturbs the RHD structure in model bilayers and enhances membrane curvature induction. Ubiquitin molecules on RHDs mediate interactions between neighbouring RHDs to form dense receptor clusters that facilitate the large-scale remodelling of lipid bilayers. Membrane remodelling was reconstituted in vitro with liposomes and ubiquitinated FAM134B. Using super-resolution microscopy, we discovered FAM134B nanoclusters and microclusters in cells. Quantitative image analysis revealed a ubiquitin-mediated increase in FAM134B oligomerization and cluster size. We found that the E3 ligase AMFR, within multimeric ER-phagy receptor clusters, catalyses FAM134B ubiquitination and regulates the dynamic flux of ER-phagy. Our results show that ubiquitination enhances RHD functions via receptor clustering, facilitates ER-phagy and controls ER remodelling in response to cellular demands.Ubiquitination of the receptor FAM134B regulates ER-phagy and remodelling of the endoplasmic reticulum in response to cellular demands. [ABSTRACT FROM AUTHOR]

The approximately 120 MDa mammalian nuclear pore complex (NPC) acts as a gatekeeper for the transport between the nucleus and cytosol1. The central channel of the NPC is filled with hundreds of intrinsically disordered proteins (IDPs) called FG-nucleoporins (FG-NUPs)2,3. Although the structure of the NPC scaffold has been resolved in remarkable detail, the actual transport machinery built up by FG-NUPs—about 50 MDa—is depicted as an approximately 60-nm hole in even highly resolved tomograms and/or structures computed with artificial intelligence4–11. Here we directly probed conformations of the vital FG-NUP98 inside NPCs in live cells and in permeabilized cells with an intact transport machinery by using a synthetic biology-enabled site-specific small-molecule labelling approach paired with highly time-resolved fluorescence microscopy. Single permeabilized cell measurements of the distance distribution of FG-NUP98 segments combined with coarse-grained molecular simulations of the NPC allowed us to map the uncharted molecular environment inside the nanosized transport channel. We determined that the channel provides—in the terminology of the Flory polymer theory12—a ‘good solvent’ environment. This enables the FG domain to adopt expanded conformations and thus control transport between the nucleus and cytoplasm. With more than 30% of the proteome being formed from IDPs, our study opens a window into resolving disorder–function relationships of IDPs in situ, which are important in various processes, such as cellular signalling, phase separation, ageing and viral entry.Previously shown as a 60-nm hole in the nuclear pore complex, the transport machinery by FG-nucleoporins is mapped. [ABSTRACT FROM AUTHOR]

In single-molecule force spectroscopy experiments, a biomolecule is attached to a force probe via polymer linkers and the total extension of the molecule plus apparatus is monitored as a function of time. In a typical unfolding experiment at constant force, the total extension jumps between two values that correspond to the folded and unfolded states of the molecule. For several biomolecular systems, the committor, which is the probability to fold starting from a given extension, has been used to extract the molecular activation barrier (a technique known as "committor inversion"). In this work, we study the influence of the force probe, which is much larger than the molecule being measured, on the activation barrier obtained by committor inversion. We use a two-dimensional framework in which the diffusion coefficient of the molecule and of the pulling device can differ. We systematically study the free energy profile along the total extension obtained from the committor by numerically solving the Onsager equation and using Brownian dynamics simulations. We analyze the dependence of the extracted barrier on the linker stiffness, molecular barrier height, and diffusion anisotropy and, thus, establish the range of validity of committor inversion. Along the way, we showcase the committor of 2-dimensional diffusive models and illustrate how it is affected by barrier asymmetry and diffusion anisotropy. [ABSTRACT FROM AUTHOR]

The steady emergence of SARS-CoV-2 variants gives us a real-time view of the interplay between viral evolution and the host immune defense. The spike protein of SARS-CoV-2 is the primary target of antibodies. Here, we show that steric accessibility to antibodies provides a strong predictor of mutation activity in the spike protein of SARS-CoV-2 variants, including Omicron. We introduce an antibody accessibility score (AAS) that accounts for the steric shielding effect of glycans at the surface of spike. We find that high values of the AAS correlate strongly with the sites of mutations in the spike proteins of newly emerging SARS-CoV-2 variants. We use the AAS to assess the escapability of variant spike proteins, i.e., their ability to escape antibody-based immune responses. The high calculated escapability of the Omicron variant BA.5 with respect to both wild-type (WT) vaccination and BA.1 infection is consistent with its rapid spread despite high rates of vaccination and prior infection with earlier variants. We calculated the AAS from structural and molecular dynamics simulation data that were available early in the pandemic, in the spring of 2020. The AAS thus allows us to prospectively assess the ability of variant spike proteins to escape antibody-based immune responses and to pinpoint regions of expected mutation activity in future variants. Author summary: Since the beginning of the COVID-19 pandemic, the steady emergence of new variants has thwarted the efficacy of vaccines and has made herd immunity a distant prospect. We show that steric accessibility to antibodies beyond the glycan shield is an excellent predictor of mutated positions in variants of concern, including Omicron. By using data from molecular dynamics simulations of a SARS-CoV-2 spike protein model constructed early in the pandemic, we demonstrate the power to predict sites of future mutation activity. [ABSTRACT FROM AUTHOR]

Recent waves of COVID-19 correlate with the emergence of the Delta and the Omicron variant. We report that the Spike trimer acts as a highly dynamic molecular caliper, thereby forming up to three tight bonds through its RBDs with ACE2 expressed on the cell surface. The Spike of both Delta and Omicron (B.1.1.529) Variant enhance and markedly prolong viral attachment to the host cell receptor ACE2, as opposed to the early Wuhan-1 isolate. Delta Spike shows rapid binding of all three Spike RBDs to three different ACE2 molecules with considerably increased bond lifetime when compared to the reference strain, thereby significantly amplifying avidity. Intriguingly, Omicron (B.1.1.529) Spike displays less multivalent bindings to ACE2 molecules, yet with a ten time longer bond lifetime than Delta. Delta and Omicron (B.1.1.529) Spike variants enhance and prolong viral attachment to the host, which likely not only increases the rate of viral uptake, but also enhances the resistance of the variants against host-cell detachment by shear forces such as airflow, mucus or blood flow. We uncover distinct binding mechanisms and strategies at single-molecule resolution, employed by circulating SARS-CoV-2 variants to enhance infectivity and viral transmission. Combining high-speed AFM, single molecule recognition force spectroscopy, and molecular dynamics simulations Zhu, Canena, Sikora et al. characterize the interaction dynamics of the trimeric spike protein of SARS-CoV-2 wt, and delta and omicron variants with its entry receptor ACE2. While delta variant increases avidity by multivalent binding to ACE2, omicron variant shows an extended binding lifetime. [ABSTRACT FROM AUTHOR]

Bacteria utilize small extracellular molecules to communicate in order to collectively coordinate their behaviors in response to the population density. Autoinducer‐2 (AI‐2), a universal molecule for both intra‐ and inter‐species communication, is involved in the regulation of biofilm formation, virulence, motility, chemotaxis, and antibiotic resistance. While many studies have been devoted to understanding the biosynthesis and sensing of AI‐2, very little information is available on its export. The protein TqsA from Escherichia coli, which belongs to the AI‐2 exporter superfamily, has been shown to export AI‐2. Here, we report the cryogenic electron microscopic structures of two AI‐2 exporters (TqsA and YdiK) from E. coli at 3.35 Å and 2.80 Å resolutions, respectively. Our structures suggest that the AI‐2 exporter exists as a homo‐pentameric complex. In silico molecular docking and native mass spectrometry experiments were employed to demonstrate the interaction between AI‐2 and TqsA, and the results highlight the functional importance of two helical hairpins in substrate binding. We propose that each monomer works as an independent functional unit utilizing an elevator‐type transport mechanism. Synopsis: Bacteria communicate by releasing chemicals known as autoinducers; these autoinducers can modify colony behavior. This article describes the structure of two autoinducer‐2 (AI‐2) exporters from E. coli, proposing a mechanism for their activity. Cryo‐EM reveals the AI‐2 exporters TqsA and YdiK form pentameric protein complexes.Two helix–turn–helix motifs mediate substrate binding suggesting an elevator‐like transport mechanism.A similar mechanism to glutamate transporters is proposed in which each protomer works independently. [ABSTRACT FROM AUTHOR]

We show how accurate rates of formation and dissociation of peptide dimers can be calculated using direct transition counting (DTC) from replica-exchange molecular dynamics (REMD) simulations. First, continuous trajectories corresponding to system replicas evolving at different temperatures are used to assign conformational states. Second, we analyze the entire REMD data to calculate the corresponding rates at each temperature directly from the number of transition counts. Finally, we compare the kinetics extracted directly, using the DTC method, with indirect estimations based on trajectory likelihood maximization using short-time propagators and on decay rates of state autocorrelation functions. For systems with relatively low-dimensional intrinsic conformational dynamics, the DTC method is simple to implement and leads to accurate temperature-dependent rates.We apply the DTC rate-extraction method to all-atom REMD simulations of dimerization of amyloid-forming NNQQ tetrapetides in explicit water. In an assessment of the REMD sampling efficiency with respect to standard MD, we find a gain of more than a factor of two at the lowest temperature. [ABSTRACT FROM AUTHOR]

Transition path sampling is a powerful tool in the study of rare events. Shooting trial trajectories from configurations along existing transition paths proved particularly efficient in the sampling of reactive trajectories. However, most shooting attempts tend not to result in transition paths, in particular in cases where the transition dynamics has diffusive character. To overcome the resulting efficiency problem, we developed an algorithm for "shooting from the top." We first define a shooting range through which all paths have to pass and then shoot off trial trajectories only from within this range. For a well chosen shooting range, nearly every shot is successful, resulting in an accepted transition path. To deal with multiple mechanisms, weighted shooting ranges can be used. To cope with the problem of unsuitably placed shooting ranges, we developed an algorithm that iteratively improves the location of the shooting range. The transition path sampling procedure is illustrated for models of diffusive and Langevin dynamics. The method should be particularly useful in cases where the transition paths are long so that only relatively few shots are possible, yet reasonable order parameters are known. [ABSTRACT FROM AUTHOR]

Post‐translational modifications (PTMs) have emerged as key modulators of protein phase separation and have been linked to protein aggregation in neurodegenerative disorders. The major aggregating protein in amyotrophic lateral sclerosis and frontotemporal dementia, the RNA‐binding protein TAR DNA‐binding protein (TDP‐43), is hyperphosphorylated in disease on several C‐terminal serine residues, a process generally believed to promote TDP‐43 aggregation. Here, we however find that Casein kinase 1δ‐mediated TDP‐43 hyperphosphorylation or C‐terminal phosphomimetic mutations reduce TDP‐43 phase separation and aggregation, and instead render TDP‐43 condensates more liquid‐like and dynamic. Multi‐scale molecular dynamics simulations reveal reduced homotypic interactions of TDP‐43 low‐complexity domains through enhanced solvation of phosphomimetic residues. Cellular experiments show that phosphomimetic substitutions do not affect nuclear import or RNA regulatory functions of TDP‐43, but suppress accumulation of TDP‐43 in membrane‐less organelles and promote its solubility in neurons. We speculate that TDP‐43 hyperphosphorylation may be a protective cellular response to counteract TDP‐43 aggregation. Synopsis: C‐terminal hyperphosphorylation of TDP‐43, a pathological hallmark of several neurodegenerative disorders, enhances the liquidity of TDP‐43 condensates and suppresses TDP‐43 condensation and aggregation, shedding a new light on this TDP‐43 disease‐linked posttranslational modification. TDP‐43 phosphorylation by CK1δ or C‐terminal phosphomimetic mutations suppress TDP‐43 phase separation and aggregation.C‐terminal phosphomimetic mutations enhance liquidity and dynamics of TDP‐43 condensates.Suppression of phase separation is associated with loss of protein‐protein interactions in the C‐terminus and enhanced solvation of negatively charged groups.TDP‐43 bearing C‐terminal phosphomimetic mutations fails to condense into stress‐induced membrane‐less compartments and remains dispersed. [ABSTRACT FROM AUTHOR]

During the co-translational assembly of protein complexes, a fully synthesized subunit engages with the nascent chain of a newly synthesized interaction partner. Such events are thought to contribute to productive assembly, but their exact physiological relevance remains underexplored. Here, we examine structural motifs contained in nucleoporins for their potential to facilitate co-translational assembly. We experimentally test candidate structural motifs and identify several previously unknown co-translational interactions. We demonstrate by selective ribosome profiling that domain invasion motifs of beta-propellers, coiled-coils, and short linear motifs may act as co-translational assembly domains. Such motifs are often contained in proteins that are members of multiple complexes (moonlighters) and engage with closely related paralogs. Surprisingly, moonlighters and paralogs assemble co-translationally in only some but not all of the relevant biogenesis pathways. Our results highlight the regulatory complexity of assembly pathways. The biogenesis of nuclear pores imposes a logistic challenge for cells. Here, the authors investigate structural motifs for co-translational interactions in nucleoporins and find that co-translational assembly events differ between paralogous assembly pathways thus contributing to faithful assembly. [ABSTRACT FROM AUTHOR]

Transport of lipids across membranes is fundamental for diverse biological pathways in cells. Multiple ion-coupled transporters take part in lipid translocation, but their mechanisms remain largely unknown. Major facilitator superfamily (MFS) lipid transporters play central roles in cell wall synthesis, brain development and function, lipids recycling, and cell signaling. Recent structures of MFS lipid transporters revealed overlapping architectural features pointing towards a common mechanism. Here we used cysteine disulfide trapping, molecular dynamics simulations, mutagenesis analysis, and transport assays in vitro and in vivo, to investigate the mechanism of LtaA, a proton-dependent MFS lipid transporter essential for lipoteichoic acid synthesis in the pathogen Staphylococcus aureus. We reveal that LtaA displays asymmetric lateral openings with distinct functional relevance and that cycling through outward- and inward-facing conformations is essential for transport activity. We demonstrate that while the entire amphipathic central cavity of LtaA contributes to lipid binding, its hydrophilic pocket dictates substrate specificity. We propose that LtaA catalyzes lipid translocation by a 'trap-and-flip' mechanism that might be shared among MFS lipid transporters. LtaA catalyzes glycolipid translocation by a 'trap-and-flip' mechanism, pointing to a shared mechanistic model among MFS lipid transporters. Asymmetric lateral openings allow access of the entire lipid substrate to the amphipathic central cavity. [ABSTRACT FROM AUTHOR]

The demands on the accuracy of force fields for classical molecular dynamics simulations are steadily growing as larger and more complex systems are studied over longer times. One way to meet these growing demands is to hand over the learning of force fields and their parameters to machines in a systematic (semi)automatic manner. Doing so, we can take full advantage of exascale computing, the increasing availability of experimental data, and advances in quantum mechanical computations and the calculation of experimental observables from molecular ensembles. Here, we discuss and illustrate the challenges one faces in this endeavor and explore a way forward by adapting the Bayesian inference of ensembles (BioEn) method [Hummer and Köfinger, J. Chem. Phys. (2015)] for force field parameterization. In the Bayesian inference of force fields (BioFF) method developed here, the optimization problem is regularized by a simplified prior on the force field parameters and an entropic prior acting on the ensemble. The latter compensates for the unavoidable over simplifications in the parameter prior. We determine optimal force field parameters using an iterative predictor–corrector approach, in which we run simulations, determine the reference ensemble using the weighted histogram analysis method (WHAM), and update the force field according to the BioFF posterior. We illustrate this approach for a simple polymer model, using the distance between two labeled sites as the experimental observable. By systematically resolving force field issues, instead of just reweighting a structural ensemble, the BioFF corrections extend to observables not included in ensemble reweighting. We envision future force field optimization as a formalized, systematic, and (semi)automatic machine-learning effort that incorporates a wide range of data from experiment and high-level quantum chemical calculations, and takes advantage of exascale computing resources. [ABSTRACT FROM AUTHOR]

Multidrug and toxic compound extrusion (MATE) transporters are widespread in all domains of life. Bacterial MATE transporters confer multidrug resistance by utilizing an electrochemical gradient of H+ or Na+ to export xenobiotics across the membrane. Despite the availability of X-ray structures of several MATE transporters, a detailed understanding of the transport mechanism has remained elusive. Here we report the crystal structure of a MATE transporter from Aquifex aeolicus at 2.0-Å resolution. In light of its phylogenetic placement outside of the diversity of hitherto-described MATE transporters and the lack of conserved acidic residues, this protein may represent a subfamily of prokaryotic MATE transporters, which was proven by phylogenetic analysis. Furthermore, the crystal structure and substrate docking results indicate that the substrate binding site is located in the N bundle. The importance of residues surrounding this binding site was demonstrated by structure-based site-directed mutagenesis. We suggest that Aq_128 is functionally similar but structurally diverse from DinF subfamily transporters. Our results provide structural insights into the MATE transporter, which further advances our global understanding of this important transporter family. [ABSTRACT FROM AUTHOR]

We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations. [ABSTRACT FROM AUTHOR]

Degradation of the endoplasmic reticulum (ER) via selective autophagy (ER‐phagy) is vital for cellular homeostasis. We identify FAM134A/RETREG2 and FAM134C/RETREG3 as ER‐phagy receptors, which predominantly exist in an inactive state under basal conditions. Upon autophagy induction and ER stress signal, they can induce significant ER fragmentation and subsequent lysosomal degradation. FAM134A, FAM134B/RETREG1, and FAM134C are essential for maintaining ER morphology in a LC3‐interacting region (LIR)‐dependent manner. Overexpression of any FAM134 paralogue has the capacity to significantly augment the general ER‐phagy flux upon starvation or ER‐stress. Global proteomic analysis of FAM134 overexpressing and knockout cell lines reveals several protein clusters that are distinctly regulated by each of the FAM134 paralogues as well as a cluster of commonly regulated ER‐resident proteins. Utilizing pro‐Collagen I, as a shared ER‐phagy substrate, we observe that FAM134A acts in a LIR‐independent manner and compensates for the loss of FAM134B and FAM134C, respectively. FAM134C instead is unable to compensate for the loss of its paralogues. Taken together, our data show that FAM134 paralogues contribute to common and unique ER‐phagy pathways. Synopsis: Selective degradation of the ER is essential to maintain ER homeostasis. This study characterizes FAM134A and FAM134C as ER‐phagy receptors involved in Collagen quality control and identifies protein clusters differentially regulated by FAM134 paralogues. FAM134A and FAM134C are existent in an inhibited state under basal conditions and get activated upon stress/environmental changes.Dynamic of the RHD domain defines the ER fragmentation ability of FAM134 paralogues.FAM134 paralogues collectively drive Collagen I quality control, while managing additional, distinct substrate clusters. [ABSTRACT FROM AUTHOR]

New drugs are urgently needed to combat the global TB epidemic. Targeting simultaneously multiple respiratory enzyme complexes of Mycobacterium tuberculosis is regarded as one of the most effective treatment options to shorten drug administration regimes, and reduce the opportunity for the emergence of drug resistance. During infection and proliferation, the cytochrome bd oxidase plays a crucial role for mycobacterial pathophysiology by maintaining aerobic respiration at limited oxygen concentrations. Here, we present the cryo-EM structure of the cytochrome bd oxidase from M. tuberculosis at 2.5 Å. In conjunction with atomistic molecular dynamics (MD) simulation studies we discovered a previously unknown MK-9-binding site, as well as a unique disulfide bond within the Q-loop domain that defines an inactive conformation of the canonical quinol oxidation site in Actinobacteria. Our detailed insights into the long-sought atomic framework of the cytochrome bd oxidase from M. tuberculosis will form the basis for the design of highly specific drugs to act on this enzyme. M. tuberculosis cytochrome bd oxidase is of interest as a TB drug target. Here, the authors present the 2.5 Å cryo-EM structure of M. tuberculosis cytochrome bd oxidase and identify a disulfide bond within the canonical quinol binding and oxidation domain (Q-loop) and a menaquinone-9 binding site at heme b595. [ABSTRACT FROM AUTHOR]

Binding of the spike protein of SARS-CoV-2 to the human angiotensinconverting enzyme 2 (ACE2) receptor triggers translocation of the virus into cells. Both the ACE2 receptor and the spike protein are heavily glycosylated, including at sites near their binding interface. We built fully glycosylated models of the ACE2 receptor bound to the receptor binding domain (RBD) of the SARS-CoV-2 spike protein. Using atomistic molecular dynamics (MD) simulations, we found that the glycosylation of the human ACE2 receptor contributes substantially to the binding of the virus. Interestingly, the glycans at two glycosylation sites, N90 and N322, have opposite effects on spike protein binding. The glycan at the N90 site partly covers the binding interface of the spike RBD. Therefore, this glycan can interfere with the binding of the spike protein and protect against docking of the virus to the cell. By contrast, the glycan at the N322 site interacts tightly with the RBD of the ACE2-bound spike protein and strengthens the complex. Remarkably, the N322 glycan binds to a conserved region of the spike protein identified previously as a cryptic epitope for a neutralizing antibody. By mapping the glycan binding sites, our MD simulations aid in the targeted development of neutralizing antibodies and SARS-CoV-2 fusion inhibitors. [ABSTRACT FROM AUTHOR]

Drug delivery mitigates toxic side effects and poor pharmacokinetics of life-saving therapeutics and enhances treatment efficacy. However, direct cytoplasmic delivery of drugs and vaccines into cells has remained out of reach. We find that liposomes studded with 0.8-nm-wide carbon nanotube porins (CNTPs) function as efficient vehicles for direct cytoplasmic drug delivery by facilitating fusion of lipid membranes and complete mixing of the membrane material and vesicle interior content. Fusion kinetics data and coarse-grained molecular dynamics simulations reveal an unusual mechanism where CNTP dimers tether the vesicles, pull the membranes into proximity, and then fuse their outer and inner leaflets. Liposomes containing CNTPs in their membranes and loaded with an anticancer drug, doxorubicin, were effective in delivering the drug to cancer cells, killing up to 90% of them. Our results open an avenue for designing efficient drug delivery carriers compatible with a wide range of therapeutics. [ABSTRACT FROM AUTHOR]

The primary immunological target of COVID-19 vaccines is the SARS-CoV-2 spike (S) protein. S is exposed on the viral surface and mediates viral entry into the host cell. To identify possible antibody binding sites, we performed multi-microsecond molecular dynamics simulations of a 4.1 million atom system containing a patch of viral membrane with four full-length, fully glycosylated and palmitoylated S proteins. By mapping steric accessibility, structural rigidity, sequence conservation, and generic antibody binding signatures, we recover known epitopes on S and reveal promising epitope candidates for structure-based vaccine design. We find that the extensive and inherently flexible glycan coat shields a surface area larger than expected from static structures, highlighting the importance of structural dynamics. The protective glycan shield and the high flexibility of its hinges give the stalk overall low epitope scores. Our computational epitope-mapping procedure is general and should thus prove useful for other viral envelope proteins whose structures have been characterized. Author summary: The SARS-CoV-2 virus has caused a global health crisis. The spike protein exposed at its surface is key for infection and the primary antibody target. However, spike is covered by highly mobile glycan molecules that could impair antibody binding. To identify accessible epitopes, we performed molecular dynamics simulations of an atomistic model of glycosylated spike embedded in a membrane. By combining extensive simulations with bioinformatics analyses, we recovered known antibody binding sites and identified several epitope candidates as targets for further vaccine development. [ABSTRACT FROM AUTHOR]

Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space. [ABSTRACT FROM AUTHOR]

The structure and flexibility of RNA depends sensitively on the microenvironment. Using pulsed electron‐electron double‐resonance (PELDOR)/double electron‐electron resonance (DEER) spectroscopy combined with advanced labeling techniques, we show that the structure of double‐stranded RNA (dsRNA) changes upon internalization into Xenopus lævis oocytes. Compared to dilute solution, the dsRNA A‐helix is more compact in cells. We recapitulate this compaction in a densely crowded protein solution. Atomic‐resolution molecular dynamics simulations of dsRNA semi‐quantitatively capture the compaction, and identify non‐specific electrostatic interactions between proteins and dsRNA as a possible driver of this effect. [ABSTRACT FROM AUTHOR]

The structure and flexibility of RNA depends sensitively on the microenvironment. Using pulsed electron‐electron double‐resonance (PELDOR)/double electron‐electron resonance (DEER) spectroscopy combined with advanced labeling techniques, we show that the structure of double‐stranded RNA (dsRNA) changes upon internalization into Xenopus lævis oocytes. Compared to dilute solution, the dsRNA A‐helix is more compact in cells. We recapitulate this compaction in a densely crowded protein solution. Atomic‐resolution molecular dynamics simulations of dsRNA semi‐quantitatively capture the compaction, and identify non‐specific electrostatic interactions between proteins and dsRNA as a possible driver of this effect. [ABSTRACT FROM AUTHOR]

Desmosomes are cell-cell junctions that link tissue cells experiencing intense mechanical stress. Although the structure of the desmosomal cadherins is known, the desmosome architecture--which is essential for mediating numerous functions--remains elusive. Here, we recorded cryo-electron tomograms (cryo-ET) in which individual cadherins can be discerned; they appear variable in shape, spacing, and tilt with respect to the membrane. The resulting subtomogram average reaches a resolution of ~26 Å, limited by the inherent flexibility of desmosomes. To address this challenge typical of dynamic biological assemblies, we combine sub-tomogram averaging with atomistic molecular dynamics (MD) simulations. We generate models of possible cadherin arrangements and perform an in silico screening according to biophysical and structural properties extracted from MD simulation trajectories. We find a truss-like arrangement of cadherins that resembles the characteristic footprint seen in the electron micrograph. The resulting model of the desmosomal architecture explains their unique biophysical properties and strength. [ABSTRACT FROM AUTHOR]

Cells maintain membrane fluidity by regulating lipid saturation, but the molecular mechanisms of this homeoviscous adaptation remain poorly understood. We have reconstituted the core machinery for regulating lipid saturation in baker's yeast to study its molecular mechanism. By combining molecular dynamics simulations with experiments, we uncover a remarkable sensitivity of the transcriptional regulator Mga2 to the abundance, position, and configuration of double bonds in lipid acyl chains, and provide insights into the molecular rules of membrane adaptation. Our data challenge the prevailing hypothesis that membrane fluidity serves as the measured variable for regulating lipid saturation. Rather, we show that Mga2 senses the molecular lipid-packing density in a defined region of the membrane. Our findings suggest that membrane property sensors have evolved remarkable sensitivities to highly specific aspects of membrane structure and dynamics, thus paving the way toward the development of genetically encoded reporters for such properties in the future. Cells maintain membrane fluidity by regulating lipid saturation, but the molecular mechanisms of this homeoviscous adaptation remain poorly understood. Here authors reconstituted the core machinery for regulating lipid saturation in baker's yeast to directly characterize its response to defined membrane environments and uncover its mode-of-action. [ABSTRACT FROM AUTHOR]

We derive simple analytical expressions for the error and computational efficiency of simulated tempering (ST) simulations. The theory applies to the important case of systems whose dynamics at long times is dominated by the slow interconversion between two metastable states. An extension to the multistate case is described. We show that the relative gain in efficiency of ST simulations over regular molecular dynamics (MD) or Monte Carlo (MC) simulations is given by the ratio of their reactive fluxes, i.e., the number of transitions between the two states summed over all ST temperatures divided by the number of transitions at the single temperature of the MD or MC simulation. This relation for the efficiency is derived for the limit in which changes in the ST temperature are fast compared to the two-state transitions. In this limit, ST is most efficient. Our expression for the maximum efficiency gain of ST simulations is essentially identical to the corresponding expression derived by us for replica exchange MD and MC simulations [E. Rosta and G. Hummer, J. Chem. Phys. 131, 165102 (2009)] on a different route. We find quantitative agreement between predicted and observed efficiency gains in a test against ST and replica exchange MC simulations of a two-dimensional Ising model. Based on the efficiency formula, we provide recommendations for the optimal choice of ST simulation parameters, in particular, the range and number of temperatures, and the frequency of attempted temperature changes. [ABSTRACT FROM AUTHOR]

We derive simple analytical expressions for the error and computational efficiency of replica exchange molecular dynamics (REMD) simulations (and by analogy replica exchange Monte Carlo simulations). The theory applies to the important case of systems whose dynamics at long times is dominated by the slow interconversion between two metastable states. As a specific example, we consider the folding and unfolding of a protein. The efficiency is defined as the rate with which the error in an estimated equilibrium property, as measured by the variance of the estimator over repeated simulations, decreases with simulation time. For two-state systems, this rate is in general independent of the particular property. Our main result is that, with comparable computational resources used, the relative efficiency of REMD and molecular dynamics (MD) simulations is given by the ratio of the number of transitions between the two states averaged over all replicas at the different temperatures, and the number of transitions at the single temperature of the MD run. This formula applies if replica exchange is frequent, as compared to the transition times. High efficiency of REMD is thus achieved by including replica temperatures in which the frequency of transitions is higher than that at the temperature of interest. In tests of the expressions for the error in the estimator, computational efficiency, and the rate of equilibration we find quantitative agreement with the results both from kinetic models of REMD and from actual all-atom simulations of the folding of a peptide in water. [ABSTRACT FROM AUTHOR]