Multiphasic architectures are found ubiquitously in biomolecular condensates and are thought to have important implications for the organization of multiple chemical reactions within the same compartment. Many of these multiphasic condensates contain RNA in addition to proteins. Here, we investigate the importance of different interactions in multiphasic condensates comprising two different proteins and RNA using computer simulations with a residue-resolution coarse-grained model of proteins and RNA. We find that in multilayered condensates containing RNA in both phases, protein-RNA interactions dominate, with aromatic residues and arginine forming the key stabilizing interactions. The total aromatic and arginine content of the two proteins must be appreciably different for distinct phases to form, and we show that this difference increases as the system is driven toward greater multiphasicity. Using the trends observed in the different interaction energies of this system, we demonstrate that we can also construct multilayered condensates with RNA preferentially concentrated in one phase. The "rules" identified can thus enable the design of synthetic multiphasic condensates to facilitate further study of their organization and function.

Biological condensates are known to retain a large fraction of water to remain in a liquid and reversible state. Local solvation contributions from water hydrating hydrophilic and hydrophobic protein surfaces were proposed to play a prominent role for the formation of condensates through liquid-liquid phase separation (LLPS). However, although the total free energy is accessible by calorimetry, the partial solvent contributions to the free energy changes upon LLPS remained experimentally inaccessible so far. Here, we show that the recently developed THz calorimetry approach allows to quantify local hydration enthalpy and entropy changes upon LLPS of α-elastin in real time, directly from experimental THz spectroscopy data. We find that hydrophobic solvation dominates the entropic solvation term, whereas hydrophilic solvation mainly contributes to the enthalpy. Both terms are in the order of hundreds of kJ/mol, which is more than one order of magnitude larger than the total free energy changes at play during LLPS. However, since we show that entropy/enthalpy mostly compensates, a small entropy/enthalpy imbalance is sufficient to tune LLPS. Theoretically, a balance was proposed before. Here we present experimental evidence based on our spectroscopic approach. We finally show that LLPS can be steered by inducing small changes of solvation entropy/enthalpy compensation via concentration or temperature in α-elastin.

Molecular dynamics simulations have strongly matured as a method to study biomolecular processes. Their validity, however, is determined by the accuracy of the underlying force fields that describe the forces between all atoms. In this article, we review the development of nucleic acids force fields. We describe the early attempts in the 1990s and emphasize their strong influence on recent force fields. State-of-the-art force fields still use the same Lennard-Jones parameters derived 25 years ago in spite of the fact that these parameters were in general not fitted for nucleic acids. In addition, electrostatic parameters also are deprecated, which may explain some of the current force field deficiencies. We compare different force fields for various systems and discuss new tests of the recently developed Tumuc1 force field. The OL-force fields and Tumuc1 are arguably the best force fields to describe the DNA double helix. However, no force field is flawless. In particular, the description of sugar-puckering remains a problem for nucleic acids force fields. Future refinements are required, so we review methods for force field refinement and give an outlook to the future of force fields.

The relationship between chromatin architecture and function defines a central problem in biology and medicine. Many computational chromatin models with atomic, coarse-grained, mesoscale, and polymer resolution have been used to shed light onto the mechanisms that dictate genome folding and regulation of gene expression. The associated simulation techniques range from Monte Carlo to molecular, Brownian, and Langevin dynamics. Here, we present an efficient Compute Unified Device Architecture (CUDA) implementation of Brownian dynamics (BD) to simulate chromatin fibers at the nucleosome resolution with our chromatin mesoscale model. With the CUDA implementation for computer architectures with graphic processing units (GPUs), we significantly accelerate compute-intensive hydrodynamic tensor calculations in the BD simulations by massive parallelization, boosting the performance a hundred-fold compared with central processing unit calculations. We validate our BD simulation approach by reproducing experimental trends on fiber diffusion and structure as a function of salt, linker histone binding, and histone-tail composition, as well as Monte Carlo equilibrium sampling results. Our approach proves to be physically accurate with performance that makes feasible the study of chromatin fibers in the range of kb or hundreds of nucleosomes (small gene). Such simulations are essential to advance the study of biological processes such as gene regulation and aberrant genome-structure related diseases.

A single mutation from aspartate to glycine at position 614 has dominated all circulating variants of the severe acute respiratory syndrome coronavirus 2. D614G mutation induces structural changes in the spike (S) protein that strengthen the virus infectivity. Here, we use molecular dynamics simulations to dissect the effects of mutation and 630-loop rigidification on S-protein structure. The introduction of the mutation orders the 630-loop structure and thereby induces global structural changes toward the cryoelectron microscopy structure of the D614G S-protein. The ordered 630-loop weakens local interactions between the 614th residue and others in contrast to disordered structures in the wild-type protein. The mutation allosterically alters global interactions between receptor-binding domains, forming an asymmetric and mobile down conformation and facilitating transitions toward up conformation. The loss of salt bridge between D614 and K854 upon the mutation generally stabilizes S-protein protomer, including the fusion peptide proximal region that mediates membrane fusion. Understanding the molecular basis of D614G mutation is crucial as it dominates in all variants of concern, including Delta and Omicron.

Over a century ago, physicists started broadly relying on theoretical models to guide new experiments. Soon thereafter, chemists began doing the same. Now, biological research enters a new era when experiment and theory walk hand in hand. Novel software and specialized hardware became essential to understand experimental data and propose new models. In fact, current petascale computing resources already allow researchers to reach unprecedented levels of simulation throughput to connect in silico and in vitro experiments. The reduction in cost and improved access allowed a large number of research groups to adopt supercomputing resources and techniques. Here, we outline how large-scale computing has evolved to expand decades-old research, spark new research efforts, and continuously connect simulation and observation. For instance, multiple publicly and privately funded groups have dedicated extensive resources to develop artificial intelligence tools for computational biophysics, from accelerating quantum chemistry calculations to proposing protein structure models. Moreover, advances in computer hardware have accelerated data processing from single-molecule experimental observations and simulations of chemical reactions occurring throughout entire cells. The combination of software and hardware has opened the way for exascale computing and the production of the first public exascale supercomputer, Frontier, inaugurated by the Oak Ridge National Laboratory in 2022. Ultimately, the popularization and development of computational techniques and the training of researchers to use them will only accelerate the diversification of tools and learning resources for future generations.

The efficient permeation across the Gram-negative bacterial membrane is an important step in the overall process of antibacterial action of a molecule and the one that has posed a significant hurdle on the way toward approved antibiotics. Predicting the permeability for a large library of molecules and assessing the effect of different molecular transformations on permeation rates of a given molecule is critical to the development of effective antibiotics. We present a computational approach for obtaining estimates of molecular permeability through a porin channel in a matter of hours using a Brownian dynamics approach. The fast sampling using a temperature acceleration scheme enables the approximate estimation of permeability using the inhomogeneous solubility diffusion model. Although the method is a significant approximation to similar all-atom approaches tested previously, we show that the present approach predicts permeabilities that correlate fairly well with the respective experimental permeation rates from liposome swelling experiments and accumulation rates from antibiotic accumulation assays, and is significantly, i.e., about 14 times, faster compared with a previously reported approach. The possible applications of the scheme in high-throughput screening for fast permeators are discussed.

We describe a complete implementation of Martini 2 and Martini 3 in the OpenMM molecular dynamics software package. Martini is a widely used coarse-grained force field with applications in biomolecular simulation, materials, and broader areas of chemistry. It is implemented as a force field but makes extensive use of facilities unique to the GROMACS software, including virtual sites and bonded terms that are not commonly used in standard atomistic force fields. OpenMM is a flexible molecular dynamics package widely used for methods development and is competitive in speed on GPUs with other commonly used packages. OpenMM has facilities to easily implement new force field terms, external forces and fields, and other nonstandard features, which we use to implement all force field terms used in Martini 2 and Martini 3. This allows Martini simulations, starting with GROMACS topology files that are processed by custom scripts, with all the added flexibility of OpenMM. We provide a GitHub repository with test cases, compare accuracy and performance between GROMACS and OpenMM, and discuss the limitations of our implementation in terms of direct comparison with GROMACS. We describe a use case that implements the Modeling Employing Limited Data method to apply experimental constraints in a Martini simulation to efficiently determine the structure of a protein complex. We also discuss issues and a potential solution with the Martini 2 topology for cholesterol.

The annexins are a family of Ca2+-dependent peripheral membrane proteins. Several annexins are implicated in plasma membrane repair and are overexpressed in cancer cells. Annexin A4 (ANXA4) and annexin A5 (ANXA5) form trimers that induce high curvature on a membrane surface, a phenomenon deemed to accelerate membrane repair. Despite being highly homologous to ANXA4, annexin A3 (ANXA3) does not form trimers on the membrane surface. Using molecular dynamics simulations, we have reverse engineered an ANXA3-mutant to trimerize on the surface of the membrane and induce high curvature reminiscent of ANXA4. In addition, atomic force microscopy images show that, like ANXA4, the engineered protein forms crystalline arrays on a supported lipid membrane. Despite the trimer-forming and curvature-inducing properties of the engineered ANXA3, it does not accumulate near a membrane lesion in laser-punctured cells and is unable to repair the lesion. Our investigation provides insights into the factors that drive annexin-mediated membrane repair and shows that the membrane-repairing property of trimer-forming annexins also necessitates high membrane binding affinity, other than trimer formation and induction of negative membrane curvature.

Sequencing of the protein coding genome has revealed many different missense mutations of human proteins and different population frequencies of corresponding haplotypes, which consist of different sets of those mutations. Here, we present evidence for pairwise intramolecular epistasis (i.e., nonadditive interactions) between many such mutations through an analysis of protein dynamics. We suggest that functional compensation for conserving protein dynamics is a likely evolutionary mechanism that maintains high-frequency mutations that are individually nonneutral but epistatically compensating within proteins. This analysis is the first of its type to look at human proteins with specific high population frequency mutations and examine the relationship between mutations that make up that observed high-frequency protein haplotype. Importantly, protein dynamics revealed a separation between high and low frequency haplotypes within a target protein cytochrome P450 2A7, with the high-frequency haplotypes showing behavior closer to the wild-type protein. Common protein haplotypes containing two mutations display dynamic compensation in which one mutation can correct for the dynamic effects of the other. We also utilize a dynamics-based metric, EpiScore, that evaluates the epistatic interactions and allows us to see dynamic compensation within many other proteins.

Accurate modeling of protein-water interactions in molecular dynamics (MD) simulations is important for understanding the molecular basis of protein function. Data from x-ray crystallography can be useful in assessing the accuracy of MD simulations, in particular, the locations of crystallographic water sites (CWS) coordinated by the protein. Such a comparison requires special methodological considerations that take into account the dynamic nature of proteins. However, existing methods for analyzing CWS in MD simulations rely on global alignment of the protein onto the crystal structure, which introduces substantial errors in the case of significant structural deviations. Here, we propose a method called local alignment for water sites (LAWS), which is based on multilateration-an algorithm widely used in GPS tracking. LAWS considers the contacts formed by CWS and protein atoms in the crystal structure and uses these interaction distances to track CWS in a simulation. We apply our method to simulations of a protein crystal and to simulations of the same protein in solution. Compared with existing methods, LAWS defines CWS characterized by more prominent water density peaks and a less-perturbed protein environment. In the crystal, we find that all high-confidence crystallographic waters are preserved. Using LAWS, we demonstrate the importance of crystal packing for the stability of CWS in the unit cell. Simulations of the protein in solution and in the crystal share a common set of preserved CWS that are located in pockets and coordinated by residues of the same domain, which suggests that the LAWS algorithm will also be useful in studying ordered waters and water networks in general.

The actin filament network is in part remodeled by the action of a family of filament severing proteins that are responsible for modulating the ratio between monomeric and filamentous actin. Recent work on the protein actophorin from the amoeba Acanthamoeba castellani identified a series of site-directed mutations that increase the thermal stability of the protein by 22°C. Here, we expand this observation by showing that the mutant protein is also significantly stable to both equilibrium and kinetic chemical denaturation, and employ computer simulations to account for the increase in thermal or chemical stability through an accounting of atomic-level interactions. Specifically, the potential of mean force (PMF) can be obtained from steered molecular dynamics (SMD) simulations in which a protein is unfolded. However, SMD can be inefficient for large proteins as they require large solvent boxes, and computationally expensive as they require increasingly many SMD trajectories to converge the PMF. Adaptive steered molecular dynamics (ASMD) overcomes the second of these limitations by steering the particle in stages, which allows for convergence of the PMF using fewer trajectories compared with SMD. Use of the telescoping water scheme within ASMD partially overcomes the first of these limitations by reducing the number of waters at each stage to only those needed to solvate the structure within a given stage. In the PMFs obtained from ASMD, the work of unfolding Acto-2 was found to be higher than the Acto-WT by approximately 120 kCal/mol and reflects the increased stability seen in the chemical denaturation experiments. The evolution of the average number of hydrogen bonds and number of salt bridges during the pulling process provides a mechanistic view of the structural changes of the actophorin protein as it is unfolded, and how it is affected by the mutation in concert with the energetics reported through the PMF.

FOF1 ATP synthase, a ubiquitous enzyme that synthesizes most ATP in living cells, is composed of two rotary motors: a membrane-embedded proton-driven FO motor and a catalytic F1 motor. These motors share both central and peripheral stalks. Although both FO and F1 have pseudo-symmetric structures, their symmetries do not match. How symmetry mismatch is solved remains elusive because of the missing intermediate structures of the rotational steps. Here, for the case of Bacillus PS3 ATP synthases with three- and 10-fold symmetries in F1 and FO, respectively, we uncovered the mechanical couplings between FO and F1 at every 36° rotation step via molecular dynamics simulations and comparative studies of cryoelectron microscopy (cryo-EM) structures from three species. We found that the mismatch could be solved using several elements: 1) the F1 head partially rotates relative to the FO a subunit via elastic distortion of the b subunits, 2) the rotor is twisted, and 3) comparisons of cryo-EM structures further suggest that the c ring rotary angles can deviate from the symmetric ones. In addition, the F1 motor may have non-canonical structures, relieving stronger frustration. Thus, we provide new insights for solving the symmetry mismatch problem.

Assessing kinetics in biological processes with molecular dynamics simulations remains a computational and conceptual challenge, given the large time and length scales involved. For kinetic transport of biochemical compounds or drug molecules, the permeability through the phospholipid membranes is a key kinetic property, but long timescales are hindering the accurate computation. Technological advances in high-performance computing therefore need to be accompanied by theoretical and methodological developments. In this contribution, the replica exchange transition interface sampling (RETIS) methodology is shown to give perspective toward observing longer permeation pathways. It is first reviewed how RETIS, a path-sampling methodology that gives in principle exact kinetics, can be used to compute membrane permeability. Next, recent and current developments in three RETIS aspects are discussed: several new Monte Carlo moves in the path-sampling algorithm, memory reduction by reducing pathlengths, and exploitation of parallel computing with CPU-imbalanced replicas. Finally, the memory reduction presenting a new replica exchange implementation, coined REPPTIS, is showcased with a permeant needing to pass a membrane with two permeation channels, either representing an entropic or energetic barrier. The REPPTIS results showed clearly that inclusion of some memory and enhancing ergodic sampling via replica exchange moves are both necessary to obtain correct permeability estimates. In an additional example, ibuprofen permeation through a dipalmitoylphosphatidylcholine membrane was modeled. REPPTIS succeeded in estimating the permeability of this amphiphilic drug molecule with metastable states along the permeation pathway. In conclusion, the presented methodological advances allow for deeper insight into membrane biophysics even if the pathways are slow, as RETIS and REPPTIS push the permeability calculations to longer timescales.

Autotransporters are a large family of virulence factors found in Gram-negative bacteria that play important roles in their pathogenesis. The passenger domain of autotransporters is almost always composed of a large β-helix, with only a small portion of it being relevant to its virulence function. This has led to the hypothesis that the folding of the β-helical structure aids the secretion of the passenger domain across the Gram-negative outer membrane. In this study, we used molecular dynamics simulations and enhanced sampling methods to investigate the stability and folding of the passenger domain of pertactin, an autotransporter from Bordetella pertussis. Specifically, we employed steered molecular dynamics to simulate the unfolding of the entire passenger domain as well as self-learning adaptive umbrella sampling to compare the energetics of folding rungs of the β-helix independently ("isolated folding") versus folding rungs on top of a previously folded rung ("vectorial folding"). Our results showed that vectorial folding is highly favorable compared with isolated folding; moreover, our simulations showed that the C-terminal rung of the β-helix is the most resistant to unfolding, in agreement with previous studies that found the C-terminal half of the passenger domain to be more stable than the N-terminal one. Overall, this study provides new insights into the folding process of an autotransporter passenger domain and its potential role in secretion across the outer membrane.

The nonstructural protein-1 (NSP1) of the severe acute respiratory syndrome-associated coronavirus 2 plays a crucial role in the translational shutdown and immune evasion inside host cells. Despite its known intrinsic disorder, the C-terminal domain (CTD) of NSP1 has been reported to form a double α-helical structure and block the 40S-ribosomal channel for mRNA translation. Experimental studies indicate that NSP1 CTD functions independently from the globular N-terminal region separated with a long linker domain, underscoring the necessity of exploring the standalone conformational ensemble. In this contribution, we utilize exascale computing resources to yield unbiased molecular dynamics simulation of NSP1 CTD in all-atom resolution starting from multiple initial seed structures. A data-driven approach elicits collective variables (CVs) that are significantly superior to conventional descriptors in capturing the conformational heterogeneity. The free energy landscape as a function of the CV space is estimated using the modified expectation maximized molecular dynamics. Originally developed by us for small peptides, here, we establish the efficacy of expectation maximized molecular dynamics in conjunction with data-driven CV space for a more complex and relevant biomolecular system. The results reveal the existence of two disordered metastable populations in the free energy landscape that are separated from the conformation resembling ribosomal subunit bound state by high kinetic barriers. Chemical shift correlation and secondary structure analysis capture significant differences among key structures of the ensemble. Altogether, these insights can underpin drug development studies and mutational experiments that help induce population shifts to alter translational blocking and understand its molecular basis in further detail.

Biomolecular condensates, thought to form via liquid-liquid phase separation of intracellular mixtures, are multicomponent systems that can include diverse types of proteins and RNAs. RNA is a critical modulator of RNA-protein condensate stability, as it induces an RNA concentration-dependent reentrant phase transition-increasing stability at low RNA concentrations and decreasing it at high concentrations. Beyond concentration, RNAs inside condensates can be heterogeneous in length, sequence, and structure. Here, we use multiscale simulations to understand how different RNA parameters interact with one another to modulate the properties of RNA-protein condensates. To do so, we perform residue/nucleotide resolution coarse-grained molecular dynamics simulations of multicomponent RNA-protein condensates containing RNAs of different lengths and concentrations, and either FUS or PR25 proteins. Our simulations reveal that RNA length regulates the reentrant phase behavior of RNA-protein condensates: increasing RNA length sensitively rises the maximum value that the critical temperature of the mixture reaches, and the maximum concentration of RNA that the condensate can incorporate before beginning to become unstable. Strikingly, RNAs of different lengths are organized heterogeneously inside condensates, which allows them to enhance condensate stability via two distinct mechanisms: shorter RNA chains accumulate at the condensate's surface acting as natural biomolecular surfactants, while longer RNA chains concentrate inside the core to saturate their bonds and enhance the density of molecular connections in the condensate. Using a patchy particle model, we additionally demonstrate that the combined impact of RNA length and concentration on condensate properties is dictated by the valency, binding affinity, and polymer length of the various biomolecules involved. Our results postulate that diversity on RNA parameters within condensates allows RNAs to increase condensate stability by fulfilling two different criteria: maximizing enthalpic gain and minimizing interfacial free energy; hence, RNA diversity should be considered when assessing the impact of RNA on biomolecular condensates regulation.

Species belonging to the Bacillus cereus group form endospores (spores) whose surface is decorated with micrometers-long and nanometers-wide endospore appendages (Enas). The Enas have recently been shown to represent a completely novel class of Gram-positive pili. They exhibit remarkable structural properties making them extremely resilient to proteolytic digestion and solubilization. However, little is known about their functional and biophysical properties. In this work, we apply optical tweezers to manipulate and assess how wild-type and Ena-depleted mutant spores immobilize on a glass surface. Furthermore, we utilize optical tweezers to extend S-Ena fibers to measure their flexibility and tensile stiffness. Finally, by oscillating single spores, we examine how the exosporium and Enas affect spores' hydrodynamic properties. Our results show that S-Enas (μm-long pili) are not as effective as L-Enas in immobilizing spores to glass surfaces but are involved in forming spore-to-spore connections, holding the spores together in a gel-like state. The measurements also show that S-Enas are flexible but tensile stiff fibers, which support structural data suggesting that the quaternary structure is composed of subunits arranged in a complex to produce a bendable fiber (helical turns can tilt against each other) with limited axial fiber extensibility. Finally, the results show that the hydrodynamic drag is 1.5 times higher for wild-type spores expressing S- and L-Enas compared with mutant spores expressing only L-Enas or "bald spores" lacking Ena, and 2 times higher compared with spores of the exosporium-deficient strain. This study unveils novel findings on the biophysics of S- and L-Enas, their role in spore aggregation, binding of spores to glass, and their mechanical behavior upon exposure to drag forces.

The resolution revolution has increasingly enabled single-particle cryogenic electron microscopy (cryo-EM) reconstructions of previously inaccessible systems, including membrane proteins-a category that constitutes a disproportionate share of drug targets. We present a protocol for using density-guided molecular dynamics simulations to automatically refine atomistic models into membrane protein cryo-EM maps. Using adaptive force density-guided simulations as implemented in the GROMACS molecular dynamics package, we show how automated model refinement of a membrane protein is achieved without the need to manually tune the fitting force ad hoc. We also present selection criteria to choose the best-fit model that balances stereochemistry and goodness of fit. The proposed protocol was used to refine models into a new cryo-EM density of the membrane protein maltoporin, either in a lipid bilayer or detergent micelle, and we found that results do not substantially differ from fitting in solution. Fitted structures satisfied classical model-quality metrics and improved the quality and the model-to-map correlation of the x-ray starting structure. Additionally, the density-guided fitting in combination with generalized orientation-dependent all-atom potential was used to correct the pixel-size estimation of the experimental cryo-EM density map. This work demonstrates the applicability of a straightforward automated approach to fitting membrane protein cryo-EM densities. Such computational approaches promise to facilitate rapid refinement of proteins under different conditions or with various ligands present, including targets in the highly relevant superfamily of membrane proteins.

The drug voxelotor (commercially known as Oxbryta) has been approved by the US Food and Drug Administration for the treatment of sickle cell disease. It is known to reduce disease-causing sickling by inhibiting the transformation of the non-polymerizing, high-oxygen-affinity R quaternary structure of sickle hemoglobin into its polymerizing, low-affinity T quaternary structure. It has not been established whether the binding of the drug has anti-sickling effects beyond restricting the change of quaternary structure. By using a laser photolysis method that employs microscope optics, we have determined that fully deoxygenated sickle hemoglobin will assume the T structure. We show that the nucleation rates essential to generate the sickle fibers are not significantly affected by voxelotor. The method employed here should be useful for determining the mechanism of sickling inhibition for proposed drugs.

Theory and simulations predict the complex nature of calcium interaction with the lipid membrane. By maintaining the calcium concentrations at physiological conditions, herein we demonstrate experimentally the effect of Ca2+ in a minimalistic cell-like model. For this purpose, giant unilamellar vesicles (GUVs) with a neutral lipid DOPC are generated, and the ion-lipid interaction is observed with attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy providing molecular resolution. Firstly, Ca2+ encapsulated within the vesicle binds to the phosphate head groups of the inner leaflets and triggers vesicle compaction. This is tracked by changes in vibrational modes of the lipid groups. As the calcium concentration within the GUV increases, IR intensities change indicating vesicle dehydration and lateral compression of the membrane. Secondly, by inducing a calcium gradient across the membrane up to a ratio of 1:20, interaction between several vesicles occurs as Ca2+ can bind to the outer leaflets leading to vesicle clustering. It is observed that larger calcium gradients induce stronger interactions. These findings with an exemplary biomimetic model reveal that divalent calcium ions not only cause local changes to the lipid packing but also have macroscopic implications to initiate vesicle-vesicle interaction.

Aggregation of the RNA-binding protein fused in sarcoma (FUS) is a hallmark of neurodegenerative diseases. Phosphorylation of Ser/Thr in the FUS low-complexity domain (FUS-LC) may regulate phase separation of FUS and prevent pathological aggregation in cells. However, many details of this process remain elusive to date. In this work, we systematically investigated the phosphorylation of FUS-LC and the underlying molecular mechanism by molecular dynamics (MD) simulations and free energy calculations. The results clearly show that phosphorylation can destroy the fibril core structure of FUS-LC by breaking interchain interactions, particularly contacts involving residues like Tyr, Ser, and Gln. Among the six phosphorylation sites, Ser61 and Ser84 may have more important effects on the stability of the fibril core. Our study reveals structural and dynamic details of FUS-LC phase separation modulated by phosphorylation.

The bacterial fimbrial adhesin FimH is a remarkable and well-studied catch-bond protein found at the tip of E. coli type 1 pili, which allows pathogenic strains involved in urinary tract infections to bind high-mannose glycans exposed on human epithelia. The catch-bond behavior of FimH, where the strength of the interaction increases when a force is applied to separate the two partners, enables the bacteria to resist clearance when they are subjected to shear forces induced by urine flow. Two decades of experimental studies performed at the single-molecule level, as well as x-ray crystallography and modeling studies, have led to a consensus picture whereby force separates the binding domain from an inhibitor domain, effectively triggering an allosteric conformational change in the former. This force-induced allostery is thought to be responsible for an increased binding affinity at the core of the catch-bond mechanism. However, some important questions remain, the most challenging one being that the crystal structures corresponding to these two allosteric states show almost superimposable binding site geometries, which questions the molecular origin for the large difference in affinity. Using molecular dynamics with a combination of enhanced-sampling techniques, we demonstrate that the static picture provided by the crystal structures conceals a variety of binding site conformations that have a key impact on the apparent affinity. Crucially, the respective populations in each of these conformations are very different between the two allosteric states of the binding domain, which can then be related to experimental affinity measurements. We also evidence a previously unappreciated but important effect: in addition to the well-established role of the force as an allosteric regulator via domain separation, application of force tends to directly favor the high-affinity binding site conformations. We hypothesize that this additional "local" catch-bond effect could delay unbinding between the bacteria and the host cell before the "global" allosteric transition occurs, as well as stabilizing the complex even more once in the high-affinity allosteric state.

Association of the cellular adhesive protein CD44 and the N-terminal (FERM) domain of cytoskeleton adaptors is critical for cell proliferation, migration, and signaling. Phosphorylation of the cytoplasmic domain (CTD) of CD44 acts as an important regulator of the protein association, but the structural transformation and dynamics mechanism remain enigmatic. In this study, extensive coarse-grained simulations were employed to explore the molecular details in the formation of CD44-FERM complex under S291 and S325 phosphorylation, a modification path known to exert reciprocal effects on the protein association. We find that phosphorylation of S291 inhibits complexation by causing the CTD of CD44 to adopt a more closed structure. In contrast, S325 phosphorylation liberates the CD44-CTD from the membrane surface and promotes the linkage with FERM. The phosphorylation-driven transformation is found to occur in a PIP2-dependent manner, with PIP2 effecting the relative stability of the closed and open conformation, and a replacement of PIP2 by POPS greatly abrogates this effect. The revealed interdependent regulation mechanism by phosphorylation and PIP2 in the association of CD44 and FERM further strengthens our understanding of the molecular basis of cellular signaling and migration.

The mechanical interaction between cells and the extracellular matrix (ECM) is fundamental to coordinate collective cell behavior in tissues. Relating individual cell-level mechanics to tissue-scale collective behavior is a challenge that cell-based models such as the cellular Potts model (CPM) are well-positioned to address. These models generally represent the ECM with mean-field approaches, which assume substrate homogeneity. This assumption breaks down with fibrous ECM, which has nontrivial structure and mechanics. Here, we extend the CPM with a bead-spring model of ECM fiber networks modeled using molecular dynamics. We model a contractile cell pulling with discrete focal adhesion-like sites on the fiber network and demonstrate agreement with experimental spatiotemporal fiber densification and displacement. We show that at high network cross-linking, contractile cell forces propagate over at least eight cell diameters, decaying with distance with power law exponent n= 0.35 - 0.65 typical of viscoelastic ECMs. Further, we use in silico atomic force microscopy to measure local cell-induced network stiffening consistent with experiments. Our model lays the foundation for investigating how local and long-ranged cell-ECM mechanobiology contributes to multicellular morphogenesis.

In vivo, cells navigate through complex environments filled with obstacles such as other cells and the extracellular matrix. Recently, the term "topotaxis" has been introduced for navigation along topographic cues such as obstacle density gradients. Experimental and mathematical efforts have analyzed topotaxis of single cells in pillared grids with pillar density gradients. A previous model based on active Brownian particles (ABPs) has shown that ABPs perform topotaxis, i.e., drift toward lower pillar densities, due to decreased effective persistence lengths at high pillar densities. The ABP model predicted topotactic drifts of up to 1% of the instantaneous speed, whereas drifts of up to 5% have been observed experimentally. We hypothesized that the discrepancy between the ABP and the experimental observations could be in 1) cell deformability and 2) more complex cell-pillar interactions. Here, we introduce a more detailed model of topotaxis based on the cellular Potts model (CPM). To model persistent cells we use the Act model, which mimics actin-polymerization-driven motility, and a hybrid CPM-ABP model. Model parameters were fitted to simulate the experimentally found motion of Dictyostelium discoideum on a flat surface. For starved D. discoideum, the topotactic drifts predicted by both CPM variants are closer to the experimental results than the previous ABP model due to a larger decrease in persistence length. Furthermore, the Act model outperformed the hybrid model in terms of topotactic efficiency, as it shows a larger reduction in effective persistence time in dense pillar grids. Also pillar adhesion can slow down cells and decrease topotaxis. For slow and less-persistent vegetative D. discoideum cells, both CPMs predicted a similar small topotactic drift. We conclude that deformable cell volume results in higher topotactic drift compared with ABPs, and that feedback of cell-pillar collisions on cell persistence increases drift only in highly persistent cells.

In the late stages of the HIV-1 life cycle, membrane localization and self-assembly of Gag polyproteins induce membrane deformation and budding. Release of the virion requires direct interaction between immature Gag lattice and upstream ESCRT machinery at the viral budding site, followed by assembly of downstream ESCRT-III factors, culminating in membrane scission. However, molecular details of upstream ESCRT assembly dynamics at the viral budding site remain unclear. In this work, using coarse-grained (CG) molecular dynamics (MD) simulations, we investigated the interactions between Gag, ESCRT-I, ESCRT-II, and membrane to delineate the dynamical mechanisms by which upstream ESCRTs assemble templated by late-stage immature Gag lattice. We first systematically derived "bottom-up" CG molecular models and interactions of upstream ESCRT proteins from experimental structural data and extensive all-atom MD simulations. Using these molecular models, we performed CG MD simulations of ESCRT-I oligomerization and ESCRT-I/II supercomplex formation at the neck of the budding virion. Our simulations demonstrate that ESCRT-I can effectively oligomerize to higher-order complexes templated by the immature Gag lattice both in the absence of ESCRT-II and when multiple copies of ESCRT-II are localized at the bud neck. The ESCRT-I/II supercomplexes formed in our simulations exhibit predominantly columnar structures, which has important implications for the nucleation pathway of downstream ESCRT-III polymers. Importantly, ESCRT-I/II supercomplexes bound to Gag initiate membrane neck constriction by pulling the inner edge of the bud neck closer to the ESCRT-I headpiece ring. Our findings serve to elucidate a network of interactions between upstream ESCRT machinery, immature Gag lattice, and membrane neck that regulate protein assembly dynamics at the HIV-1 budding site.

Inositol 1,4,5-trisphosphate (IP3)-induced Ca2+ signaling is a second messenger system used by almost all eukaryotic cells. Recent research demonstrated randomness of Ca2+ signaling on all structural levels. We compile eight general properties of Ca2+ spiking common to all cell types investigated and suggest a theory of Ca2+ spiking starting from the random behavior of IP3 receptor channel clusters mediating the release of Ca2+ from the endoplasmic reticulum capturing all general properties and pathway-specific behavior. Spike generation begins after the absolute refractory period of the previous spike. According to its hierarchical spreading from initiating channel openings to cell level, we describe it as a first passage process from none to all clusters open while the cell recovers from the inhibition which terminated the previous spike. Our theory reproduces the exponential stimulation response relation of the average interspike interval Tav and its robustness properties, random spike timing with a linear moment relation between Tav and the interspike interval SD and its robustness properties, sensitive dependency of Tav on diffusion properties, and nonoscillatory local dynamics. We explain large cell variability of Tav observed in experiments by variability of channel cluster coupling by Ca2+-induced Ca2+ release, the number of clusters, and IP3 pathway component expression levels. We predict the relation between puff probability and agonist concentration and [IP3] and agonist concentration. Differences of spike behavior between cell types and stimulating agonists are explained by the different types of negative feedback terminating spikes. In summary, the hierarchical random character of spike generation explains all of the identified general properties.

Salt bridges are important factors in maintaining the stability of proteins, and their contribution to protein folding has received much attention. Although the interaction energies, or stabilizing contributions, of individual salt bridges have been measured in various proteins, a systematic assessment of various types of salt bridges in a relatively uniform environment is still a valuable analysis. Here, we used a collagen heterotrimer as a host-guest platform to construct 48 heterotrimers with the same charge pattern. A variety of salt bridges were formed between the oppositely charged residues Lys, Arg, Asp, and Glu. The melting temperature (Tm) of the heterotrimers was measured with circular dichroism. The atomic structures of 10 salt bridges were shown in three x-ray crystals of heterotrimer. Molecular dynamics simulation based on the crystal structures indicated that strong, intermediate, and weak salt bridges have distinctive N-O distances. A linear regression model was used to predict the stability of heterotrimers with high accuracy (R2 = 0.93). We developed an online database to help readers understand how a salt bridge stabilizes collagen. This work will help us better understand the stabilizing mechanism of salt bridges in collagen folding and provide a new strategy to design collagen heterotrimers.

Plasma membrane (PM) heterogeneity has long been implicated in various cellular functions. However, mechanistic principles governing functional regulations of lipid environment are not well understood due to the inherent complexities associated with the relevant length and timescales that limit both direct experimental measurements and their interpretation. In this context, computer simulations hold immense potential to investigate molecular-level interactions and mechanisms that lead to PM heterogeneity and its functions. Herein, we investigate spatial and dynamic heterogeneity in model membranes with coexisting liquid ordered and liquid disordered phases and characterize the membrane order in terms of the local topological changes in lipid environment using the nonaffine deformation framework. Furthermore, we probe the packing defects in these membranes, which can be considered as the conjugate of membrane order assessed in terms of the nonaffine parameter. In doing so, we formalize the connection between membrane packing and local membrane order and use that to explore the mechanistic principles behind their functions. Our observations suggest that heterogeneity in mixed phase membranes is a consequence of local lipid topology and its temporal evolution, which give rise to disparate lipid packing in ordered and disordered domains. This in turn governs the distinct nature of packing defects in these domains that can play a crucial role in preferential localization of proteins in mixed phase membranes. Furthermore, we observe that lipid packing also leads to contrasting distribution of free volume in the membrane core region in ordered and disordered membranes, which can lead to distinctive membrane permeability of small molecules. Our results, thus, indicate that heterogeneity in mixed phase membranes closely governs the membrane functions that may emerge from packing-related basic design principles.

The zipper model has been dominantly used to describe the driving mechanism of the engulfment process and its specific identification of antigens during phagocytosis in macrophages. However, the abilities and limitations of the zipper model, capturing the process as an irreversible reaction, have not been examined yet under the critical conditions of engulfment capacity. Here, we demonstrated the phagocytic behavior of macrophages after reaching the maximum engulfment capacity by tracking the progression of their membrane extension during engulfment using IgG-coated nondigestible polystyrene beads and glass microneedles. The results showed that, after macrophages reached their maximum engulfment capacity, they induced membrane backtracking (the reverse phenomenon of engulfment) in both polystyrene beads and glass microneedles, regardless of the difference in the shape of these antigens. We evaluated the correlation of engulfment in simultaneous stimulations of two IgG-coated microneedles and found that each microneedle was regurgitated by the macrophage independently of the advancement or backtracking of membranes on the other microneedle. Moreover, assessing the total engulfment capacity determined by the maximum amount the macrophage was capable of engulfing when imposing each antigen geometry showed that the capacity increased as the attached antigen areas increased. These results indicate that the mechanism of engulfment should imply the following: 1) macrophages have a backtracking function to recover their phagocytic activity after reaching maximal engulfment limit, 2) both phagocytosis and backtracking are local phenomena of the macrophage membrane that operates independently, and 3) the maximum engulfment capacity is determined not only by mere local cell membrane capacity but also by the whole-cell volume increase during simultaneous phagocytosis of multiple antigens by the single macrophages. Thus, the phagocytic activity may entail a hidden backtracking function, adding to the conventionally known irreversible zipper-like ligand-receptor binding mechanism during membrane progression to recover the macrophages that are saturated from engulfing targets beyond their capacity.

Gene expression is inherently noisy due to small numbers of proteins and nucleic acids inside a cell. Likewise, cell division is stochastic, particularly when tracking at the level of a single cell. The two can be coupled when gene expression affects the rate of cell division. Single-cell time-lapse experiments can measure both fluctuations by simultaneously recording protein levels inside a cell and its stochastic division. These information-rich noisy trajectory data sets can be harnessed to learn about the underlying molecular and cellular details that are often not known a priori. A critical question is: How can we infer a model given data where fluctuations at two levels-gene expression and cell division-are intricately convoluted? We show the principle of maximum caliber (MaxCal)-integrated within a Bayesian framework-can be used to infer several cellular and molecular details (division rates, protein production, and degradation rates) from these coupled stochastic trajectories (CSTs). We demonstrate this proof of concept using synthetic data generated from a known model. An additional challenge in data analysis is that trajectories are often not in protein numbers, but in noisy fluorescence that depends on protein number in a probabilistic manner. We again show that MaxCal can infer important molecular and cellular rates even when data are in fluorescence, another example of CST with three confounding factors-gene expression noise, cell division noise, and fluorescence distortion-all coupled. Our approach will provide guidance to build models in synthetic biology experiments as well as general biological systems where examples of CSTs are abundant.