From residue coevolution to protein conformational ensembles and functional dynamics

The analysis of evolutionary amino acid correlations has recently attracted a surge of renewed interest, also due to their successful use in de novo protein native structure prediction. However, many aspects of protein function, such as substrate binding and product release in enzymatic activity, can be fully understood only in terms of an equilibrium ensemble of alternative structures, rather than a single static structure. In this paper we combine coevolutionary data and molecular dynamics simulations to study protein conformational heterogeneity. To that end, we adapt the Boltzmann-learning algorithm to the analysis of homologous protein sequences and develop a coarse-grained protein model specifically tailored to convert the resulting contact predictions to a protein structural ensemble. By means of exhaustive sampling simulations, we analyze the set of conformations that are consistent with the observed residue correlations for a set of representative protein domains, showing that (i) the most representative structure is consistent with the experimental fold and (ii) the various regions of the sequence display different stability, related to multiple biologically relevant conformations and to the cooperativity of the coevolving pairs. Moreover, we show that the proposed protocol is able to reproduce the essential features of a protein folding mechanism as well as to account for regions involved in conformational transitions through the correct sampling of the involved conformers.Pairs of positions along a protein sequence can show strong correlations arising both from functional and structural constraints (1–9). Earliest approaches for detecting interdependent residues and predicting 3D contacts in proteins (1–4, 8) analyzed alignments containing from tens to a few hundreds sequences. Given the small size of available sequences datasets, these works relied on an independent pair approximation: a “coevolutionary coupling” between two residues was estimated independently for each pair, ignoring the rest of the network of residues. The number of known protein sequences, however, has grown dramatically in the past few years (10). Such a large increase in the size of datasets has allowed to fit—either explicitly (11, 12) or implicitly (13, 14)—pairwise models for protein sequences that take into account the whole network of correlated residues simultaneously, and are able to disentangle correlated positions from “interacting” positions by identifying the parameters of the model with the coupling constants in an Ising-like Hamiltonian (15, 16). Despite their simplicity, these models have had remarkable success in the design of synthetic sequences preserving natural function (13, 14) and in the prediction of interacting pairs of residues from the knowledge of their sequence alone (12, 17–21).In this paper, we tackle the problem of sampling an ensemble of structures compatible with the observed coevolution between protein residues. We will follow a two-step procedure. The first step corresponds to an inverse problem: from a set of homologous sequences to the parameters of a model. Inverse problems are notoriously computationally hard. For large sets of variables, an exact evaluation of the normalizing constant of the variables’ joint distribution (the partition function, in the language of statistical mechanics) is impracticable. Previous works in the literature focused on efficiency, circumventing this problem by adopting different, approximated solutions (12, 17–19, 22–26), generically based on tractable approximations of the likelihood function. However, given the success and the number of potential applications of coevolutionary analysis, the study of reference and more quantitative approaches is necessary. In this regard, the Monte Carlo Markov chain (MCMC)-based, maximum-likelihood approach, albeit computationally demanding, is in principle exact given a sufficient sampling at each minimization step. In this work we adopt the Boltzmann learning algorithm (11, 27), whose accuracy in inferring the parameters of the pairwise model, at variance with all of the previous approaches in the literature, is not biased a priori by the choice of a particular approximation scheme.The second step is a direct problem: after translating the probabilistic model for sequences into an energy potential for protein structures, we can explore the resulting energy landscape using molecular dynamics (MD). After extensive sampling, we can characterize the folding reaction and find the best candidate for the native fold as well as metastable intermediates and conformers that may have a functional role. Moreover, we can spot flexible regions, directly connecting coevolution to function and dynamics. With this goal in mind, we introduce a coarse-grained model particularly apt to translate predictions of contacts to a structural ensemble. Thanks to the great reduction in the number of degrees of freedom, coarse-grained models have been widely used to study many aspects of proteins (28–34). Due to their simplicity, C_α models in particular have already been used to predict protein folds from coevolutionary data (35–37). Here, in the same spirit as the model presented in ref. 35, where coevolutionary information is used with a C_α coarse-grained protein model, we present a higher-resolution coarse-grained model that combines the pairwise predictions with an adapted all-atom force field for the heavy backbone atoms, similar to the approach used in ref. 38. The predicted contacts are introduced as favorable interactions between C_β atoms of a coarse-grained side-chain, whereas the protein backbone is modeled with all of the heavy atoms to capture the secondary structure conformation with high resolution. Indeed, we show through extensive molecular dynamics simulations on a set of 18 proteins that the final accuracy of structure prediction, measured as root-mean-square deviation (RMSD) from the native experimental structure, is determined solely by the accuracy of contact predictions.However, besides recovering a protein native fold, the main advantage of the proposed approach is its ability to capture the conformational heterogeneity and the thermodynamical features of the folding reaction as implied by coevolutionary information only. In contrast to more expensive approaches like all-atom MD or more refined coarse-grained potentials (39), we can afford an extensive equilibrium sampling of the conformational space. We illustrate this point by applying our approach to analyze two energy landscapes related to the folding of the Ras protein and the conformational dynamics of a tyrosine kinase. As expected, Ras folds cooperatively, and we find and characterize a folding intermediate. The protein kinase correctly samples an ensemble of active-like and inactive-like structures that are biologically relevant for its function and shows a flexibility pattern compatible with experimental observations.     

Single-molecule spectroscopy reveals how calmodulin activates NO synthase by controlling its conformational fluctuation dynamics

Mechanisms that regulate the nitric oxide synthase enzymes (NOS) are of interest in biology and medicine. Although NOS catalysis relies on domain motions, and is activated by calmodulin binding, the relationships are unclear. We used single-molecule fluorescence resonance energy transfer (FRET) spectroscopy to elucidate the conformational states distribution and associated conformational fluctuation dynamics of the two electron transfer domains in a FRET dye-labeled neuronal NOS reductase domain, and to understand how calmodulin affects the dynamics to regulate catalysis. We found that calmodulin alters NOS conformational behaviors in several ways: It changes the distance distribution between the NOS domains, shortens the lifetimes of the individual conformational states, and instills conformational discipline by greatly narrowing the distributions of the conformational states and fluctuation rates. This information was specifically obtainable only by single-molecule spectroscopic measurements, and reveals how calmodulin promotes catalysis by shaping the physical and temporal conformational behaviors of NOS.Although proteins adopt structures determined by their amino acid sequences, they are not static objects and fluctuate among ensembles of conformations (1). Transitions between these states can occur on a variety of length scales (Å to nm) and time scales (ps to s) and have been linked to functionally relevant phenomena such as allosteric signaling, enzyme catalysis, and protein–protein interactions (2–4). Indeed, protein conformational fluctuations and dynamics, often associated with static and dynamic inhomogeneity, are thought to play a crucial role in biomolecular functions (5–11). It is difficult to characterize such spatially and temporally inhomogeneous dynamics in bulk solution by an ensemble-averaged measurement, especially in proteins that undergo multiple-conformation transformations. In such cases, single-molecule spectroscopy is a powerful approach to analyze protein conformational states and dynamics under physiological conditions, and can provide a molecular-level perspective on how a protein’s structural dynamics link to its functional mechanisms (12–21).A case in point is the nitric oxide synthase (NOS) enzymes (22–24), whose nitric oxide (NO) biosynthesis involves electron transfer reactions that are associated with relatively large-scale movement (tens of Å) of the enzyme domains (Fig. 1A). During catalysis, NADPH-derived electrons first transfer into an FAD domain and an FMN domain in NOS that together comprise the NOS reductase domain (NOSr), and then transfer from the FMN domain to a heme group that is bound in a separate attached “oxygenase” domain, which then enables NO synthesis to begin (22, 25–27). The electron transfers into and out of the FMN domain are the key steps for catalysis, and they appear to rely on the FMN domain cycling between electron-accepting and electron-donating conformational states (28, 29) (Fig. 1B). In this model, the FMN domain is suggested to be highly dynamic and flexible due to a connecting hinge that allows it to alternate between its electron-accepting (FAD→FMN) or closed conformation and electron-donating (FMN→heme) or open conformation (Fig. 1 A and B) (28, 30–36). In the electron-accepting closed conformation, the FMN domain interacts with the NADPH/FAD domain (FNR domain) to receive electrons, whereas in the electron donating open conformation the FMN domain has moved away to expose the bound FMN cofactor so that it may transfer electrons to a protein acceptor like the NOS oxygenase domain, or to a generic protein acceptor like cytochrome c. In this way, the reductase domain structure cycles between closed and open conformations to deliver electrons, according to a conformational equilibrium that determines the movements and thus the electron flux capacity of the FMN domain (25, 28, 32, 34, 35, 37). A similar conformational switching mechanism is thought to enable electron transfer through the FMN domain in the related flavoproteins NADPH-cytochrome P450 reductase and methionine synthase reductase (38–42).Open in a separate windowFig. 1.(A) The nNOSr ribbon structure (from PDB: 1TLL) showing bound FAD (yellow) in FNR domain (green), FMN (orange) in FMN domain (yellow), connecting hinge (blue), and the Cy3–Cy5 label positions (pink) and distance (42 Å, dashed line). (B) Cartoon of an equilibrium between the FMN-closed and FMN-open states, with Cy dye label positions indicated. (C) Cytochrome c reductase activity of nNOSr proteins in their CaM-bound and CaM-free states. Color scheme of bar graphs: Black, WT nNOSr unlabeled; Red, Cys-lite (CL) nNOSr unlabeled; Blue, E827C/Q1268C CL nNOSr unlabeled; and Dark cyan, E827C/Q1268C CL nNOSr labeled.NOS enzymes also contain a calmodulin (CaM) binding domain that is located just before the N terminus of the FMN domain (Fig. 1B), and this provides an important layer of regulation (25, 27). CaM binding to NOS enzymes increases electron transfer from NADPH through the reductase domain and also triggers electron transfer from the FMN domain to the NOS heme as is required for NO synthesis (31, 32). The ability of CaM, or similar signaling proteins, to regulate electron transfer reactions in enzymes is unusual, and the mechanism is a topic of interest and intensive study. It has long been known that CaM binding alters NOSr structure such that, on average, it populates a more open conformation (43, 44). Recent equilibrium studies have detected a buildup of between two to four discreet conformational populations in NOS enzymes and in related flavoproteins, and in some cases, have also estimated the distances between the bound FAD and FMN cofactors in the different species (26, 36, 37, 39, 40), and furthermore, have confirmed that CaM shifts the NOS population distribution toward more open conformations (34, 36, 45). Although valuable, such ensemble-averaged results about conformational states cannot explain how electrons transfer through these enzymes, or how CaM increases the electron flux in NOS, because answering these questions requires a coordinate understanding of the dynamics of the conformational fluctuations. Indeed, computer modeling has indicated that a shift toward more open conformations as is induced by CaM binding to nNOS should, on its own, actually diminish electron flux through nNOS and through certain related flavoproteins (38). Despite its importance, measuring enzyme conformational fluctuation dynamics is highly challenging, and as far as we know, there have been no direct measures on the NOS enzymes or on related flavoproteins, nor studies on how CaM binding might influence the conformational fluctuation dynamics in NOS.To address this gap, we used single-molecule fluorescence energy resonance transfer (FRET) spectroscopy to characterize individual molecules of nNOSr that had been labeled at two specific positions with Cyanine 3 (Cy3) donor and Cyanine 5 (Cy5) acceptor dye molecules, regarding their conformational states distribution and the associated conformational fluctuation dynamics, which in turn enabled us to determine how CaM binding impacts both parameters. This work provides a unique perspective and a novel study of the NOS enzymes and within the broader flavoprotein family, which includes the mammalian enzymes methionine synthase reductase (MSR) and cytochrome P450 reductase (CPR), and reveals how CaM’s control of the conformational behaviors may regulate the electron transfer reactions of NOS catalysis.     

Chronophin coordinates cell leading edge dynamics by controlling active cofilin levels

Cofilin, a critical player of actin dynamics, is spatially and temporally regulated to control the direction and force of membrane extension required for cell locomotion. In carcinoma cells, although the signaling pathways regulating cofilin activity to control cell direction have been established, the molecular machinery required to generate the force of the protrusion remains unclear. We show that the cofilin phosphatase chronophin (CIN) spatiotemporally regulates cofilin activity at the cell edge to generate persistent membrane extension. We show that CIN translocates to the leading edge in a PI3-kinase–, Rac1-, and cofilin-dependent manner after EGF stimulation to activate cofilin, promotes actin free barbed end formation, accelerates actin turnover, and enhances membrane protrusion. In addition, we establish that CIN is crucial for the balance of protrusion/retraction events during cell migration. Thus, CIN coordinates the leading edge dynamics by controlling active cofilin levels to promote MTLn3 cell protrusion.Cofilin is one crucial mediator of actin cytoskeletal dynamics during cell motility (1–5). At the cell edge, cofilin severs F-actin filaments, generating substrates for Arp2/3-mediated branching activity and contributing to F-actin depolymerization by creating a new pointed end and F-actin assembly by increasing the pool of polymerization-competent actin monomers (G-actin) (6, 7). Because of its ability to sever actin filaments and thus, modulate actin dynamics, the precise spatial and temporal regulation of cofilin activity at the cell leading edge is crucial to cell protrusion, chemotaxis, and motility both in vitro and in vivo (2, 8–13). Misregulation of cofilin activity and/or expression is directly related to diseases, including tumor metastasis (14–18) and Alzheimer’s disease (19).Several mechanisms regulate tightly the activation of cofilin in response to upstream stimuli, including interaction with phosphatidylinositol (4,5)-bisphosphate (20–22), local pH changes (23, 24), and phosphorylation at a single regulatory serine (Ser3) (8, 25). The phosphorylation of cofilin, leading to its inactivation, is catalyzed by two kinase families: the LIM-kinases [LIMKs(Lin11, Isl-1, and Mec-3 domain)] and the testicular kinases (25–27). Two primary families of ser/thr phosphatases dephosphorylate and reactivate the actin-depolymerizing and -severing functions of cofilin: slingshot (SSH) (28) and chronophin (CIN) (29).SSH was identified as a cofilin phosphatase through genetic studies in Drosophila (28). The most active and abundant SSH isoform, SSH-1L, has been implicated in such biological processes as cell division, growth cone motility/morphology, neurite extension, and actin dynamics during membrane protrusion (30). SSH dephosphorylates a number of actin regulatory proteins in addition to cofilin, including LIMK1 (31) and Coronin 1B (32). CIN is a haloacid dehydrogenase-type phosphatase, a family of enzymes with activity in mammalian cells that has been poorly characterized. CIN dephosphorylates a very limited number of substrates (33) and as opposed to SSH, has little phosphatase activity toward LIMK both in vitro and in vivo; thus, it seems to be the more specific activator of cofilin (29, 30). CIN exhibits several predicted interaction motifs potentially linking it to regulation by PI3-kinase and phospholipase Cγ (PLCγ), both of which have been implicated in signaling to cofilin activation in vivo in MTLn3 adenocarcinoma cells (10, 34). CIN has been involved in cell division (29), cofilin–actin rod formation in neurons (35), and chemotaxing leukocytes (36, 37). The molecular mechanisms that control the activity and localization of CIN in cells are still not well-understood. In neutrophils, CIN mediates cofilin dephosphorylation downstream of Rac2 (36), and stimulation of protease-activated receptor2 results in recruitment of CIN and cofilin at the cell edge by β-arrestins to promote localized generation of free actin barbed ends, membrane protrusion, and chemotaxis (37). Chemotaxis to EGF by breast tumor cells is directly correlated with cancer cell invasion and metastasis (38, 39). Although cofilin activity is required for tumor cell migration, the contribution(s) of CIN to the regulation of actin dynamics at the leading edge has not yet been investigated.The importance of cofilin in regulating tumor cell motility has been extensively studied using MTLn3 mammary carcinoma cells as a model system. The initial step of MTLn3 cell chemotaxis to EGF consists of a biphasic actin polymerization response resulting from two peaks of free actin barbed end formation (34, 40, 41). The first or early peak of actin polymerization occurs at 1 min after EGF stimulation and requires both cofilin and PLCγ activities (34), but it is not dependent on cofilin dephosphorylation (42). This first transient allows the cells to sense EGF gradients and initiate small-membrane protrusions (11). The second or late peak of actin polymerization occurs at 3 min and is dependent on both cofilin and PI3-kinase activities (43, 44). Cofilin activity in this late transient has been associated with full protrusion of lamellipodia (34). The mechanism by which cofilin becomes activated at the 3-min peak has not been identified, although it is likely to involve the phosphoregulation of Ser3 (42, 45).In this work, we determine the molecular mechanisms involved in the full protrusion of the leading edge upon EGF stimulation. We have identified CIN as a critical regulator of cofilin activation to coordinate leading edge dynamics. Our results yield insights into how CIN controls cell protrusion, a key step in the process of cell migration and metastasis.     

Nonequilibrium dynamics and ultraslow relaxation of confined DNA during viral packaging

Many viruses use molecular motors that generate large forces to package DNA to near-crystalline densities inside preformed viral proheads. Besides being a key step in viral assembly, this process is of interest as a model for understanding the physics of charged polymers under tight 3D confinement. A large number of theoretical studies have modeled DNA packaging, and the nature of the molecular dynamics and the forces resisting the tight confinement is a subject of wide debate. Here, we directly measure the packaging of single DNA molecules in bacteriophage phi29 with optical tweezers. Using a new technique in which we stall the motor and restart it after increasing waiting periods, we show that the DNA undergoes nonequilibrium conformational dynamics during packaging. We show that the relaxation time of the confined DNA is >10 min, which is longer than the time to package the viral genome and 60,000 times longer than that of the unconfined DNA in solution. Thus, the confined DNA molecule becomes kinetically constrained on the timescale of packaging, exhibiting glassy dynamics, which slows the motor, causes significant heterogeneity in packaging rates of individual viruses, and explains the frequent pausing observed in DNA translocation. These results support several recent hypotheses proposed based on polymer dynamics simulations and show that packaging cannot be fully understood by quasistatic thermodynamic models.DNA packaging is both a critical step in viral assembly and a unique model for understanding the physics of polymers under strong confinement. Before packaging, the DNA (∼6–60 µm long) forms a loose random coil of diameter ∼1–3 µm. After translocation into the viral prohead (∼50–100 nm in diameter), a ∼10,000-fold volume compaction is achieved. Packaging is driven by a powerful molecular motor that must work against the large forces resisting confinement arising from DNA bending, repulsion between DNA segments, and entropy loss (1–8).DNA packaging in bacteriophages phi29, lambda, and T4 has been directly measured via single-molecule manipulation with optical tweezers and the packaging motors have been shown to generate forces of >60 pN, among the highest known for biomotors, while translocating DNA at rates ranging from ∼100 bp (for phage phi29, which packages a 19.3-kbp genome into a 42 × 54-nm prohead shell) up to as high as ∼2,000 bp/s (for phage T4, which packages a 171-kbp genome into a 120 × 86-nm prohead) (9–15). The force resisting packaging rises steeply with prohead filling and has been proposed to play an important role in driving viral DNA ejection (16).Recently, a variety of theoretical models for viral DNA packaging have been proposed (3–5, 17–21). The simplest treat DNA as an elastic rod with repulsive self-interactions and assume that packaging is a quasistatic thermodynamic process, i.e., that the DNA is able to continuously relax to a free-energy minimum state (3–5, 19–21). The DNA arrangement is generally assumed to be an inverse spool with local hexagonal close packing between DNA segments, as suggested by electron microscopy and X-ray scattering studies (22, 23). Such models yield exact analytical predictions that reproduce many of the experimental trends, including the sharp rise in resistance during the latter stages of packaging (3–5, 20).Dynamic simulations, however, predict differing results. Depending on model and simulation protocol, some predict rapid equilibration into ordered spool or folded toroid conformations, whereas others predict nonequilibrium dynamics and disordered conformations (3, 6, 24–31). The packaged DNA conformation also depends on ionic conditions, capsid size and shape, and shape of the internal core structure found in some phages (6, 30). Notably, some electron microscopy studies have also been interpreted as suggesting ordered spooled conformations (22), whereas others have been interpreted as suggesting partly disordered conformations (29). Although some simulations predict nonequilibrium dynamics, several potential caveats are that (i) the DNA has been represented by coarse-grained polymer models with various approximations for physical interactions (6), (ii) the packaging rate used in the simulations is >10⁵ times higher than the measured packaging rate due to computational constraints (3, 26–28), and (iii) it has been pointed out by some authors that simulation timescale cannot be directly related to experimental timescale because of the use of coarse-grained models for DNA (25, 28). As noted in early modeling studies, the calculations based on quasistatic models may represent a lower bound on the required packaging forces due to dissipative dynamic losses (4). Whether nonequilibrium dynamics play a significant role in real systems has thus remained an important open question.     

Self-assembly of alkynylplatinum(II) terpyridine amphiphiles into nanostructures via steric control and metal–metal interactions

A series of mono- and dinuclear alkynylplatinum(II) terpyridine complexes containing the hydrophilic oligo(para-phenylene ethynylene) with two 3,6,9-trioxadec-1-yloxy chains was designed and synthesized. The mononuclear alkynylplatinum(II) terpyridine complex was found to display a very strong tendency toward the formation of supramolecular structures. Interestingly, additional end-capping with another platinum(II) terpyridine moiety of various steric bulk at the terminal alkyne would lead to the formation of nanotubes or helical ribbons. These desirable nanostructures were found to be governed by the steric bulk on the platinum(II) terpyridine moieties, which modulates the directional metal−metal interactions and controls the formation of nanotubes or helical ribbons. Detailed analysis of temperature-dependent UV-visible absorption spectra of the nanostructured tubular aggregates also provided insights into the assembly mechanism and showed the role of metal−metal interactions in the cooperative supramolecular polymerization of the amphiphilic platinum(II) complexes.Square-planar d⁸ platinum(II) polypyridine complexes have long been known to exhibit intriguing spectroscopic and luminescence properties (1–54) as well as interesting solid-state polymorphism associated with metal−metal and π−π stacking interactions (1–14, 25). Earlier work by our group showed the first example, to our knowledge, of an alkynylplatinum(II) terpyridine system [Pt(tpy)(C ≡ CR)]⁺ that incorporates σ-donating and solubilizing alkynyl ligands together with the formation of Pt···Pt interactions to exhibit notable color changes and luminescence enhancements on solvent composition change (25) and polyelectrolyte addition (26). This approach has provided access to the alkynylplatinum(II) terpyridine and other related cyclometalated platinum(II) complexes, with functionalities that can self-assemble into metallogels (27–31), liquid crystals (32, 33), and other different molecular architectures, such as hairpin conformation (34), helices (35–38), nanostructures (39–45), and molecular tweezers (46, 47), as well as having a wide range of applications in molecular recognition (48–52), biomolecular labeling (48–52), and materials science (53, 54). Recently, metal-containing amphiphiles have also emerged as a building block for supramolecular architectures (42–44, 55–59). Their self-assembly has always been found to yield different molecular architectures with unprecedented complexity through the multiple noncovalent interactions on the introduction of external stimuli (42–44, 55–59).Helical architecture is one of the most exciting self-assembled morphologies because of the uniqueness for the functional and topological properties (60–69). Helical ribbons composed of amphiphiles, such as diacetylenic lipids, glutamates, and peptide-based amphiphiles, are often precursors for the growth of tubular structures on an increase in the width or the merging of the edges of ribbons (64, 65). Recently, the optimization of nanotube formation vs. helical nanostructures has aroused considerable interests and can be achieved through a fine interplay of the influence on the amphiphilic property of molecules (66), choice of counteranions (67, 68), or pH values of the media (69), which would govern the self-assembly of molecules into desirable aggregates of helical ribbons or nanotube scaffolds. However, a precise control of supramolecular morphology between helical ribbons and nanotubes remains challenging, particularly for the polycyclic aromatics in the field of molecular assembly (64–69). Oligo(para-phenylene ethynylene)s (OPEs) with solely π−π stacking interactions are well-recognized to self-assemble into supramolecular system of various nanostructures but rarely result in the formation of tubular scaffolds (70–73). In view of the rich photophysical properties of square-planar d⁸ platinum(II) systems and their propensity toward formation of directional Pt···Pt interactions in distinctive morphologies (27–31, 39–45), it is anticipated that such directional and noncovalent metal−metal interactions might be capable of directing or dictating molecular ordering and alignment to give desirable nanostructures of helical ribbons or nanotubes in a precise and controllable manner.Herein, we report the design and synthesis of mono- and dinuclear alkynylplatinum(II) terpyridine complexes containing hydrophilic OPEs with two 3,6,9-trioxadec-1-yloxy chains. The mononuclear alkynylplatinum(II) terpyridine complex with amphiphilic property is found to show a strong tendency toward the formation of supramolecular structures on diffusion of diethyl ether in dichloromethane or dimethyl sulfoxide (DMSO) solution. Interestingly, additional end-capping with another platinum(II) terpyridine moiety of various steric bulk at the terminal alkyne would result in nanotubes or helical ribbons in the self-assembly process. To the best of our knowledge, this finding represents the first example of the utilization of the steric bulk of the moieties, which modulates the formation of directional metal−metal interactions to precisely control the formation of nanotubes or helical ribbons in the self-assembly process. Application of the nucleation–elongation model into this assembly process by UV-visible (UV-vis) absorption spectroscopic studies has elucidated the nature of the molecular self-assembly, and more importantly, it has revealed the role of metal−metal interactions in the formation of these two types of nanostructures.     

Random bursts determine dynamics of active filaments

Constituents of living or synthetic active matter have access to a local energy supply that serves to keep the system out of thermal equilibrium. The statistical properties of such fluctuating active systems differ from those of their equilibrium counterparts. Using the actin filament gliding assay as a model, we studied how nonthermal distributions emerge in active matter. We found that the basic mechanism involves the interplay between local and random injection of energy, acting as an analog of a thermal heat bath, and nonequilibrium energy dissipation processes associated with sudden jump-like changes in the system’s dynamic variables. We show here how such a mechanism leads to a nonthermal distribution of filament curvatures with a non-Gaussian shape. The experimental curvature statistics and filament relaxation dynamics are reproduced quantitatively by stochastic computer simulations and a simple kinetic model.In active systems, perpetual local energy input prevents relaxation into a thermal equilibrium state (1–3). Examples are living matter (4–10) or appropriately reconstituted or synthetic model systems (11–17). It is widely accepted that nonthermal fluctuations play a crucial role for the dynamics of active systems (8, 9, 18–24) and may even cause an apparent violation of the fluctuation-dissipation theorem (11). The physical origin of the violation can be attributed to local tensile stresses generated by myosin minifilaments, as shown by rheological measurements of 3D actin networks consisting of myosin II, actin filaments, and cross-linkers (11). Although this study focused on how the macroscopic properties of the active filament network are altered with respect to its equilibrium counterpart, we consider how local stresses generated by motors mesoscopically affect the dynamics and the conformational statistics of individual filaments. To this end, we use the actin gliding assay (25, 26), which has become a paradigm of active systems. In this assay, actin filaments are moved by individual nonprocessive myosin motors, which are bound to a substrate. We find that motile filaments in this assay display a nonthermal distribution of curvatures with an exponential shape, which is essentially different from its equilibrium counterpart. Based on our observations, we were able to elucidate the origin of the nonthermal fluctuations in the gliding assay and introduce a mechanism that explains how nonthermal distributions may emerge in active matter systems. The mechanism relies on the interplay between local and random input of energy, acting as an analog of a thermal heat bath, and nonequilibrium energy dissipation processes due to sudden jump-like changes in the system’s dynamic variables. We perform stochastic simulations of the filament’s dynamics and provide a rationale drawn from kinetic theory. Both approaches quantitatively reproduce the experimental curvature distribution and correctly predict the relaxation dynamics of the active filament.     

Understanding the kinetic mechanism of RNA single base pair formation

RNA functions are intrinsically tied to folding kinetics. The most elementary step in RNA folding is the closing and opening of a base pair. Understanding this elementary rate process is the basis for RNA folding kinetics studies. Previous studies mostly focused on the unfolding of base pairs. Here, based on a hybrid approach, we investigate the folding process at level of single base pairing/stacking. The study, which integrates molecular dynamics simulation, kinetic Monte Carlo simulation, and master equation methods, uncovers two alternative dominant pathways: Starting from the unfolded state, the nucleotide backbone first folds to the native conformation, followed by subsequent adjustment of the base conformation. During the base conformational rearrangement, the backbone either retains the native conformation or switches to nonnative conformations in order to lower the kinetic barrier for base rearrangement. The method enables quantification of kinetic partitioning among the different pathways. Moreover, the simulation reveals several intriguing ion binding/dissociation signatures for the conformational changes. Our approach may be useful for developing a base pair opening/closing rate model.RNAs perform critical cellular functions at the level of gene expression and regulation (1–4). RNA functions are determined not only by RNA structure or structure motifs [e.g., tetraloop hairpins (5, 6)] but also by conformational distributions and dynamics and kinetics of conformational changes. For example, riboswitches can adopt different conformations in response to specific conditions of the cellular environment (7, 8). Understanding the kinetics, such as the rate and pathways for the conformational changes, is critical for deciphering the mechanism of RNA function (9–19). Extensive experimental and theoretical studies on RNA folding kinetics have provided significant insights into the kinetic mechanism of RNA functions (19–36). However, due to the complexity of the RNA folding energy landscape (37–46) and the limitations of experimental tools (47–55), many fundamental problems, including single base flipping and base pair formation and fraying, remain unresolved. These unsolved fundamental problems have hampered our ability to resolve other important issues, such as RNA hairpin and larger structure folding kinetics. Several key questions remain unanswered, such as whether the hairpin folding is rate-limited by the conformational search of the native base pairs, whose formation leads to fast downhill folding of the whole structure, or by the breaking of misfolded base pairs before refolding to the native structure (18, 19, 54–73).Motivated by the need to understand the basic steps of nucleic acids folding, Hagan et al. (74) performed forty-three 200-ps unfolding trajectories at 400 K and identified both on- and off-pathway intermediates and two dominant unfolding pathways for a terminal C-G base pair in a DNA duplex. In one of the pathways, base pairing and stacking interactions are broken concomitantly, whereas in the other pathway, base stacking is broken after base pairing is disrupted. Furthermore, the unfolding requires that the Cyt diffuse away from the pairing Gua to a distance such that the C-G hydrogen bond cannot reform easily. More recently, Colizzi and Bussi (75) performed molecular dynamics (MD) pulling simulations for an RNA duplex and construct free energy landscape from the pulling simulation. The simulation showed that the base pair opening reaction starts with the unbinding of the 5′-base, followed by the unbinding of the 3′-base (i.e., the 5′-base is less stable than the 3′-base). These previous unfolding simulations offered significant insights into the pathways and transition states. However, as shown below, several important issues remain.One intriguing problem is the rate model for base pairing. There are currently three main types of models. In the first type of model, the barrier ΔG+‡ for closing a base pair is dominated by the entropic cost ΔS for positioning the nucleotides to the base-paired configuration and the barrier ΔG−‡ for opening a base pair is the enthalpic cost ΔH for disrupting the hydrogen bonds and base stacking interactions (18, 59, 60). In the second type of model, ΔG+‡ is the net free energy change for base pairing ΔG = ΔH ? TΔS and ΔG−‡ is zero (76, 77). In the third type of model, ΔG±‡=±ΔG/2 is used (78). In addition to the above three main types, other models, such as more sophisticated hybrid rate models, have been proposed (29).In this paper, we report a hybrid method (see Fig. 1) to investigate the single base pairing process. In contrast to the previous simulations for temperature- or force-induced unfolding reactions, we directly model the folding process here (i.e., the base pair closing process). Specifically, we use MD simulations to identify the conformational clusters. Based on the network of the conformational clusters as a reduced conformational ensemble, we apply kinetic Monte Carlo (KMC) and master equation (ME) methods to elucidate the detailed roles of base pairing and stacking interactions, as well as the roles of water and ions (79–82). The study reveals previously unidentified kinetics pathways, misfolded states, and rate-limiting steps. A clear understanding of the microscopic details of the elementary kinetic move is a prerequisite for further rigorous study of large-scale RNA kinetic studies. The method described here may provide a feasible way to develop a rate model for the base pair/stack-based kinetic move set. Furthermore, the mechanism of RNA single base folding may provide useful insights into many biologically significant processes, such as nucleotide flipping (83) in helicases and base pair fraying (84) (as the possible first step for nucleic duplex melting in nucleic acid enzymatic processes).Open in a separate windowFig. 1.(A) Folding of a single nucleotide (G1, red) from the unfolded (Left) to the native folded (Right) state. (B) Exhaustive sampling for the (discrete) conformations of the G1 nucleotide (Right) through enumeration of the torsion angles (formed by the blue bonds). (C) Schematic plot shows the trajectories on the energy landscape (depicted with two reaction coordinates for clarity) explored by the MD simulations. The lines, open circles, and hexagons denote the trajectories; the initial states; and the (centroid structures of the) clusters, respectively. (D) Conformational network based on six clusters. (E) The rmsds to the different clusters provide information about the structural changes in a MD trajectory.     

Crucial role of nonspecific interactions in amyloid nucleation

Protein oligomers have been implicated as toxic agents in a wide range of amyloid-related diseases. However, it has remained unsolved whether the oligomers are a necessary step in the formation of amyloid fibrils or just a dangerous byproduct. Analogously, it has not been resolved if the amyloid nucleation process is a classical one-step nucleation process or a two-step process involving prenucleation clusters. We use coarse-grained computer simulations to study the effect of nonspecific attractions between peptides on the primary nucleation process underlying amyloid fibrillization. We find that, for peptides that do not attract, the classical one-step nucleation mechanism is possible but only at nonphysiologically high peptide concentrations. At low peptide concentrations, which mimic the physiologically relevant regime, attractive interpeptide interactions are essential for fibril formation. Nucleation then inevitably takes place through a two-step mechanism involving prefibrillar oligomers. We show that oligomers not only help peptides meet each other but also, create an environment that facilitates the conversion of monomers into the β-sheet–rich form characteristic of fibrils. Nucleation typically does not proceed through the most prevalent oligomers but through an oligomer size that is only observed in rare fluctuations, which is why such aggregates might be hard to capture experimentally. Finally, we find that the nucleation of amyloid fibrils cannot be described by classical nucleation theory: in the two-step mechanism, the critical nucleus size increases with increases in both concentration and interpeptide interactions, which is in direct contrast with predictions from classical nucleation theory.During the process of amyloid formation, normally soluble proteins assemble into fibrils that are enriched in β-sheet content and have diameters of a few nanometers and lengths up to several micrometers. This phenomenon has been implicated in a variety of pathogenic processes, including Alzheimer’s and Parkinson’s diseases, type 2 diabetes, and systemic amyloidoses (1–3). The association with human diseases has largely motivated a long-standing effort to probe the assembly process, and numerous studies have aimed at elucidating the mechanism of amyloid aggregation (4). The basic nature of the aggregation reaction has emerged as a nucleation and growth process (5, 6), where the aggregates are created through a not well-understood primary nucleation event and can grow by recruiting additional peptides or proteins to their ends (7, 8). In this paper, we focus on the nature of this primary step in amyloid nucleation and the fundamental initial events that underlie amyloid formation.Amyloidogenic peptides and proteins, when in their nonpathological cellular form, can range in the structures from mainly α-helical to β-sheet and even random coil, whereas the amyloid forms of proteins possess a generic cross–β-structure (9–14). The formation of amyloid is, hence, accompanied by marked changes in the conformations of the peptides and proteins that undergo this process. A pertinent question is whether this conformational change takes place simultaneously with the nucleation process or whether nucleation takes place first and is then followed by conformational change. These two possible scenarios of nucleation have been extensively discussed in the experimental and theoretical literature (5, 8, 15–19). We will refer in this work to the two scenarios simply as one-step nucleation (1SN), in which the β-sheet–enriched nucleus forms directly from the solution, and two-step nucleation (2SN), where soluble monomers first assemble into disordered oligomers, which subsequently convert into a β-sheet nucleus. Disordered oligomers, ranging in size between dimers and micrometer-sized particles, have been observed in some experiments (20–28). These findings highlight a central question regarding the role of disordered oligomers in fibril formation: are such clusters a necessary step in the process of fibril formation or just a byproduct?From a biological and biomedical perspective, it is important to understand the conditions under which oligomeric clusters form, because such species exhibit high cytotoxicity (1, 29–31). Indeed, there is strong evidence that the disordered oligomers rather than fully grown fibrils are the main pathogenic species in protein aggregation diseases (31–33). As such, defining the role of the prefibrillar oligomers during amyloid formation will be crucial to develop intervention strategies that target these species (1, 30, 34, 35).Mutations in the polypeptide sequence and extrinsic changes in the experimental conditions are known to alter the concentrations of aggregated species, their size, and their cytotoxicity (25, 36–39). For instance, mutations that increase hydrophobicity of the Alzheimer’s β-peptide (Aβ1–42) have a pronounced effect on its aggregation behavior and the size distribution of the resulting oligomers (23–26, 40), promoting toxicity and expediting the fibrillization process. In the same spirit, two extra hydrophobic residues in Aβ1–42 are believed to contribute to the more pronounced oligomerization and faster fibrillization compared with its alloform Aβ1–40 (24, 25, 40). Temperature, pH, and concentration of certain metals also affect oligomerization and pathways of fibrillization (41–44).The common feature of the above experiments is that they modify the internal free energy difference between the soluble and the β-sheet–forming state, also called the β-sheet propensity, which has been extensively studied in the literature (45–48). However, they also modify interactions between peptides that aggregate, a crucial contribution that has not yet been systematically addressed.In this paper, we study the effect of nonspecific interactions between peptides on the amyloid nucleation process. Such nonspecific interactions do not depend on the atomistic details of the amino acids involved, allowing us to address question about amyloid aggregation and nucleation using a coarse-grained model. In particular, generic hydrophobic stretches in the sequence of Aβ have been shown to be sufficient to promote aggregation (49, 50). Mutations of nonpolar residues to other nonpolar residues had little or no effect on aggregation, whereas mutations that reduce charge and/or increase hydrophobicity enhanced it (50, 51). Furthermore, atomic force microscopy measurements have shown that the strength of overall interactions between amyloidogenic proteins correlates with their tendency to aggregate (52, 53).We have performed extensive computer simulations that allowed us to observe both the 1SN and the 2SN mechanisms. These simulations reveal that 1SN and 2SN can be viewed as two limits of the same process, something that several previous studies have suspected (16, 18). Importantly, we observe that only 2SN is possible at low peptide concentrations, comparable with the levels that are found in vivo. Another key observation is that fibril nucleation typically does not proceed through the most prevalent oligomeric species but rather, through an oligomer with a size that is only observed as a result of rare fluctuations. As a consequence, such oligomers will be hard to capture experimentally, although their presence is required for nucleation to take place. Our simulations show that the free energy barrier for fibril nucleation through the two-step mechanism decreases with increasing strength of the interpeptide interactions. Furthermore, the critical nucleus size in the two-step mechanism is found to grow with the increase in the peptide concentrations as well as with stronger interpeptide interactions, which is in direct contrast with the classical nucleation. These results imply that weakening the nonspecific interactions between peptide monomers in solution and thereby, simultaneously increasing both the free energy barrier for oligomer formation and the free energy barrier for peptide conversion at a given oligomer size may be a crucial step in preventing amyloid aggregation.     

Actomyosin dynamics drive local membrane component organization in an in vitro active composite layer

The surface of a living cell provides a platform for receptor signaling, protein sorting, transport, and endocytosis, whose regulation requires the local control of membrane organization. Previous work has revealed a role for dynamic actomyosin in membrane protein and lipid organization, suggesting that the cell surface behaves as an active composite composed of a fluid bilayer and a thin film of active actomyosin. We reconstitute an analogous system in vitro that consists of a fluid lipid bilayer coupled via membrane-associated actin-binding proteins to dynamic actin filaments and myosin motors. Upon complete consumption of ATP, this system settles into distinct phases of actin organization, namely bundled filaments, linked apolar asters, and a lattice of polar asters. These depend on actin concentration, filament length, and actin/myosin ratio. During formation of the polar aster phase, advection of the self-organizing actomyosin network drives transient clustering of actin-associated membrane components. Regeneration of ATP supports a constitutively remodeling actomyosin state, which in turn drives active fluctuations of coupled membrane components, resembling those observed at the cell surface. In a multicomponent membrane bilayer, this remodeling actomyosin layer contributes to changes in the extent and dynamics of phase-segregating domains. These results show how local membrane composition can be driven by active processes arising from actomyosin, highlighting the fundamental basis of the active composite model of the cell surface, and indicate its relevance to the study of membrane organization.The cell surface mediates interactions between the cell and the outside world by serving as the site for signal transduction. It also facilitates the uptake and release of cargo and supports adhesion to substrates. These diverse roles require that the cell surface components involved in each function are spatially and temporally organized into domains spanning a few nanometers (nanoclusters) to several micrometers (microdomains). The cell surface itself may be considered as a fluid–lipid bilayer wherein proteins are embedded (1). In the living cell, this multicomponent system is supported by an actin cortex, composed of a branched network of actin and a collection of filaments (2–4).Current models of membrane organization fall into three categories: those invoking lipid–lipid and lipid–protein interactions in the plasma membrane [e.g., the fluid mosaic model (1, 5) and the lipid raft hypothesis (6)], or those that appeal to the membrane-associated actin cortex (e.g., the picket fence model) (7), or a combination of these (8, 9). Although these models based on thermodynamic equilibrium principles have successfully explained the organization and dynamics of a range of membrane components and molecules, there is a growing class of phenomena that appears inconsistent with chemical and thermal equilibrium, which might warrant a different explanation. These include aspects of the organization and dynamics of outer leaflet glycosyl-phosphatidylinositol-anchored proteins (GPI-anchored proteins) (10–13), inner leaflet Ras proteins (14), and actin-binding transmembrane proteins (13, 15, 16).Recent experimental and theoretical work has shown that these features can be explained by taking into account that many cortical and membrane proteins are driven by ATP-consuming processes that drive the system out of equilibrium (13, 15, 17). The membrane models mentioned above have by-and-large neglected this active nature of the actin cortex where actin filaments are being continuously polymerized and depolymerized (18–21), in addition to being persistently acted upon by a variety of myosin motors (22–24) that consume ATP and exert contractile stresses on cortical actin filaments, continually remodeling the architecture of the cortex (4, 21, 25). These active processes in turn can generate tangential stresses and currents on the cell surface, which could drive the dynamics and local composition of membrane components at different scales (22, 26–29).Actin polymerization is proposed to be driven at the membrane by two nucleators, the Arp2/3 complex, which creates a densely branched network, as well as formins that nucleate filaments (18, 21, 30). A number of myosin motors are also associated with the juxtamembranous actin cortex, of which nonmuscle myosin II is the major component in remodeling the cortex and creating actin flows (4, 23, 25, 26, 31, 32). Based on our observations that the clustering of cell surface components that couple directly or indirectly to cortical actin [e.g., GPI-anchored proteins, proteins of the Ezrin, Radaxin, or Moesin (ERM) family (13, 15)] depends on myosin activity, we proposed that this clustering arises from the coupling to contractile actomyosin platforms (called “actin asters”) produced at the cortex (15, 33).A coarse-grained theory describing this idea has been put forward and corroborated by the verification of its key predictions in live cells (15, 33), but a systematic identification of the underlying microscopic processes is lacking. Given the complexity of numerous processes acting at the membrane of a living cell, we use an in vitro approach to study the effect of an energy-consuming actomyosin network on the dynamics of membrane molecules that directly interact with filamentous actin.A series of in vitro studies have explored the organization of confined, dynamic filaments (both actin and microtubules) (34–39) or the role of actin architecture on membrane organization (40–46). Indeed, these studies have yielded insights into the nontrivial emergent configurations that mixtures of polar filaments and motors can adopt when fueled by ATP (34–37), in particular constitutively remodeling steady states that display characteristics of active mechanics (38, 39, 47). However, the effect of linking these mechanics to the confining lipid bilayer and its organization has not been studied.The consequences of actin polymerization on membrane organization, in particular on giant unilamellar vesicles (GUVs), have been addressed in a number of studies on the propulsion of GUVs by an actin comet tail (40, 45, 46). In those experiments, the apparent advection of membrane bound ActA or WASP toward the site of actin polymerization is mainly due to the change in binding affinity of WASP to actin through Arp2/3 (44) and the spherical geometry resulting in the drag of actin to one pole of the vesicle after symmetry break of the actin shell. That this dynamic process changes the bulk properties of the bilayer, namely the critical temperature of a phase-separating lipid bilayer, was shown by Liu and Fletcher (40) when the actin nucleator N-WASP was connected to a lipid species (PIP₂) that was capable of partitioning into one of the two phases.Besides these pioneering studies on the effects of active processes on membrane organization, little was done to directly test the effect of active lateral stresses as well as actomyosin remodeling at the membrane, particularly on the dynamics and organization of membrane-associated components.To this end, we build an active composite in vitro by stepwise addition of components: a supported lipid bilayer with an actin-binding component, actin filaments, and myosin motors. By systematically varying the concentrations of actin and myosin as well as the average actin filament length, we find distinct states of actomyosin organization at the membrane surface upon complete ATP consumption. More importantly, we find that the ATP-fueled contractile actomyosin currents induce the transient accumulation of actin-binding membrane components. As predicted, the active mechanics of actin and myosin at physiologically relevant ATP concentrations drives the system into a nonequilibrium steady state with anomalous density fluctuations and the transient clustering of actin-binding components of the lipid bilayer (15, 33). Finally, connection of this active layer of actomyosin to a phase-segregating bilayer, influences its phase behavior and coarsening dynamics.     

Programmable motion of DNA origami mechanisms

DNA origami enables the precise fabrication of nanoscale geometries. We demonstrate an approach to engineer complex and reversible motion of nanoscale DNA origami machine elements. We first design, fabricate, and characterize the mechanical behavior of flexible DNA origami rotational and linear joints that integrate stiff double-stranded DNA components and flexible single-stranded DNA components to constrain motion along a single degree of freedom and demonstrate the ability to tune the flexibility and range of motion. Multiple joints with simple 1D motion were then integrated into higher order mechanisms. One mechanism is a crank–slider that couples rotational and linear motion, and the other is a Bennett linkage that moves between a compacted bundle and an expanded frame configuration with a constrained 3D motion path. Finally, we demonstrate distributed actuation of the linkage using DNA input strands to achieve reversible conformational changes of the entire structure on ∼minute timescales. Our results demonstrate programmable motion of 2D and 3D DNA origami mechanisms constructed following a macroscopic machine design approach.The ability to control, manipulate, and organize matter at the nanoscale has demonstrated immense potential for advancements in industrial technology, medicine, and materials (1–3). Bottom-up self-assembly has become a particularly promising area for nanofabrication (4, 5); however, to date designing complex motion at the nanoscale remains a challenge (6–9). Amino acid polymers exhibit well-defined and complex dynamics in natural systems and have been assembled into designed structures including nanotubes, sheets, and networks (10–12), although the complexity of interactions that govern amino acid folding make designing complex geometries extremely challenging. DNA nanotechnology, on the other hand, has exploited well-understood assembly properties of DNA to create a variety of increasingly complex designed nanostructures (13–15).Scaffolded DNA origami, the process of folding a long single-stranded DNA (ssDNA) strand into a custom structure (16–18), has enabled the fabrication of nanoscale objects with unprecedented geometric complexity that have recently been implemented in applications such as containers for drug delivery (19, 20), nanopores for single-molecule sensing (21–23), and templates for nanoparticles (24, 25) or proteins (26–28). The majority of these and other applications of DNA origami have largely focused on static structures. Natural biomolecular machines, in contrast, have a rich diversity of functionalities that rely on complex but well-defined and reversible conformational changes. Currently, the scope of biomolecular nanotechnology is limited by an inability to achieve similar motion in designed nanosystems.DNA nanotechnology has enabled critical steps toward that goal starting with the work of Mao et al. (29), who developed a DNA nanostructure that took advantage of the B–Z transition of DNA to switch states. Since then, efforts to fabricate dynamic DNA systems have primarily focused on strand displacement approaches (30) mainly on systems comprising a few strands or arrays of strands undergoing ∼nm-scale motions (31–37) in some cases guided by DNA origami templates (38–40). More recently, strand displacement has been used to reconfigure DNA origami nanostructures, for example opening DNA containers (19, 41, 42), controlling molecular binding (43, 44), or reconfiguring structures (45). The largest triggerable structural change was achieved by Han et al. in a DNA origami Möbius strip (one-sided ribbon structure) that could be opened to approximately double in size (45). Constrained motion has been achieved in systems with rotational motion (19, 20, 32, 41, 44, 46, 47) in some cases to open lid-like components (19, 20, 41) or detect molecular binding (44, 48, 49). A few of these systems achieved reversible conformational changes (32, 41, 44, 46), although the motion path and flexibility were not studied. Constrained linear motion has remained largely unexplored. Linear displacements on the scale of a few nanometers have been demonstrated via conformational changes of DNA structure motifs (50–55), strand invasion to open DNA hairpins (36, 55, 56), or the reversible sliding motion of a DNA tile actuator (56); these cases also did not investigate the motion path or flexibility of motion.Building on these prior studies, this work implements concepts from macroscopic machine design to build modular parts with constrained motion. We demonstrate an ability to tune the flexibility and range of motion and then integrate these parts into prototype mechanisms with designed 2D and 3D motion. We further demonstrate reversible actuation of a mechanism with complex conformational changes on minute timescales.     

Residue-level resolution of alphavirus envelope protein interactions in pH-dependent fusion

Alphavirus envelope proteins, organized as trimers of E2–E1 heterodimers on the surface of the pathogenic alphavirus, mediate the low pH-triggered fusion of viral and endosomal membranes in human cells. The lack of specific treatment for alphaviral infections motivates our exploration of potential antiviral approaches by inhibiting one or more fusion steps in the common endocytic viral entry pathway. In this work, we performed constant pH molecular dynamics based on an atomic model of the alphavirus envelope with icosahedral symmetry. We have identified pH-sensitive residues that cause the largest shifts in thermodynamic driving forces under neutral and acidic pH conditions for various fusion steps. A series of conserved interdomain His residues is identified to be responsible for the pH-dependent conformational changes in the fusion process, and ligand binding sites in their vicinity are anticipated to be potential drug targets aimed at inhibiting viral infections.Alphaviruses, mosquito-borne human pathogens causing severe inflammations and fatal fevers, have infected many millions of people in recent outbreaks worldwide since 2005 (1–3). The lack of a vaccine or specific treatment prompts investigations of the fundamental mechanisms of the alphaviral lifecycle to facilitate the development of effective antiviral therapies (4). Alphaviruses have been reported to enter the cell through receptor-mediated endocytosis. Here, alphaviruses are ferried toward the perinuclear space of the host cell inside vesicles towed by molecular motors and delivered to specific locations for productive replication (5–11). Even when direct entry into the cytoplasm is possible (11–15), the endocytic entry pathway facilitates the transportation of viruses across the crowded cytoplasmic space and delays detection by the immune system without leaving empty capsid or envelope as obvious evidence of the viral infection exposed outside the host cell (10, 11). Before the delivery of its viral genome into the cytoplasm of a host cell, the alphavirus must undergo a critical step of low pH-triggered membrane fusion, which is a common mechanism in the endocytic viral entry pathway among many different viruses. Understanding the mechanism of the low pH-triggered alphaviral membrane fusion is essential for the development of therapies against alphavirus as well as other viruses using similar endocytic entry mechanisms.Recent studies of the lifecycle of alphavirus reveal that a precursor, p62, is first synthesized as a chaperon forming a heterodimer with E1, which is essential for viral budding (16); p62 protects the E1 protein in the low-pH environment of the secretory pathway before being cleaved by cellular furin to produce mature E2-E1 and a smaller fragment, E3 (17–21). After the virus buds from the cytoplasmic membrane, E3 is released from the virus particle under neutral pH conditions outside the host cell (13, 22–24).On the surface of a mature alphavirus, 80 (E2–E1)₃ viral spikes, organized in T = 4 icosahedral symmetry on the viral lipid membrane, enclose the viral capsid and genome (25–43). On internalization of the mature virus in the endosome of the host cell in a new round of infection cycle, the increasingly acidified endosomal environment triggers a series of conformational changes in the alphaviral spike (E2–E1)₃ (38), including the dissociation of E2 (42, 44, 45), release of a fusion loop on E1 (46, 47), and trimerization of E1 (48). The fusion loop, roughly residues 83–100 on the cd loop of each E1 protein (13, 49, 50), in the newly formed E1 homotrimer (HT), inserts into the endosomal membrane. Then, the E1 proteins fold back, pulling the viral and endosomal membranes together and thus, promoting membrane fusion (13, 24).Recently solved high-resolution structures of the alphavirus envelope proteins E2–E1 fitted into cryo-EM data representing the intact virus under both acidic and neutral pH conditions (43, 51, 52) provide excellent atomic models for studies of the low pH-triggered fusion process. The structure of Chikungunya virus (CHIKV) obtained at pH 8.0 represents the initial mature state (M state) of the (E2–E1)₃ viral spike before the fusion process (51). Under pH 5.6, domain B (DB) of E2, which protects the E1 fusion loop, is observed to be disordered in Sindbis virus (52). The rest of the domains of the (E2–E1)₃ spike show moderate conformational differences with an rmsd = 4.0 Å among C_α atoms compared with the structures obtained at pH 8.0 for CHIKV (43, 51). The structure of the envelope proteins in acidic conditions most likely depicts a fusion intermediate (FI) state (52) before E2 dissociation during the low pH-triggered fusion process. In addition, the crystal structure of the folded-back E1 HT (53) is a good model to describe the postfusion state.Based on these atomic models of the E2 and E1 envelope proteins and our previously developed constant pH molecular dynamics (CPHMD) method (54–58), we simulated the envelope proteins with icosahedral symmetry under various pH conditions covering pH 2.0–9.0. We used pH replica exchange in CPHMD and calculated pK_a values using pH titration fitting, which has been shown as a reliable and accurate approach to capture pK_a values of protein residues in various systems (59–64). Through the CPHMD modeling, we calculated the pK_a of the possible pH-sensitive residues (Asp, Glu, and His) in the M, FI, dissociated E2 (Dis), and HT states. We, therefore, derive the shifts in the thermodynamic stabilities originating from each titrating residue for the steps from the M to the FI state (M→FI) of (E2–E1)₃, from the FI to the Dis state (FI→Dis) of E2 proteins, and from the FI to the HT state (FI→HT) of E1 proteins as shown in Fig. 1D. For these processes, we assume that the virus is in the endosomal environment, and we do not consider possible receptor-induced conformational changes. Our residue-level resolution simulations and analyses allow us to identify the critical functional residues with significant pK_a shifts and changes in thermodynamic stability in the low pH-triggered fusion activation. Our results suggest that the most pH-sensitive residues are highly conserved among different alphaviral species and that these critical residues control the pH threshold of fusion activities, provide guidance to further mutagenesis experiments, and lead to more fundamental understanding of low pH-triggered alphaviral membrane fusion.Open in a separate windowFig. 1.Structure and organization of alphaviral envelope proteins. (A) The alphaviral envelope modeled in our simulations. (B) The alphaviral envelope proteins in an MAU. (C) The heterodimer of E2 (DA–DB–DC) and E1 (DI–DII–DIII). (D) Structures of a viral spike in different conformational states simulated for shifts in pK_a values and thermodynamic stabilities. E1 proteins are shown in blue, cyan, and light blue. E2 proteins are shown in red, magenta, and pink.