A lever hypothesis for Synaptotagmin-1 action in neurotransmitter release.

Neurotransmitter release is triggered in microseconds by Ca2+-binding to the Synaptotagmin-1 C2 domains and by SNARE complexes that form four-helix bundles between synaptic vesicles and plasma membranes, but the coupling mechanism between Ca2+-sensing and membrane fusion is unknown. Release requires extension of SNARE helices into juxtamembrane linkers that precede transmembrane regions (linker zippering) and binding of the Synaptotagmin-1 C2B domain to SNARE complexes through a 'primary interface' comprising two regions (I and II). The Synaptotagmin-1 Ca2+-binding loops were believed to accelerate membrane fusion by inducing membrane curvature, perturbing lipid bilayers or helping bridge the membranes, but SNARE complex binding orients the Ca2+-binding loops away from the fusion site, hindering these putative activities. Molecular dynamics simulations now suggest that Synaptotagmin-1 C2 domains near the site of fusion hinder SNARE action, providing an explanation for this paradox and arguing against previous models of Sytnaptotagmin-1 action. NMR experiments reveal that binding of C2B domain arginines to SNARE acidic residues at region II remains after disruption of region I. These results and fluorescence resonance energy transfer assays, together with previous data, suggest that Ca2+ causes reorientation of the C2B domain on the membrane and dissociation from the SNAREs at region I but not region II. Based on these results and molecular modeling, we propose that Synaptotagmin-1 acts as a lever that pulls the SNARE complex when Ca2+ causes reorientation of the C2B domain, facilitating linker zippering and fast membrane fusion. This hypothesis is supported by the electrophysiological data described in the accompanying paper.

Molecular mechanism underlying SNARE-mediated membrane fusion enlightened by all-atom molecular dynamics simulations.

The SNAP receptor (SNARE) proteins syntaxin-1, SNAP-25, and synaptobrevin mediate neurotransmitter release by forming tight SNARE complexes that fuse synaptic vesicles with the plasma membranes in microseconds. Membrane fusion is generally explained by the action of proteins on macroscopic membrane properties such as curvature, elastic modulus, and tension, and a widespread model envisions that the SNARE motifs, juxtamembrane linkers, and C-terminal transmembrane regions of synaptobrevin and syntaxin-1 form continuous helices that act mechanically as semirigid rods, squeezing the membranes together as they assemble ("zipper") from the N to the C termini. However, the mechanism underlying fast SNARE-induced membrane fusion remains unknown. We have used all-atom molecular dynamics simulations to investigate this mechanism. Our results need to be interpreted with caution because of the limited number and length of the simulations, but they suggest a model of membrane fusion that has a natural physicochemical basis, emphasizes local molecular events over general membrane properties, and explains extensive experimental data. In this model, the central event that initiates fast (microsecond scale) membrane fusion occurs when the SNARE helices zipper into the juxtamembrane linkers which, together with the adjacent transmembrane regions, promote encounters of acyl chains from both bilayers at the polar interface. The resulting hydrophobic nucleus rapidly expands into stalk-like structures that gradually progress to form a fusion pore, aided by the SNARE transmembrane regions and without clearly discernible intermediates. The propensity of polyunsaturated lipids to participate in encounters that initiate fusion suggests that these lipids may be important for the high speed of neurotransmitter release.

Hairpin trimer transition state of amyloid fibril.

Protein fibril self-assembly is a universal transition implicated in neurodegenerative diseases. Although fibril structure/growth are well characterized, fibril nucleation is poorly understood. Here, we use a computational-experimental approach to resolve fibril nucleation. We show that monomer hairpin content quantified from molecular dynamics simulations is predictive of experimental fibril formation kinetics across a tau motif mutant library. Hairpin trimers are predicted to be fibril transition states; one hairpin spontaneously converts into the cross-beta conformation, templating subsequent fibril growth. We designed a disulfide-linked dimer mimicking the transition state that catalyzes fibril formation, measured by ThT fluorescence and TEM, of wild-type motif - which does not normally fibrillize. A dimer compatible with extended conformations but not the transition-state fails to nucleate fibril at any concentration. Tau repeat domain simulations show how long-range interactions sequester this motif in a mutation-dependent manner. This work implies that different fibril morphologies could arise from disease-dependent hairpin seeding from different loci.

Automated discovery of algorithms from data.

To automate the discovery of new scientific and engineering principles, artificial intelligence must distill explicit rules from experimental data. This has proven difficult because existing methods typically search through the enormous space of possible functions. Here we introduce deep distilling, a machine learning method that does not perform searches but instead learns from data using symbolic essence neural networks and then losslessly condenses the network parameters into a concise algorithm written in computer code. This distilled code, which can contain loops and nested logic, is equivalent to the neural network but is human-comprehensible and orders-of-magnitude more compact. On arithmetic, vision and optimization tasks, the distilled code is capable of out-of-distribution systematic generalization to solve cases orders-of-magnitude larger and more complex than the training data. The distilled algorithms can sometimes outperform human-designed algorithms, demonstrating that deep distilling is able to discover generalizable principles complementary to human expertise.

Kinetic and thermodynamic allostery in the Ras protein family.

Allostery, the transfer of information between distant parts of a macromolecule, is a fundamental feature of protein function and regulation. However, allosteric mechanisms are usually not explained by protein structure, requiring information on correlated fluctuations uniquely accessible to molecular simulation. Existing work to extract allosteric pathways from molecular dynamics simulations has focused on thermodynamic correlations. Here, we show how kinetic correlations encode complementary information essential to explain observed variations in allosteric regulation. We applied kinetic and thermodynamic correlation analysis on atomistic simulations of H, K, and NRas isoforms in the apo, GTP, and GDP-bound states of Ras protein, with and without complexing to its downstream effector, Raf. We show that switch I and switch II are the primary components of thermodynamic and kinetic allosteric networks, consistent with the key roles of these two motifs. These networks connect the switches to an allosteric loop recently discovered from a crystal structure of HRas. This allosteric loop is inactive in KRas, but is coupled to the hydrolysis arm switch II in NRas and HRas. We find that the mechanism in the latter two isoforms are thermodynamic and kinetic, respectively. Binding of Raf-RBD further activates thermodynamic allostery in HRas and KRas but has limited effect on NRas. These results indicate that kinetic and thermodynamic correlations are both needed to explain protein function and allostery. These two distinct channels of allosteric regulation, and their combinatorial variability, may explain how subtle mutational differences can lead to diverse regulatory profiles among enzymatic proteins.

SAMHD1 impairs type I interferon induction through the MAVS, IKKε, and IRF7 signaling axis during viral infection.

Sterile alpha motif and HD domain-containing protein 1 (SAMHD1) restricts human immunodeficiency virus type 1 (HIV-1) infection by reducing the intracellular dNTP pool. We have shown that SAMHD1 suppresses nuclear factor kappa-B activation and type I interferon (IFN-I) induction by viral infection and inflammatory stimuli. However, the mechanism by which SAMHD1 inhibits IFN-I remains unclear. Here, we show that SAMHD1 inhibits IFN-I activation induced by the mitochondrial antiviral-signaling protein (MAVS). SAMHD1 interacted with MAVS and suppressed MAVS aggregation in response to Sendai virus infection in human monocytic THP-1 cells. This resulted in increased phosphorylation of TANK binding kinase 1 (TBK1), inhibitor of nuclear factor kappa-B kinase epsilon (IKKε), and IFN regulatory factor 3 (IRF3). SAMHD1 suppressed IFN-I activation induced by IKKε and prevented IRF7 binding to the kinase domain of IKKε. We found that SAMHD1 interaction with the inhibitory domain (ID) of IRF7 (IRF7-ID) was necessary and sufficient for SAMHD1 suppression of IRF7-mediated IFN-I activation in HEK293T cells. Computational docking and molecular dynamics simulations revealed possible binding sites between IRF7-ID and full-length SAMHD1. Individual substitution of F411, E416, or V460 in IRF7-ID significantly reduced IRF7 transactivation activity and SAMHD1 binding. Furthermore, we investigated the role of SAMHD1 inhibition of IRF7-mediated IFN-I induction during HIV-1 infection. We found that THP-1 cells lacking IRF7 expression had reduced HIV-1 infection and viral transcription compared to control cells, indicating a positive role of IRF7 in HIV-1 infection. Our findings suggest that SAMHD1 suppresses IFN-I induction through the MAVS, IKKε, and IRF7 signaling axis.

Protein aggregates thermodynamically order regardless of sequence.

Proteins can aggregate into disordered aggregates or ordered assemblies such as amyloid fibrils. These two distinct phases serve differing roles in function and disease. How protein sequence determines the preferred phase is unknown. Here we establish a statistical mechanical disorder-to-order transition condition for compact polymer aggregates, including proteins. The theory produces a simple universal equation determining the favored phase as a function of temperature, polymer length, and interaction energy variance. We show that the sequence-dependent energy variance is efficiently calculated using atomistic simulations, so that the theory has no adjustable parameters. The equation accurately predicts experimental length-dependent crystallization temperatures of synthetic polymers. The equation also predicts that all protein sequences that aggregate will also favor ordering. Consequently, energy must be expended to maintain the steady-state disordered phase if it is not kinetically metastable on physiological timescales. More broadly, the theory suggests that aggregates of organic polymers will generally tend to order on habitable planets.

All-atom molecular dynamics simulations of Synaptotagmin-SNARE-complexin complexes bridging a vesicle and a flat lipid bilayer.

Synaptic vesicles are primed into a state that is ready for fast neurotransmitter release upon Ca2+-binding to Synaptotagmin-1. This state likely includes trans-SNARE complexes between the vesicle and plasma membranes that are bound to Synaptotagmin-1 and complexins. However, the nature of this state and the steps leading to membrane fusion are unclear, in part because of the difficulty of studying this dynamic process experimentally. To shed light into these questions, we performed all-atom molecular dynamics simulations of systems containing trans-SNARE complexes between two flat bilayers or a vesicle and a flat bilayer with or without fragments of Synaptotagmin-1 and/or complexin-1. Our results need to be interpreted with caution because of the limited simulation times and the absence of key components, but suggest mechanistic features that may control release and help visualize potential states of the primed Synaptotagmin-1-SNARE-complexin-1 complex. The simulations suggest that SNAREs alone induce formation of extended membrane-membrane contact interfaces that may fuse slowly, and that the primed state contains macromolecular assemblies of trans-SNARE complexes bound to the Synaptotagmin-1 C2B domain and complexin-1 in a spring-loaded configuration that prevents premature membrane merger and formation of extended interfaces, but keeps the system ready for fast fusion upon Ca2+ influx.

TRIM7 inhibits enterovirus replication and promotes emergence of a viral variant with increased pathogenicity.

To control viral infection, vertebrates rely on both inducible interferon responses and less well-characterized cell-intrinsic responses composed of "at the ready" antiviral effector proteins. Here, we show that E3 ubiquitin ligase TRIM7 is a cell-intrinsic antiviral effector that restricts multiple human enteroviruses by targeting viral 2BC, a membrane remodeling protein, for ubiquitination and proteasome-dependent degradation. Selective pressure exerted by TRIM7 results in emergence of a TRIM7-resistant coxsackievirus with a single point mutation in the viral 2C ATPase/helicase. In cultured cells, the mutation helps the virus evade TRIM7 but impairs optimal viral replication, and this correlates with a hyperactive and structurally plastic 2C ATPase. Unexpectedly, the TRIM7-resistant virus has a replication advantage in mice and causes lethal pancreatitis. These findings reveal a unique mechanism for targeting enterovirus replication and provide molecular insight into the benefits and trade-offs of viral evolution imposed by a host restriction factor.

Explainable neural networks that simulate reasoning.

The success of deep neural networks suggests that cognition may emerge from indecipherable patterns of distributed neural activity. Yet these networks are pattern-matching black boxes that cannot simulate higher cognitive functions and lack numerous neurobiological features. Accordingly, they are currently insufficient computational models for understanding neural information processing. Here, we show how neural circuits can directly encode cognitive processes via simple neurobiological principles. To illustrate, we implemented this model in a non-gradient-based machine learning algorithm to train deep neural networks called essence neural networks (ENNs). Neural information processing in ENNs is intrinsically explainable, even on benchmark computer vision tasks. ENNs can also simulate higher cognitive functions such as deliberation, symbolic reasoning and out-of-distribution generalization. ENNs display network properties associated with the brain, such as modularity, distributed and localist firing, and adversarial robustness. ENNs establish a broad computational framework to decipher the neural basis of cognition and pursue artificial general intelligence.

Circuit Reduction of Heterogeneous Nonequilibrium Systems.

Predicting the behavior of heterogeneous nonequilibrium systems is currently analytically intractable. Consequently, complex biological systems have resisted unifying principles. Here, I introduce a mapping from dynamical systems to battery-resistor circuits. I show that in these transformed variables (i) arbitrary numbers of heterogeneous dynamical transitions can be reduced to a Thevenin equivalent resistor which is invariant to driving from equilibrium, (ii) resistors (together with the external driving sources) are sufficient to describe system behavior, and (iii) the resistor's directional symmetry leads to universal theorems of nonequilibrium behavior. This mapping is used to derive two general steady-state relations. First, for any cyclic process, the maximum amplification of any state is tightly bounded by the total dissipation of all states; experimental data are used to show that the master signal protein Ras achieves this bound. Second, for any process, the response of any reaction due to driving any other reaction is identical to the reciprocal response rescaled by the ratio of the corresponding Thevenin resistors. This result generalizes Onsager's reciprocal relation to the strongly driven regime and makes a testable prediction about how systems should be designed or evolved to maximize response. These analytic results represent a new perspective applicable to biological complexity and suggest that this mapping provides the natural variables to study heterogeneous nonequilibrium systems.

Directional allosteric regulation of protein filament length.

Cofilin and ADF are cytoskeleton remodeling proteins that cooperatively bind and fragment actin filaments. Bound cofilin molecules do not directly interact with each other, indicating that cooperative binding of cofilin is mediated by the actin filament lattice. Cofilactin is therefore a model system for studying allosteric regulation of self-assembly. How cofilin binding changes structural and mechanical properties of actin filaments is well established. Less is known about the interaction energies and the thermodynamics of filament fragmentation, which describes the collective manner in which the cofilin concentration controls mean actin filament length. Here, we provide a general thermodynamic framework for allosteric regulation of self-assembly, and we use the theory to predict the interaction energies of experimental actin filament length distributions over a broad range of cofilin binding densities and for multiple cofilactin variants. We find that bound cofilin induces changes in nearby actin-actin interactions, and that these allosteric effects are propagated along the filament to affect up to four neighboring cofilin-binding sites (i.e., beyond nearest-neighbor allostery). The model also predicts that cofilin differentially stabilizes and destabilizes longitudinal versus lateral actin-actin interactions, and that the magnitude, range, asymmetry, and even the sign of these interaction energies can be altered using different actin and cofilin mutational variants. These results demonstrate that the theoretical framework presented here can provide quantitative thermodynamic information governing cooperative protein binding and filament length regulation, thus revealing nanometer length-scale interactions from micron length-scale "wet-lab" measurements.

4D electron microscopy of T cell activation.

T cells can be controllably stimulated through antigen-specific or nonspecific protocols. Accompanying functional hallmarks of T cell activation can include cytoskeletal reorganization, cell size increase, and cytokine secretion. Photon-induced near-field electron microscopy (PINEM) is used to image and quantify evanescent electric fields at the surface of T cells as a function of various stimulation conditions. While PINEM signal strength scales with multiple of the biophysical changes associated with T cell functional activation, it mostly strongly correlates with antigen-engagement of the T cell receptors, even under conditions that do not lead to functional T cell activation. PINEM image analysis suggests that a stimulation-induced reorganization of T cell surface structure, especially over length scales of a few hundred nanometers, is the dominant contributor to these PINEM signal changes. These experiments reveal that PINEM can provide a sensitive label-free probe of nanoscale cellular surface structures.

Tau local structure shields an amyloid-forming motif and controls aggregation propensity.

Tauopathies are neurodegenerative diseases characterized by intracellular amyloid deposits of tau protein. Missense mutations in the tau gene (MAPT) correlate with aggregation propensity and cause dominantly inherited tauopathies, but their biophysical mechanism driving amyloid formation is poorly understood. Many disease-associated mutations localize within tau's repeat domain at inter-repeat interfaces proximal to amyloidogenic sequences, such as 306VQIVYK311. We use cross-linking mass spectrometry, recombinant protein and synthetic peptide systems, in silico modeling, and cell models to conclude that the aggregation-prone 306VQIVYK311 motif forms metastable compact structures with its upstream sequence that modulates aggregation propensity. We report that disease-associated mutations, isomerization of a critical proline, or alternative splicing are all sufficient to destabilize this local structure and trigger spontaneous aggregation. These findings provide a biophysical framework to explain the basis of early conformational changes that may underlie genetic and sporadic tau pathogenesis.

Timing Correlations in Proteins Predict Functional Modules and Dynamic Allostery.

How protein structure encodes functionality is not fully understood. For example, long-range intraprotein communication can occur without measurable conformational change and is often not captured by existing structural correlation functions. It is shown here that important functional information is encoded in the timing of protein motions, rather than motion itself. I introduce the conditional activity function to quantify such timing correlations among the degrees of freedom within proteins. For three proteins, the conditional activities between side-chain dihedral angles were computed using the output of microseconds-long atomistic simulations. The new approach demonstrates that a sparse fraction of side-chain pairs are dynamically correlated over long distances (spanning protein lengths up to 7 nm), in sharp contrast to structural correlations, which are short-ranged (<1 nm). Regions of high self- and inter-side-chain dynamical correlations are found, corresponding to experimentally determined functional modules and allosteric connections, respectively.

eQuIPS: eQTL Analysis Using Informed Partitioning of SNPs - A Fully Bayesian Approach.

We develop a Bayesian multi-SNP Markov chain Monte Carlo approach that allows published functional significance scores to objectively inform single nucleotide polymorphism (SNP) prior effect sizes in expression quantitative trait locus (eQTL) studies. We developed the Normal Gamma prior to allow the inclusion of functional information. We partition SNPs into predefined functional groups and select prior distributions that fit the group-specific observed functional significance scores. We test our method on two simulated datasets and previously analysed human eQTL data containing validated causal SNPs. In our simulations the modified Normal Gamma always performs at least as well, and generally outperforms, the other methods considered. When analysing the human eQTL data, we placed all SNPs into their actual functional group. The ranks of the four validated causal SNPs analysed using the modified Normal Gamma increase dramatically compared to those of the other methods considered. Using our new method, three of the four validated SNPs are ranked in the top 1% of SNPs and the other is in the top 2%. For the standard Normal Gamma, the best of the other methods, the four validated SNPs had ranks in the top 1%, 4%, 20% and 59%. Crucially these substantive improvements in the ranks make it highly likely that most, if not all, of these validated SNPs would have been flagged for follow-up using our new method, whereas at least two of them would certainly not have been using the current approaches.

Cooperativity leads to temporally-correlated fluctuations in the bacteriophage lambda genetic switch.

Cooperative interactions are widespread in biochemical networks, providing the nonlinear response that underlies behavior such as ultrasensitivity and robust switching. We introduce a temporal correlation function-the conditional activity-to study the behavior of these phenomena. Applying it to the bistable genetic switch in bacteriophage lambda, we find that cooperative binding between binding sites on the prophage DNA lead to non-Markovian behavior, as quantified by the conditional activity. Previously, the conditional activity has been used to predict allosteric pathways in proteins; here, we show that it identifies the rare unbinding events which underlie induction from lysogeny to lysis.

Dominance of misfolded intermediates in the dynamics of α-helix folding.

Helices are the "hydrogen atoms" of biomolecular complexity; the DNA/RNA double hairpin and protein α-helix ubiquitously form the building blocks of life's constituents at the nanometer scale. Nevertheless, the formation processes of these structures, especially the dynamical pathways and rates, remain challenging to predict and control. Here, we present a general analytical method for constructing dynamical free-energy landscapes of helices. Such landscapes contain information about the thermodynamic stabilities of the possible macromolecular conformations, as well as about the dynamic connectivity, thus enabling the visualization and computation of folding pathways and timescales. We elucidate the methodology using the folding of polyalanine, and demonstrate that its α-helix folding kinetics is dominated by misfolded intermediates. At the physiological temperature of T = 298 K and midfolding time t = 250 ns, the fraction of structures in the native-state (α-helical) basin equals 22%, which is in good agreement with time-resolved experiments and massively distributed, ensemble-convergent molecular-dynamics simulations. We discuss the prominent role of ß-strand-like intermediates in flight toward the native fold, and in relation to the primary conformational change precipitating aggregation in some neurodegenerative diseases.

Metformin improves endothelial function in type 1 diabetic subjects: a pilot, placebo-controlled randomized study.

AIMS: Several studies have investigated the effects of metformin treatment in patients with type 1 diabetes mellitus (T1DM). No study has hitherto examined its effects on endothelial function in these patients. In this study we sought to evaluate the effect of metformin on endothelial function in type 1 diabetic patients. METHODS: Forty-two uncomplicated T1DM patients were randomized in a placebo-controlled, double-blind, 6-month trial to treatment with either metformin or placebo. Glycometabolic and clinical parameters as well as flow-mediated dilation (FMD) and nitrate-mediated dilation (NMD) of the right brachial artery were measured at baseline and at the end of the study. Glycaemic variability (GV, calculated from continuous glucose monitoring data) and a biomarker of oxidative stress [urinary 8-iso-prostaglandin F2α (PGF2α)] were also assessed. RESULTS: Baseline data were similar in the two groups. Compared with placebo, metformin significantly reduced body weight [-2.27 kg (95% confidence interval: -3.99; -0.54); p = 0.012] whilst improved FMD [1.32% (0.30; 2.43); p = 0.013] and increased PGF2α [149 pg/mg creatinine (50; 248); p = 0.004]. Notably, the improvement of FMD did not correlate with the decrease of body weight (r(2) < 1%). NMD, haemoglobin A1c, GV, daily insulin dose and other parameters did not significantly change after the treatment comparing the two groups. CONCLUSIONS: Our pilot trial showed that, in uncomplicated type 1 diabetic subjects, metformin improved FMD and increased PGF2α, a marker of oxidative stress, irrespective of its effects on glycaemic control and body weight. Randomized, blinded clinical trials are needed to evaluate the benefits and risks of metformin added to insulin in type 1 diabetes.

Hydrophobic forces and the length limit of foldable protein domains.

To find the native conformation (fold), proteins sample a subspace that is typically hundreds of orders of magnitude smaller than their full conformational space. Whether such fast folding is intrinsic or the result of natural selection, and what is the longest foldable protein, are open questions. Here, we derive the average conformational degeneracy of a lattice polypeptide chain in water and quantitatively show that the constraints associated with hydrophobic forces are themselves sufficient to reduce the effective conformational space to a size compatible with the folding of proteins up to approximately 200 amino acids long within a biologically reasonable amount of time. This size range is in general agreement with the experimental protein domain length distribution obtained from approximately 1,200 proteins. Molecular dynamics simulations of the Trp-cage protein confirm this picture on the free energy landscape. Our analytical and computational results are consistent with a model in which the length and time scales of protein folding, as well as the modular nature of large proteins, are dictated primarily by inherent physical forces, whereas natural selection determines the native state.