Coarse-grained Sampling Research Articles

Reverse Mapping of Coarse Grained Polyglutamine Conformations from PRIME20 Sampling.

An inverse coarse-graining protocol is presented for generating and validating atomistic structures of large (bio-) molecules from conformations obtained via a coarse-grained sampling method. Specifically, the protocol is implemented and tested based on the (coarse-grained) PRIME20 protein model (P20/SAMC), and the resulting all-atom conformations are simulated using conventional biomolecular force fields. The phase space sampling at the coarse-grained level is performed with a stochastical approximation Monte Carlo approach. The method is applied to a series of polypeptides, specifically dimers of polyglutamine with varying chain length in aqueous solution. The majority (>70 %) of the conformations obtained from the coarse-grained peptide model can successfully be mapped back to atomistic structures that remain conformationally stable during 10 ns of molecular dynamics simulations. This work can be seen as the first step towards the overarching goal of improving our understanding of protein aggregation phenomena through simulation methods.

Synchronizing DDoS detection and mitigation based graph learning with programmable data plane, SDN

The availability of SD-IoT is now under complex and serious cyber threats, especially distributed denial-of-service attacks. However, traditional defense schemes suffer from coarse-grained centralized sampling approaches, low accuracy of detection models, and inefficient mitigation methods. In this paper, a novel DDoS defense scheme is proposed, which consists of a high-accuracy detection mechanism based on a Graph Convolutional Neural Network learning model and a mitigation mechanism based on fast traffic migration. In the detection stage, a fine-grained INT sampling approach is utilized to obtain multidimensional network topology and status information. The Graph Convolutional Neural Network learning model detects switches containing DDoS attack traffic with high accuracy because the detection model not only extracts and utilizes multiple temporal and spatial features of the collected information, but also has a better learning and representation capability. In the mitigation stage, the enhanced whitelist with dynamic threshold-based values is automatically adapted to the real-time state of the network environment for enhanced mitigation flexibility. The fast programmable segment rerouting strategy can block attack traffic in time and ensure the continuity of network services. The results of several comparison experiments show that the proposed scheme can detect DDoS attacks more accurately and mitigate them more effectively than traditional schemes.

Molecular dynamics study on micelle-small molecule interactions: developing a strategy for an extensive comparison

Theoretical predictions of the solubilizing capacity of micelles and vesicles present in intestinal fluid are important for the development of new delivery techniques and bioavailability improvement. A balance between accuracy and computational cost is a key factor for an extensive study of numerous compounds in diverse environments. In this study, we aimed to determine an optimal molecular dynamics (MD) protocol to evaluate small-molecule interactions with micelles composed of bile salts and phospholipids. MD simulations were used to produce free energy profiles for three drug molecules (danazol, probucol, and prednisolone) and one surfactant molecule (sodium caprate) as a function of the distance from the colloid center of mass. To address the challenges associated with such tasks, we compared different simulation setups, including freely assembled colloids versus pre-organized spherical micelles, full free energy profiles versus only a few points of interest, and a coarse-grained model versus an all-atom model. Our findings demonstrate that combining these techniques is advantageous for achieving optimal performance and accuracy when evaluating the solubilization capacity of micelles.Graphical abstractAll-atom (AA) and coarse-grained (CG) umbrella sampling (US) simulations and point-wise free energy (FE) calculations were compared to their efficiency to computationally analyze the solubilization of active pharmaceutical ingredients in intestinal fluid colloids.

A computational study for understanding the impact of p120-catenin on the cis-dimerization of cadherin.

A prototype of cross-membrane signal transduction is that extracellular binding of cell surface receptors to their ligands induces intracellular signalling cascades. However, much less is known about the process in the opposite direction, called inside-out signalling. Recent studies show that it plays a more important role in regulating the functions of many cell surface receptors than we used to think. In particular, in cadherin-mediated cell adhesion, recent experiments indicate that intracellular binding of the scaffold protein p120-catenin (p120ctn) can promote extracellular clustering of cadherin and alter its adhesive function. The underlying mechanism, however, is not well understood. To explore possible mechanisms, we designed a new multiscale simulation procedure. Using all-atom molecular dynamics simulations, we found that the conformational dynamics of the cadherin extracellular region can be altered by the intracellular binding of p120ctn. More intriguingly, by integrating all-atom simulation results into coarse-grained random sampling, we showed that the altered conformational dynamics of cadherin caused by the binding of p120ctn can increase the probability of lateral interactions between cadherins on the cell surface. These results suggest that p120ctn could allosterically regulate the cis-dimerization of cadherin through two mechanisms. First, p120ctn controls the extracellular conformational dynamics of cadherin. Second, p120ctn oligomerization can further promote cadherin clustering. Therefore, our study provides a mechanistic foundation for the inside-out signalling in cadherin-mediated cell adhesion, while the computational framework can be generally applied to other cross-membrane signal transduction systems.

Improved accuracy and spatial resolution for bio-logging-derived chlorophyll a fluorescence measurements in the Southern Ocean

The ocean’s meso- and submeso-scales (1-100 km, days to weeks) host features like filaments and eddies that have a key structuring effect on phytoplankton distribution, but that due to their ephemeral nature, are challenging to observe. This problem is exacerbated in regions with heavy cloud coverage and/or difficult access like the Southern Ocean, where observations of phytoplankton distribution by satellite are sparse, manned campaigns costly, and automated devices limited by power consumption. Here, we address this issue by considering high-resolution in-situ data from 18 bio-logging devices deployed on southern elephant seals (Mirounga leonina) in the Kerguelen Islands between 2018 and 2020. These devices have submesoscale-resolving capabilities of light profiles due to the high spatio-temporal frequency of the animals’ dives (on average 1.1 +-0.6 km between consecutive dives, up to 60 dives per day), but observations of fluorescence are much coarser due to power constraints. Furthermore, the chlorophyll a concentrations derived from the (uncalibrated) bio-logging devices’ fluorescence sensors lack a common benchmark to properly qualify the data and allow comparisons of observations. By proposing a method based on functional data analysis, we show that a reliable predictor of chlorophyll a concentration can be constructed from light profiles (14 686 in our study). The combined use of light profiles and matchups with satellite ocean-color data enable effective (1) homogenization then calibration of the bio-logging devices’ fluorescence data and (2) filling of the spatial gaps in coarse-grained fluorescence sampling. The developed method improves the spatial resolution of the chlorophyll a field description from ~30 km to ~12 km. These results open the way to empirical study of the coupling between physical forcing and biological response at submesoscale in the Southern Ocean, especially useful in the context of upcoming high-resolution ocean-circulation satellite missions.

Ensuring thermodynamic consistency with invertible coarse-graining.

Coarse-grained models are a core computational tool in theoretical chemistry and biophysics. A judicious choice of a coarse-grained model can yield physical insights by isolating the essential degrees of freedom that dictate the thermodynamic properties of a complex, condensed-phase system. The reduced complexity of the model typically leads to lower computational costs and more efficient sampling compared with atomistic models. Designing "good" coarse-grained models is an art. Generally, the mapping from fine-grained configurations to coarse-grained configurations itself is not optimized in any way; instead, the energy function associated with the mapped configurations is. In this work, we explore the consequences of optimizing the coarse-grained representation alongside its potential energy function. We use a graph machine learning framework to embed atomic configurations into a low-dimensional space to produce efficient representations of the original molecular system. Because the representation we obtain is no longer directly interpretable as a real-space representation of the atomic coordinates, we also introduce an inversion process and an associated thermodynamic consistency relation that allows us to rigorously sample fine-grained configurations conditioned on the coarse-grained sampling. We show that this technique is robust, recovering the first two moments of the distribution of several observables in proteins such as chignolin and alanine dipeptide.

Generating a conformational landscape of ubiquitin chains at atomistic resolution by back-mapping based sampling.

Ubiquitin chains are flexible multidomain proteins that have important biological functions in cellular signalling. Computational studies with all-atom molecular dynamics simulations of the conformational spaces of polyubiquitins can be challenging due to the system size and a multitude of long-lived meta-stable states. Coarse graining is an efficient approach to overcome this problem-at the cost of losing high-resolution details. Recently, we proposed the back-mapping based sampling (BMBS) approach that reintroduces atomistic information into a given coarse grained (CG) sampling based on a two-dimensional (2D) projection of the conformational landscape, produces an atomistic ensemble and allows to systematically compare the ensembles at the two levels of resolution. Here, we apply BMBS to K48-linked tri-ubiquitin, showing its applicability to larger systems than those it was originally introduced on and demonstrating that the algorithm scales very well with system size. In an extension of the original BMBS we test three different seeding strategies, i.e. different approaches from where in the CG landscape atomistic trajectories are initiated. Furthermore, we apply a recently introduced conformational clustering algorithm to the back-mapped atomistic ensemble. Thus, we obtain insight into the structural composition of the 2D landscape and illustrate that the dimensionality reduction algorithm separates different conformational characteristics very well into different regions of the map. This cluster analysis allows us to show how atomistic trajectories sample conformational states, move through the projection space and in sum converge to an atomistic conformational landscape that slightly differs from the original CG map, indicating a correction of flaws in the CG template.

CachePerf

The cache plays a key role in determining the performance of applications, no matter for sequential or concurrent programs on homogeneous and heterogeneous architecture. Fixing cache misses requires to understand the origin and the type of cache misses. However, this remains to be an unresolved issue even after decades of research. This paper proposes a unified profiling tool--CachePerf--that could correctly identify different types of cache misses, differentiate allocator-induced issues from those of applications, and exclude minor issues without much performance impact. The core idea behind CachePerf is a hybrid sampling scheme: it employs the PMU-based coarse-grained sampling to select very few susceptible instructions (with frequent cache misses) and then employs the breakpoint-based fine-grained sampling to collect the memory access pattern of these instructions. Based on our evaluation, CachePerf only imposes 14% performance overhead and 19% memory overhead (for applications with large footprints), while identifying the types of cache misses correctly. CachePerf detected 9 previous-unknown bugs. Fixing the reported bugs achieves from 3% to 3788% performance speedup. CachePerf will be an indispensable complementary to existing profilers due to its effectiveness and low overhead.

Antibody–nanobody combination increases their neutralizing activity against SARS-CoV-2 and nanobody H11-H4 is effective against Alpha, Kappa and Delta variants

The global spread of COVID-19 is devastating health systems and economies worldwide. While the use of vaccines has yielded encouraging results, the emergence of new variants of SARS-CoV-2 shows that combating COVID-19 remains a big challenge. One of the most promising treatments is the use of not only antibodies, but also nanobodies. Recent experimental studies revealed that the combination of antibody and nanobody can significantly improve their neutralizing ability through binding to the SARS-CoV-2 spike protein, but the molecular mechanisms underlying this observation remain largely unknown. In this work, we investigated the binding affinity of the CR3022 antibody and H11-H4 nanobody to the SARS-CoV-2 receptor binding domain (RBD) using molecular modeling. Both all-atom steered molecular dynamics simulations and coarse-grained umbrella sampling showed that, consistent with the experiment, CR3022 associates with RBD more strongly than H11-H4. We predict that the combination of CR3022 and H11-H4 considerably increases their binding affinity to the spike protein. The electrostatic interaction was found to control the association strength of CR3022, but the van der Waals interaction dominates in the case of H11-H4. However, our study for a larger set of nanobodies and antibodies showed that the relative role of these interactions depends on the specific complex. Importantly, we showed Beta, Gamma, Lambda, and Mu variants reduce the H11-H4 activity while Alpha, Kappa and Delta variants increase its neutralizing ability, which is in line with experiment reporting that the nanobody elicited from the llama is very promising for fighting against the Delta variant.

Cocktail of REGN Antibodies Binds More Strongly to SARS-CoV-2 Than Its Components, but the Omicron Variant Reduces Its Neutralizing Ability.

A promising approach to combat Covid-19 infections is the development of effective antiviral antibodies that target the SARS-CoV-2 spike protein. Understanding the structures and molecular mechanisms underlying the binding of antibodies to SARS-CoV-2 can contribute to quickly achieving this goal. Recently, a cocktail of REGN10987 and REGN10933 antibodies was shown to be an excellent candidate for the treatment of Covid-19. Here, using all-atom steered molecular dynamics and coarse-grained umbrella sampling, we examine the interactions of the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein with REGN10987 and REGN10933 separately as well as together. Both computational methods show that REGN10933 binds to RBD more strongly than REGN10987. Importantly, the cocktail binds to RBD (simultaneous binding) more strongly than its components. The dissociation constants of REGN10987-RBD and REGN10933-RBD complexes calculated from the coarse-grained simulations are in good agreement with the experimental data. Thus, REGN10933 is probably a better candidate for treating Covid-19 than REGN10987, although the cocktail appears to neutralize the virus more efficiently than REGN10933 or REGN10987 alone. The association of REGN10987 with RBD is driven by van der Waals interactions, while electrostatic interactions dominate in the case of REGN10933 and the cocktail. We also studied the effectiveness of these antibodies on the two most dangerous variants Delta and Omicron. Consistent with recent experimental reports, our results confirmed that the Omicron variant reduces the neutralizing activity of REGN10933, REGN10987, and REGN10933+REGN10987 with the K417N, N440K, L484A, and Q498R mutations playing a decisive role, while the Delta variant slightly changes their activity.

A quantum hash function with grouped coarse-grained boson sampling

A quantum hash function with grouped coarse-grained boson sampling

Extending the Martini Coarse-Grained Force Field to N-Glycans.

Glycans play a vital role in a large number of cellular processes. Their complex and flexible nature hampers structure-function studies using experimental techniques. Molecular dynamics (MD) simulations can help in understanding dynamic aspects of glycans if the force field parameters used can reproduce key experimentally observed properties. Here, we present optimized coarse-grained (CG) Martini force field parameters for N-glycans, calibrated against experimentally derived binding affinities for lectins. The CG bonded parameters were obtained from atomistic (ATM) simulations for different glycan topologies including high mannose and complex glycans with various branching patterns. In the CG model, additional elastic networks are shown to improve maintenance of the overall conformational distribution. Solvation free energies and octanol-water partition coefficients were also calculated for various N-glycan disaccharide combinations. When using standard Martini nonbonded parameters, we observed that glycans spontaneously aggregated in the solution and required down-scaling of their interactions for reproduction of ATM model radial distribution functions. We also optimized the nonbonded interactions for glycans interacting with seven lectin candidates and show that a relatively modest scaling down of the glycan-protein interactions can reproduce free energies obtained from experimental studies. These parameters should be of use in studying the role of glycans in various glycoproteins and carbohydrate binding proteins as well as their complexes, while benefiting from the efficiency of CG sampling.

Isomerization kinetics of flexible molecules in the gas phase: Atomistic versus coarse-grained sampling.

Isomerization kinetics of molecules in the gas phase naturally falls on the microcanonical ensemble of statistical mechanics, which for small systems might significantly differ from the more traditional canonical ensemble. In this work, we explore the examples of cis-trans isomerization in butane and bibenzyl and to what extent the fully atomistic rate constants in isolated molecules can be reproduced by coarse-graining the system into a lower dimensional potential of mean force (PMF) along a reaction coordinate of interest, the orthogonal degrees of freedom acting as a canonical bath in a Langevin description. Time independent microcanonical rate constants can be properly defined from appropriate state residence time correlation functions; however, the resulting rate constants acquire some time dependence upon canonical averaging of initial conditions. Stationary rate constants are recovered once the molecule is placed into a real condensed environment pertaining to the canonical ensemble. The effective one-dimensional kinetics along the PMF, based on appropriately chosen inertia and damping parameters, quantitatively reproduces the atomistic rate constants at short times but deviates systematically over long times owing to the neglect of some couplings between the system and the bath that are all intrinsically present in the atomistic treatment. In bibenzyl, where stronger temperature effects are noted than in butane, the effective Langevin dynamics along the PMF still performs well at short times, indicating the potential interest of this extremely simplified approach for sampling high-dimensional energy surfaces and evaluating reaction rate constants.

Redundancy-free analysis of multi-revision software artifacts

Researchers often analyze several revisions of a software project to obtain historical data about its evolution. For example, they statically analyze the source code and monitor the evolution of certain metrics over multiple revisions. The time and resource requirements for running these analyses often make it necessary to limit the number of analyzed revisions, e.g., by only selecting major revisions or by using a coarse-grained sampling strategy, which could remove significant details of the evolution. Most existing analysis techniques are not designed for the analysis of multi-revision artifacts and they treat each revision individually. However, the actual difference between two subsequent revisions is typically very small. Thus, tools tailored for the analysis of multiple revisions should only analyze these differences, thereby preventing re-computation and storage of redundant data, improving scalability and enabling the study of a larger number of revisions. In this work, we propose the Lean Language-Independent Software Analyzer (LISA), a generic framework for representing and analyzing multi-revisioned software artifacts. It employs a redundancy-free, multi-revision representation for artifacts and avoids re-computation by only analyzing changed artifact fragments across thousands of revisions. The evaluation of our approach consists of measuring the effect of each individual technique incorporated, an in-depth study of LISA resource requirements and a large-scale analysis over 7 million program revisions of 4,000 software projects written in four languages. We show that the time and space requirements for multi-revision analyses can be reduced by multiple orders of magnitude, when compared to traditional, sequential approaches.

Native State of Complement Protein C3d Analysed via Hydrogen Exchange and Conformational Sampling.

Hydrogen/deuterium exchange detected by mass spectrometry (HDXMS) provides valuable information on protein structure and dynamics. Although HDX-MS data is often interpreted using crystal structures, it was suggested that conformational ensembles produced by molecular dynamics simulations yield more accurate interpretations. In this paper, we analyse the complement protein C3d by performing an HDX-MS experiment, and evaluate several interpretation methodologies using an existing prediction model to derive HDX-MS data from protein structure. To interpret and refine C3d's HDX-MS data, we look for a conformation (or conformational ensemble) of C3d that allows computationally replicating this data. We confirm that crystal structures are not a good choice and suggest that conformational ensembles produced by molecular dynamics simulations might not always be satisfactory either. Finally, we show that coarse-grained conformational sampling of C3d produces a conformation from which its HDX-MS data can be replicated and refined.

A polar SxxS motif drives assembly of the transmembrane domains of Toll-like receptor 4

Like all members of the Toll-like receptor (TLR) family, TLR4 comprises of a large ectodomain (ECD) involved in ligand recognition at the cell-surface, and a cytosolic Toll/interleukin-1 receptor (TIR) signalling domain, linked by a lipid membrane-anchored transmembrane (TM) domain (TMD). Binding of immunostimulatory pathogen-associated molecular patterns (PAMPs) such as bacterial lipopolysaccharide (LPS) to myeloid differentiation factor 2 (MD-2) coreceptor-complexed TLR4 leads to its dimerization, resulting in productive juxtaposition of TIR domains and the initiation of pro-inflammatory innate immune responses. Whilst the process of PAMP recognition is relatively well understood, thanks to numerous high-resolution crystallographic structures of ECDs, the mechanism by which such recognition is translated into TMD dimerization and activating conformational changes is less clear. Based on available biophysical and biochemical experimental data, ab initio modelling, and multiscale molecular dynamics (MD) simulations entailing a total of >13μs and >200μs of atomistic and coarse-grained sampling, respectively, we investigate the structural basis for TLR4 TMD dimerization within a biologically relevant lipid membrane environment. A key polar-xx-polar (637SxxS640) motif is shown to drive association of the TLR4 TMDs, and to maintain a flexible interface, which may be disrupted by selected point mutations. Furthermore, MD simulations of various TMD+ECD constructs have been used to investigate the coupling between domains, revealing that flexible linkers abrogate dimerization via aggregation of ECDs at the membrane surface, explaining previous biochemical observations. These results improve our understanding of the assembly and signalling mechanisms of TLR4, and pave the way for rational structure-based development of membrane-associated immunomodulatory molecules.

Coarse-Grained Conformational Sampling of Protein Structure Improves the Fit to Experimental Hydrogen-Exchange Data.

Monitoring hydrogen/deuterium exchange (HDX) undergone by a protein in solution produces experimental data that translates into valuable information about the protein's structure. Data produced by HDX experiments is often interpreted using a crystal structure of the protein, when available. However, it has been shown that the correspondence between experimental HDX data and crystal structures is often not satisfactory. This creates difficulties when trying to perform a structural analysis of the HDX data. In this paper, we evaluate several strategies to obtain a conformation providing a good fit to the experimental HDX data, which is a premise of an accurate structural analysis. We show that performing molecular dynamics simulations can be inadequate to obtain such conformations, and we propose a novel methodology involving a coarse-grained conformational sampling approach instead. By extensively exploring the intrinsic flexibility of a protein with this approach, we produce a conformational ensemble from which we extract a single conformation providing a good fit to the experimental HDX data. We successfully demonstrate the applicability of our method to four small and medium-sized proteins.

Coarse-Grained Models for Automated Fragmentation and Parametrization of Molecular Databases.

We calibrate coarse-grained interaction potentials suitable for screening large data sets in top-down fashion. Three new algorithms are introduced: (i) automated decomposition of molecules into coarse-grained units (fragmentation); (ii) Coarse-Grained Reference Interaction Site Model-Hypernetted Chain (CG RISM-HNC) as an intermediate proxy for dissipative particle dynamics (DPD); and (iii) a simple top-down coarse-grained interaction potential/model based on activity coefficient theories from engineering (using COSMO-RS). We find that the fragment distribution follows Zipf and Heaps scaling laws. The accuracy in Gibbs energy of mixing calculations is a few tenths of a kilocalorie per mole. As a final proof of principle, we use full coarse-grained sampling through DPD thermodynamics integration to calculate logPOW for 4627 compounds with an average error of 0.84 log unit. The computational speeds per calculation are a few seconds for CG RISM-HNC and a few minutes for DPD thermodynamic integration.

Double-layer capacitance for a charged surface

Supercapacitors are a key technology for the energy storage requirements of future energy systems. A primary problem of supercapacitors is their limited energy density, but new electrode materials and electrode designs might help to overcome this limitation. Numerical modelling can be a valuable tool in this challenge, although realistic ab initio calculations are usually very cumbersome. In this work, we show that electric double-layer capacitors can be modelled to good accuracy by a coarse-grained sampling of the electrolyte’s configuration space rather than using full molecular dynamics simulations. This saves considerable computation time, and it allows for processing more materials and more complicated systems with modest computational effort.

Sample and Fetch-Based Large Flow Detection Mechanism in Software Defined Networks

Detecting large flows in a software-defined network accurately is important for many applications. However, due to the constraints of measurement resources such as TCAMs, the existing solutions often suffer from feasibility and accuracy issues. In this letter, by combining the advantages of sampling and flow counting, we propose FlowMon, a sample and fetch-based two-stage large flow detection mechanism. FlowMon first captures the suspicious large flows through coarse-grained sampling method, and then notifies the SDN controller to determine the true large flows from these suspicious ones by installing measurement rules in the specific OpenFlow switches. To optimize the TCAM resource allocation, we also design a dynamic flow entry assignment model. Experiment results show that FlowMon can improve the large flow detection accuracy, decrease the TCAM resource consumption, and balance the measurement load among OpenFlow switches.