SWINGER: a clustering algorithm for concurrent coupling of atomistic and supramolecular liquids.
Ontology highlight
ABSTRACT:
SUBMITTER: Zavadlav J
PROVIDER: S-EPMC6501350 | biostudies-literature | 2019 Jun
REPOSITORIES: biostudies-literature
Publications
SWINGER: a clustering algorithm for concurrent coupling of atomistic and supramolecular liquids.
Zavadlav Julija J Marrink Siewert J SJ Praprotnik Matej M
Interface focus 20190419 3
In this contribution, we review recent developments and applications of a dynamic clustering algorithm SWINGER tailored for the multiscale molecular simulations of biomolecular systems. The algorithm on-the-fly redistributes solvent molecules among supramolecular clusters. In particular, we focus on its applications in combination with the adaptive resolution scheme, which concurrently couples atomistic and coarse-grained molecular representations. We showcase the versatility of our multiscale a ...[more]
Similar Datasets
Project description:We develop an efficient parallel multiscale method that bridges the atomistic and mesoscale regimes, from nanometers to microns and beyond, via concurrent coupling of atomistic simulation and mesoscopic dynamics. In particular, we combine an all-atom molecular dynamics (MD) description for specific atomistic details in the vicinity of the functional surface with a dissipative particle dynamics (DPD) approach that captures mesoscopic hydrodynamics in the domain away from the functional surface. In order to achieve a seamless transition in dynamic properties we endow the MD simulation with a DPD thermostat, which is validated against experimental results by modeling water at different temperatures. We then validate the MD-DPD coupling method for transient Couette and Poiseuille flows, demonstrating that the concurrent MD-DPD coupling can resolve accurately the continuum-based analytical solutions. Subsequently, we simulate shear flows over grafted polydimethylsiloxane (PDMS) surfaces (polymer brushes) for various grafting densities, and investigate the slip flow as a function of the shear stress. We verify that a "universal" power law exists for the slip length, in agreement with published results. Having validated the MD-DPD coupling method, we simulate time-dependent flows past an endothelial glycocalyx layer (EGL) in a microchannel. Coupled simulation results elucidate the dynamics of the EGL changing from an equilibrium state to a compressed state under shear by aligning the molecular structures along the shear direction. MD-DPD simulation results agree well with results of a single MD simulation, but with the former more than two orders of magnitude faster than the latter for system sizes above one micron.
| S-EPMC6414210 | biostudies-literature
Project description:Brains perform complex tasks using a fraction of the power that would be required to do the same on a conventional computer. New neuromorphic hardware systems are now becoming widely available that are intended to emulate the more power efficient, highly parallel operation of brains. However, to use these systems in applications, we need "neuromorphic algorithms" that can run on them. Here we develop a spiking neural network model for neuromorphic hardware that uses spike timing-dependent plasticity and lateral inhibition to perform unsupervised clustering. With this model, time-invariant, rate-coded datasets can be mapped into a feature space with a specified resolution, i.e., number of clusters, using exclusively neuromorphic hardware. We developed and tested implementations on the SpiNNaker neuromorphic system and on GPUs using the GeNN framework. We show that our neuromorphic clustering algorithm achieves results comparable to those of conventional clustering algorithms such as self-organizing maps, neural gas or k-means clustering. We then combine it with a previously reported supervised neuromorphic classifier network to demonstrate its practical use as a neuromorphic preprocessing module.
| S-EPMC6658584 | biostudies-literature
Project description:The accumulation of high-throughput data from different experiments has facilitated the extraction of condition-specific networks over the same set of biological entities. Comparing and contrasting of such multiple biological networks is in the center of differential network biology, aiming at determining general and condition-specific responses captured in the network structure (i.e., included associations between the network components). We provide a novel way for comparison of multiple networks based on determining network clustering (i.e., partition into communities) which is optimal across the set of networks with respect to a given cluster quality measure. To this end, we formulate the optimization-based problem of concurrent conditional clustering of multiple networks, termed COCONETS, based on the modularity. The solution to this problem is a clustering which depends on all considered networks and pinpoints their preserved substructures. We present theoretical results for special classes of networks to demonstrate the implications of conditionality captured by the COCONETS formulation. As the problem can be shown to be intractable, we extend an existing efficient greedy heuristic and applied it to determine concurrent conditional clusters on coexpression networks extracted from publically available time-resolved transcriptomics data of Escherichia coli under five stresses as well as on metabolite correlation networks from metabolomics data set from Arabidopsis thaliana exposed to eight environmental conditions. We demonstrate that the investigation of the differences between the clustering based on all networks with that obtained from a subset of networks can be used to quantify the specificity of biological responses. While a comparison of the Escherichia coli coexpression networks based on seminal properties does not pinpoint biologically relevant differences, the common network substructures extracted by COCONETS are supported by existing experimental evidence. Therefore, the comparison of multiple networks based on concurrent conditional clustering offers a novel venue for detection and investigation of preserved network substructures.
| S-EPMC4126743 | biostudies-literature
Project description:"Unlike charges attract, but like charges repel". This conventional wisdom has been recently challenged for ionic liquids. It could be shown that like-charged ions attract each other despite the powerful opposing electrostatic forces. In principle, cooperative hydrogen bonding between ions of like-charge can overcome the repulsive Coulomb interaction while pushing the limits of chemical bonding. The key challenge of this solvation phenomenon is to establish design principles for the efficient formation of clusters of like-charged ions in ionic liquids. This is realised here for a set of well-suited ionic liquids including the same hydrophobic anion but different cations all equipped with hydroxyethyl groups for possible H-bonding. The formation of H-bonded cationic clusters can be controlled by the delocalization of the positive charge on the cations. Strongly localized charge results in cation-anion interaction, delocalized charge leads to the formation of cationic clusters. For the first time we can show, that the cationic clusters influence the properties of ILs. ILs comprising these clusters can be supercooled and form glasses. Crystalline structures are obtained only, if the ILs are dominantly characterized by the attraction between opposite-charged ions resulting in conventional ion pairs. That may open a new path for controlling glass formation and crystallization. The glass temperatures and the phase transitions of the ILs are observed by differential scanning calorimetry (DSC) and infrared (IR) spectroscopy.
| S-EPMC6170405 | biostudies-other
Project description:Guanosine and related derivatives self-assemble in the presence of cations like potassium into supramolecular G-quadruplexes (SGQs), where four guanine moieties form planar tetrads (T) that coaxially stack into columnar aggregates with broad size distributions. However, SGQs made from 8-aryl-2'-deoxyguanosine derivatives (8ArGs), form mostly octamers, or two-tetrad (2T)-SGQs, while some form dodecamers (3T-SGQs), or hexadecamers (4T-SGQs), and none reported to date form higher assemblies. A theoretical model that addresses the configurational space available for the multiple pathways available for 8ArGs to self-assemble into SGQs is used to frame a series of molecular dynamics simulations (MDS) with selected SGQs. Some key insights from this work include: (a) The predicted entropic costs are not significantly higher for SGQs with more subunits due to their hierarchical assembly pathways; (b) The multiple isomeric SGQs vary in the interfacial contacts between consecutive tetrads, due to their two distinct sides (head, h; tail, t), with the MDS supporting the predicted order of stability of hh > ht > tt for octamers. (c) Such order also applies to dodecamers and hexadecamers, but with context-dependent exceptions due to strong allosteric effects. (d) The main factor disfavoring the tt interface is the repulsive dipolar interactions between the O4' from ribose moieties on adjacent tetrads. (e) SGQs with 5 or more tetrads are disfavored because the attractive interactions are not large or strong enough to overcome the many repulsive forces resulting from the addition of further tetrads. We expect these findings provide some guidelines to enable the further development of SGQs into functional materials.
| S-EPMC5649357 | biostudies-literature
Project description:In competitive team sports, players maintain a certain formation during a game to achieve effective attacks and defenses. For the quantitative game analysis and assessment of team styles, we need a general framework that can characterize such formation structures dynamically. This paper develops a clustering algorithm for formations of multiple football (soccer) games based on the Delaunay method, which defines the formation of a team as an adjacency matrix of Delaunay triangulation. We first show that heat maps of entire football games can be clustered into several average formations: "442", "4141", "433", "541", and "343". Then, using hierarchical clustering, each average formation is further divided into more specific patterns (clusters) in which the configurations of players are different. Our method enables the visualization, quantitative comparison, and time-series analysis for formations in different time scales by focusing on transitions between clusters at each hierarchy. In particular, we can extract team styles from multiple games regarding the positional exchange of players within the formations. Applying our algorithm to the datasets comprising football games, we extract typical transition patterns of the formation for a particular team.
| S-EPMC6739367 | biostudies-literature
Project description:BackgroundWe present the algorithm PFClust (Parameter Free Clustering), which is able automatically to cluster data and identify a suitable number of clusters to group them into without requiring any parameters to be specified by the user. The algorithm partitions a dataset into a number of clusters that share some common attributes, such as their minimum expectation value and variance of intra-cluster similarity. A set of n objects can be clustered into any number of clusters from one to n, and there are many different hierarchical and partitional, agglomerative and divisive, clustering methodologies available that can be used to do this. Nonetheless, automatically determining the number of clusters present in a dataset constitutes a significant challenge for clustering algorithms. Identifying a putative optimum number of clusters to group the objects into involves computing and evaluating a range of clusterings with different numbers of clusters. However, there is no agreed or unique definition of optimum in this context. Thus, we test PFClust on datasets for which an external gold standard of 'correct' cluster definitions exists, noting that this division into clusters may be suboptimal according to other reasonable criteria. PFClust is heuristic in the sense that it cannot be described in terms of optimising any single simply-expressed metric over the space of possible clusterings.ResultsWe validate PFClust firstly with reference to a number of synthetic datasets consisting of 2D vectors, showing that its clustering performance is at least equal to that of six other leading methodologies - even though five of the other methods are told in advance how many clusters to use. We also demonstrate the ability of PFClust to classify the three dimensional structures of protein domains, using a set of folds taken from the structural bioinformatics database CATH.ConclusionsWe show that PFClust is able to cluster the test datasets a little better, on average, than any of the other algorithms, and furthermore is able to do this without the need to specify any external parameters. Results on the synthetic datasets demonstrate that PFClust generates meaningful clusters, while our algorithm also shows excellent agreement with the correct assignments for a dataset extracted from the CATH part-manually curated classification of protein domain structures.
| S-EPMC3747858 | biostudies-literature
Project description:This article presents the data used to evaluate the performance of evolutionary clustering algorithm star (ECA*) compared to five traditional and modern clustering algorithms. Two experimental methods are employed to examine the performance of ECA* against genetic algorithm for clustering++ (GENCLUST++), learning vector quantisation (LVQ), expectation maximisation (EM), K-means++ (KM++) and K-means (KM). These algorithms are applied to 32 heterogenous and multi-featured datasets to determine which one performs well on the three tests. For one, ther paper examines the efficiency of ECA* in contradiction of its corresponding algorithms using clustering evaluation measures. These validation criteria are objective function and cluster quality measures. For another, it suggests a performance rating framework to measurethe the performance sensitivity of these algorithms on varos dataset features (cluster dimensionality, number of clusters, cluster overlap, cluster shape and cluster structure). The contributions of these experiments are two-folds: (i) ECA* exceeds its counterpart aloriths in ability to find out the right cluster number; (ii) ECA* is less sensitive towards dataset features compared to its competitive techniques. Nonetheless, the results of the experiments performed demonstrate some limitations in the ECA*: (i) ECA* is not fully applied based on the premise that no prior knowledge exists; (ii) Adapting and utilising ECA* on several real applications has not been achieved yet.
| S-EPMC8086011 | biostudies-literature
Project description:It was demonstrated that the ?-valerolactone-based ionic liquid, tetrabutylphosphonium 4-ethoxyvalerate as a partially bio-based solvent can be utilized as alternative reaction medium for copper- and auxiliary base-free Pd-catalyzed Sonogashira coupling reactions of aryl iodides and functionalized acetylenes under mild conditions. Twenty-two cross-coupling products were isolated with good to excellent yields (72-99%) and purity (>98%). These results represent an example which proves that biomass-derived safer solvents can be utilized efficiently in common, industrially important transformations exhibiting higher chemical and environmental efficiency.
| S-EPMC6902853 | biostudies-literature
Project description:Microbial network inference and analysis have become successful approaches to extract biological hypotheses from microbial sequencing data. Network clustering is a crucial step in this analysis. Here, we present a novel heuristic network clustering algorithm, manta, which clusters nodes in weighted networks. In contrast to existing algorithms, manta exploits negative edges while differentiating between weak and strong cluster assignments. For this reason, manta can tackle gradients and is able to avoid clustering problematic nodes. In addition, manta assesses the robustness of cluster assignment, which makes it more robust to noisy data than most existing tools. On noise-free synthetic data, manta equals or outperforms existing algorithms, while it identifies biologically relevant subcompositions in real-world data sets. On a cheese rind data set, manta identifies groups of taxa that correspond to intermediate moisture content in the rinds, while on an ocean data set, the algorithm identifies a cluster of organisms that were reduced in abundance during a transition period but did not correlate strongly to biochemical parameters that changed during the transition period. These case studies demonstrate the power of manta as a tool that identifies biologically informative groups within microbial networks.IMPORTANCE manta comes with unique strengths, such as the abilities to identify nodes that represent an intermediate between clusters, to exploit negative edges, and to assess the robustness of cluster membership. manta does not require parameter tuning, is straightforward to install and run, and can be easily combined with existing microbial network inference tools.
| S-EPMC7029223 | biostudies-literature