Browse
Submit Data
Databases
API
Help

Dataset Information

11 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

SAS profile correlations reveal SAS hierarchical nature and information content.

ABSTRACT: In structural biology, Small-Angle Scattering experiments (SAS) are unique, because although they provide low resolution data, they can be performed in closer-to-native conditions than those arising in X-Ray crystallography. A number of questions on SAS, however, remain unsolved, particularly in the light of modelling ensembles of conformers in solution. In this article, we study the ensemble average and covariance of SAS profiles analytically. Using this ensemble covariance, we demonstrate the hierarchical nature of SAS profiles. Furthermore, we show that the information content is not uniform and reaches its maximum in the intermediate q range. The arguments are generalized using microsecond-scale molecular dynamics trajectories of the lysozyme and on an ensemble of the intrinsically disordered protein p15PAF. We show that for highly flexible systems, the SAS profile is a representation of the ensemble of conformers in solution, and not that of one conformer in particular.

SUBMITTER: Spill YG

PROVIDER: S-EPMC5426712 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

SAS profile correlations reveal SAS hierarchical nature and information content.

Spill Yannick G YG Nilges Michael M

PloS one 20170511 5

In structural biology, Small-Angle Scattering experiments (SAS) are unique, because although they provide low resolution data, they can be performed in closer-to-native conditions than those arising in X-Ray crystallography. A number of questions on SAS, however, remain unsolved, particularly in the light of modelling ensembles of conformers in solution. In this article, we study the ensemble average and covariance of SAS profiles analytically. Using this ensemble covariance, we demonstrate the ...[more]

PMID: 28493950

Similar Datasets

Nature-Inspired Hierarchical Steels.

Project description:Materials can be made strong, but as such they are often brittle and prone to fracture when under stress. Inspired by the exceptionally strong and ductile structure of byssal threads found in certain mussels, we have designed and manufactured a multi-hierarchical steel, based on an inexpensive austenitic stainless steel, which defeats this "conflict" by possessing both superior strength and ductility. These excellent mechanical properties are realized by structurally introducing sandwich structures at both the macro- and nano-scales, the latter via an isometric, alternating, dual-phase crystal phases comprising nano-band austenite and nano-lamellar martensite, without change in chemical composition. Our experiments (transmission and scanning electron microscopy, electron back-scattered diffraction, nano-indentation and tensile tests) and micromechanics simulation results reveal a synergy of mechanisms underlying such exceptional properties. This synergy is key to the development of vastly superior mechanical properties, and may provide a unique strategy for the future development of new super strong and tough (damage-tolerant), lightweight and inexpensive structural materials.

| S-EPMC5865137 | biostudies-literature

Analysis of the "thermodynamic information content" of a Homo sapiens structural database reveals hierarchical thermodynamic organization.

Project description:Classification of the amounts and types of lower order structural elements in proteins is a prerequisite to effective comparisons between protein folds. In an effort to provide an additional vehicle for fold comparison, we present an alternative classification scheme whereby protein folds are represented in statistical thermodynamic terms in such a way as to illuminate the energetic building blocks within protein structures. The thermodynamic relationship is examined between amino acid sequences and the conformational ensembles for a database of 159 Homo sapiens protein structures ranging from 50 to 250 amino acids. Using hierarchical clustering, it is shown through fold-recognition experiments that (1) eight thermodynamic environmental descriptors sufficiently accounts for the energetic variation within the native state ensembles of the H. sapiens structural database, (2) an amino acid library of only six residue types is sufficient to encode >90% of the thermodynamic information required for fold specificity in the entire database, and (3) structural resolution of the statistically derived environments reveals sequential cooperative segments throughout the protein, which are independent of secondary structure. As the first level of thermodynamic organization in proteins, these segments represent the thermodynamic counterpart to secondary structure.

| S-EPMC2279918 | biostudies-literature

Nature of light correlations in ghost imaging.

Project description:We investigate the nature of correlations in Gaussian light sources used for ghost imaging. We adopt methods from quantum information theory to distinguish genuinely quantum from classical correlations. Combining a microscopic analysis of speckle-speckle correlations with an effective coarse-grained description of the beams, we show that quantum correlations exist even in 'classical'-like thermal light sources, and appear relevant for the implementation of ghost imaging in the regime of low illumination. We further demonstrate that the total correlations in the thermal source beams effectively determine the quality of the imaging, as quantified by the signal-to-noise ratio.

| S-EPMC3439652 | biostudies-other

Information Content Differentiates Enhancers From Silencers in Mouse Photoreceptors

Project description:Enhancers and silencers often depend on the same transcription factors (TFs) and are imperfectly distinguished from each other by genomic assays of TF binding or chromatin state. To identify sequence features that define enhancers and silencers, we assayed massively parallel reporter libraries of genomic sequences targeted by the photoreceptor TF CRX and found instances of enhancer, silencer, or no activity. Both enhancers and silencers contain more TF motifs than inactive sequences, but enhancers contain motifs from a more diverse collection of TFs. We developed a measure of information content that describes the number and diversity of motifs in a sequence and found that, while both enhancers and silencers depend on CRX motifs, enhancers have higher information content. Our results indicate that enhancers contain motifs for a diverse but degenerate collection of TFs, while silencers depend on a smaller and less diverse collection of TFs.

2021-01-30 | GSE165812 | GEO

Origin of information-limiting noise correlations.

Project description:The ability to discriminate between similar sensory stimuli relies on the amount of information encoded in sensory neuronal populations. Such information can be substantially reduced by correlated trial-to-trial variability. Noise correlations have been measured across a wide range of areas in the brain, but their origin is still far from clear. Here we show analytically and with simulations that optimal computation on inputs with limited information creates patterns of noise correlations that account for a broad range of experimental observations while at same time causing information to saturate in large neural populations. With the example of a network of V1 neurons extracting orientation from a noisy image, we illustrate to our knowledge the first generative model of noise correlations that is consistent both with neurophysiology and with behavioral thresholds, without invoking suboptimal encoding or decoding or internal sources of variability such as stochastic network dynamics or cortical state fluctuations. We further show that when information is limited at the input, both suboptimal connectivity and internal fluctuations could similarly reduce the asymptotic information, but they have qualitatively different effects on correlations leading to specific experimental predictions. Our study indicates that noise at the sensory periphery could have a major effect on cortical representations in widely studied discrimination tasks. It also provides an analytical framework to understand the functional relevance of different sources of experimentally measured correlations.

| S-EPMC4687541 | biostudies-literature

Fast R Functions for Robust Correlations and Hierarchical Clustering.

Project description:Many high-throughput biological data analyses require the calculation of large correlation matrices and/or clustering of a large number of objects. The standard R function for calculating Pearson correlation can handle calculations without missing values efficiently, but is inefficient when applied to data sets with a relatively small number of missing data. We present an implementation of Pearson correlation calculation that can lead to substantial speedup on data with relatively small number of missing entries. Further, we parallelize all calculations and thus achieve further speedup on systems where parallel processing is available. A robust correlation measure, the biweight midcorrelation, is implemented in a similar manner and provides comparable speed. The functions cor and bicor for fast Pearson and biweight midcorrelation, respectively, are part of the updated, freely available R package WGCNA.The hierarchical clustering algorithm implemented in R function hclust is an order n(3) (n is the number of clustered objects) version of a publicly available clustering algorithm (Murtagh 2012). We present the package flashClust that implements the original algorithm which in practice achieves order approximately n(2), leading to substantial time savings when clustering large data sets.

| S-EPMC3465711 | biostudies-literature

Gene expression profile between ARID1A knockdown SAS cells and mock SAS cells

Project description:The aim of the study was to elucidate the character of ARID1A by a comprehensive gene expression analysis.

2019-04-09 | GSE129475 | GEO

The non-unitary nature of information preference.

Project description:Factors affecting information-seeking behaviour can be task-endogenous (e.g., probability of winning a gamble), or task-exogenous (e.g., personality trait measures). Various task-endogenous factors affecting non-instrumental information-seeking behaviour have been identified, but it is unclear how task-exogenous factors affect such behaviour, and if they interact with task-endogenous factors. In an online information seeking experiment (N = 279), we focus on the role that outcome probability, as a task-endogenous factor, has on information preferences. We find reliable preference for advance information on highly probable gains and low preference for highly probable losses. Comparisons with individual trait measures of information preference (e.g., intolerance of uncertainty scale, obsessive-compulsive inventory, information preferences scale) reveal minimal association between these task-exogenous factors with choice task performance. We also find minimal interaction between outcome probability and individual trait measures. Despite the choice task and trait measures purportedly tapping the same (or similar) construct, the absence of clear relationships ultimately suggests a multi-dimensional nature of information preference.

| S-EPMC10716071 | biostudies-literature

Confidence resets reveal hierarchical adaptive learning in humans.

Project description:Hierarchical processing is pervasive in the brain, but its computational significance for learning under uncertainty is disputed. On the one hand, hierarchical models provide an optimal framework and are becoming increasingly popular to study cognition. On the other hand, non-hierarchical (flat) models remain influential and can learn efficiently, even in uncertain and changing environments. Here, we show that previously proposed hallmarks of hierarchical learning, which relied on reports of learned quantities or choices in simple experiments, are insufficient to categorically distinguish hierarchical from flat models. Instead, we present a novel test which leverages a more complex task, whose hierarchical structure allows generalization between different statistics tracked in parallel. We use reports of confidence to quantitatively and qualitatively arbitrate between the two accounts of learning. Our results support the hierarchical learning framework, and demonstrate how confidence can be a useful metric in learning theory.

| S-EPMC6474633 | biostudies-other

Noise enhances information transfer in hierarchical networks.

Project description:We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Barabási networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor.

| S-EPMC3565226 | biostudies-other

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data