Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Guiding conformation space search with an all-atom energy potential.

ABSTRACT: The most significant impediment for protein structure prediction is the inadequacy of conformation space search. Conformation space is too large and the energy landscape too rugged for existing search methods to consistently find near-optimal minima. To alleviate this problem, we present model-based search, a novel conformation space search method. Model-based search uses highly accurate information obtained during search to build an approximate, partial model of the energy landscape. Model-based search aggregates information in the model as it progresses, and in turn uses this information to guide exploration toward regions most likely to contain a near-optimal minimum. We validate our method by predicting the structure of 32 proteins, ranging in length from 49 to 213 amino acids. Our results demonstrate that model-based search is more effective at finding low-energy conformations in high-dimensional conformation spaces than existing search methods. The reduction in energy translates into structure predictions of increased accuracy.

SUBMITTER: Brunette TJ

PROVIDER: S-EPMC2752431 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Guiding conformation space search with an all-atom energy potential.

Brunette T J TJ Brock Oliver O

Proteins 20081201 4

The most significant impediment for protein structure prediction is the inadequacy of conformation space search. Conformation space is too large and the energy landscape too rugged for existing search methods to consistently find near-optimal minima. To alleviate this problem, we present model-based search, a novel conformation space search method. Model-based search uses highly accurate information obtained during search to build an approximate, partial model of the energy landscape. Model-base ...[more]

PMID: 18536015

Similar Datasets

Molecular Conformer Search with Low-Energy Latent Space.

Project description:Identifying low-energy conformers with quantum mechanical accuracy for molecules with many degrees of freedom is challenging. In this work, we use the molecular dihedral angles as features and explore the possibility of performing molecular conformer search in a latent space with a generative model named variational auto-encoder (VAE). We bias the VAE towards low-energy molecular configurations to generate more informative data. In this way, we can effectively build a reliable energy model for the low-energy potential energy surface. After the energy model has been built, we extract local-minimum conformations and refine them with structure optimization. We have tested and benchmarked our low-energy latent-space (LOLS) structure search method on organic molecules with 5-9 searching dimensions. Our results agree with previous studies.

| S-EPMC9281398 | biostudies-literature

Ultracold atom interferometry in space.

Project description:Bose-Einstein condensates (BECs) in free fall constitute a promising source for space-borne interferometry. Indeed, BECs enjoy a slowly expanding wave function, display a large spatial coherence and can be engineered and probed by optical techniques. Here we explore matter-wave fringes of multiple spinor components of a BEC released in free fall employing light-pulses to drive Bragg processes and induce phase imprinting on a sounding rocket. The prevailing microgravity played a crucial role in the observation of these interferences which not only reveal the spatial coherence of the condensates but also allow us to measure differential forces. Our work marks the beginning of matter-wave interferometry in space with future applications in fundamental physics, navigation and earth observation.

| S-EPMC7910597 | biostudies-literature

Observation of dynamic atom-atom correlation in liquid helium in real space.

Project description:Liquid 4He becomes superfluid and flows without resistance below temperature 2.17 K. Superfluidity has been a subject of intense studies and notable advances were made in elucidating the phenomenon by experiment and theory. Nevertheless, details of the microscopic state, including dynamic atom-atom correlations in the superfluid state, are not fully understood. Here using a technique of neutron dynamic pair-density function (DPDF) analysis we show that 4He atoms in the Bose-Einstein condensate have environment significantly different from uncondensed atoms, with the interatomic distance larger than the average by about 10%, whereas the average structure changes little through the superfluid transition. DPDF peak not seen in the snap-shot pair-density function is found at 2.3 Å, and is interpreted in terms of atomic tunnelling. The real space picture of dynamic atom-atom correlations presented here reveal characteristics of atomic dynamics not recognized so far, compelling yet another look at the phenomenon.

| S-EPMC5418607 | biostudies-literature

Halogen Atom Participation in Guiding the Stereochemical Outcomes of Acetal Substitution Reactions.

Project description:Acetal substitution reactions of α-halogenated five- and six-membered rings can be highly stereoselective. Erosion of stereoselectivity occurs as nucleophilicity increases, which is consistent with additions to a halogen-stabilized oxocarbenium ion, not a three-membered-ring halonium ion. Computational investigations confirmed that the open-form oxocarbenium ions are the reactive intermediates involved. Kinetic studies suggest that hyperconjugative effects and through-space electrostatic interactions can both contribute to the stabilization of halogen-substituted oxocarbenium ions.

| S-EPMC9561118 | biostudies-literature

Prediction of tetraoxygen reaction mechanism with sulfur atom on the singlet potential energy surface.

Project description:The mechanism of S+O? (D(?h)) reaction has been investigated at the B3LYP/6-311+G(3df) and CCSD levels on the singlet potential energy surface. One stable complex has been found for the S+O? (D(?h)) reaction, IN1, on the singlet potential energy surface. For the title reaction, we obtained four kinds of products at the B3LYP level, which have enough thermodynamic stability. The results reveal that the product P3 is spontaneous and exothermic with -188.042 and -179.147?kcal/mol in Gibbs free energy and enthalpy of reaction, respectively. Because P1 adduct is produced after passing two low energy level transition states, kinetically, it is the most favorable adduct in the ¹S+¹O? (D(?h)) atmospheric reactions.

| S-EPMC3920618 | biostudies-literature

A drunken search in crystallization space.

Project description:The REMARK280 field of the Protein Data Bank is the richest open source of successful crystallization information. The REMARK280 field is optional and currently uncurated, so significant effort needs to be applied to extract reliable data. There are well over 15 000 crystallization conditions available commercially from 12 different vendors. After putting the PDB crystallization information and the commercial cocktail data into a consistent format, these data are used to extract information about the overlap between the two sets of crystallization conditions. An estimation is made as to which commercially available conditions are most appropriate for producing well diffracting crystals by looking at which commercial conditions are found unchanged (or almost unchanged) in the PDB. Further analyses include which commercial kits are the most appropriate for shotgun or more traditional approaches to crystallization screening. This analysis suggests that almost 40% of the crystallization conditions found currently in the PDB are identical or very similar to a commercial condition.

| S-EPMC4188070 | biostudies-literature

Learning in continuous action space for developing high dimensional potential energy models.

Project description:Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action spaces, as in chess, Shogi and Go. Many real-world materials discovery and design applications, however, involve multi-dimensional search problems and learning domains that have continuous action spaces. Exploring high-dimensional potential energy models of materials is an example. Traditionally, these searches are time consuming (often several years for a single bulk system) and driven by human intuition and/or expertise and more recently by global/local optimization searches that have issues with convergence and/or do not scale well with the search dimensionality. Here, in a departure from discrete action and other gradient-based approaches, we introduce a RL strategy based on decision trees that incorporates modified rewards for improved exploration, efficient sampling during playouts and a "window scaling scheme" for enhanced exploitation, to enable efficient and scalable search for continuous action space problems. Using high-dimensional artificial landscapes and control RL problems, we successfully benchmark our approach against popular global optimization schemes and state of the art policy gradient methods, respectively. We demonstrate its efficacy to parameterize potential models (physics based and high-dimensional neural networks) for 54 different elemental systems across the periodic table as well as alloys. We analyze error trends across different elements in the latent space and trace their origin to elemental structural diversity and the smoothness of the element energy surface. Broadly, our RL strategy will be applicable to many other physical science problems involving search over continuous action spaces.

| S-EPMC8766468 | biostudies-literature

FAAST: Flow-space Assisted Alignment Search Tool.

Project description:BackgroundHigh throughput pyrosequencing (454 sequencing) is the major sequencing platform for producing long read high throughput data. While most other sequencing techniques produce reading errors mainly comparable with substitutions, pyrosequencing produce errors mainly comparable with gaps. These errors are less efficiently detected by most conventional alignment programs and may produce inaccurate alignments.ResultsWe suggest a novel algorithm for calculating the optimal local alignment which utilises flowpeak information in order to improve alignment accuracy. Flowpeak information can be retained from a 454 sequencing run through interpretation of the binary SFF-file format. This novel algorithm has been implemented in a program named FAAST (Flow-space Assisted Alignment Search Tool).ConclusionsWe present and discuss the results of simulations that show that FAAST, through the use of the novel algorithm, can gain several percentage points of accuracy compared to Smith-Waterman-Gotoh alignments, depending on the 454 data quality. Furthermore, through an efficient multi-thread aware implementation, FAAST is able to perform these high quality alignments at high speed. The tool is available at http://www.ifm.liu.se/bioinfo/

| S-EPMC3228549 | biostudies-literature

Feature space resampling for protein conformational search.

Project description:De novo protein structure prediction requires location of the lowest energy state of the polypeptide chain among a vast set of possible conformations. Powerful approaches include conformational space annealing, in which search progressively focuses on the most promising regions of conformational space, and genetic algorithms, in which features of the best conformations thus far identified are recombined. We describe a new approach that combines the strengths of these two approaches. Protein conformations are projected onto a discrete feature space which includes backbone torsion angles, secondary structure, and beta pairings. For each of these there is one "native" value: the one found in the native structure. We begin with a large number of conformations generated in independent Monte Carlo structure prediction trajectories from Rosetta. Native values for each feature are predicted from the frequencies of feature value occurrences and the energy distribution in conformations containing them. A second round of structure prediction trajectories are then guided by the predicted native feature distributions. We show that native features can be predicted at much higher than background rates, and that using the predicted feature distributions improves structure prediction in a benchmark of 28 proteins. The advantages of our approach are that features from many different input structures can be combined simultaneously without producing atomic clashes or otherwise physically inviable models, and that the features being recombined have a relatively high chance of being correct.

| S-EPMC2854516 | biostudies-literature

The suprachoroidal space: from potential space to a space with potential.

Project description:Recent advances have made it possible to image the suprachoroidal space, and the understanding of its clinical applications is currently being greatly expanded. This opinion piece covers the advances in imaging techniques that enable the demonstration of the suprachoroidal space, and its implication in various retinal pathologies. It also reviews its potential uses as a route for drug delivery for the treatment of retinal diseases, and its use in innovative surgical techniques. Current research is leading the way for the suprachoroidal space to be an aspect of retinal disease diagnosis, monitoring, medical treatment, and surgical manipulation.

| S-EPMC4734808 | biostudies-literature

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