scholarly journals OPTIMIZATION BIAS IN ENERGY-BASED STRUCTURE PREDICTION

2013 ◽  
Vol 12 (08) ◽  
pp. 1341014 ◽  
Author(s):  
ROBERT J. PETRELLA
Keyword(s):  
Structure Prediction ◽  
Probability Distributions ◽  
Energy Shift ◽  
Model Systems ◽  
Local Probability ◽  
Optimal Accuracy ◽  
Simple Systems ◽  
Local Sampling ◽  
Correction Terms ◽  
Unequal Sampling

Physics-based computational approaches to predicting the structure of macromolecules such as proteins are gaining increased use, but there are remaining challenges. In the current work, it is demonstrated that in energy-based prediction methods, the degree of optimization of the sampled structures can influence the prediction results. In particular, discrepancies in the degree of local sampling can bias the predictions in favor of the oversampled structures by shifting the local probability distributions of the minimum sampled energies. In simple systems, it is shown that the magnitude of the errors can be calculated from the energy surface, and for certain model systems, derived analytically. Further, it is shown that for energy wells whose forms differ only by a randomly assigned energy shift, the optimal accuracy of prediction is achieved when the sampling around each structure is equal. Energy correction terms can be used in cases of unequal sampling to reproduce the total probabilities that would occur under equal sampling, but optimal corrections only partially restore the prediction accuracy lost to unequal sampling. For multiwell systems, the determination of the correction terms is a multibody problem; it is shown that the involved cross-correlation multiple integrals can be reduced to simpler integrals. The possible implications of the current analysis for macromolecular structure prediction are discussed.

STRUCTURE PREDICTION OF HELICAL TRANSMEMBRANE PROTEINS AT TWO LENGTH SCALES

2006 ◽  
Vol 04 (02) ◽  
pp. 317-333 ◽  
Author(s):  
ZHONG CHEN ◽  
YING XU
Keyword(s):  
Protein Structure ◽  
Membrane Protein ◽  
Structure Prediction ◽  
Model Systems ◽  
Membrane Protein Structure ◽  
Transmembrane Helices ◽  
Energy Functions ◽  
Multi Scale ◽  
Knowledge Based ◽  
Atom Level

As the first step toward a multi-scale, hierarchical computational approach for membrane protein structure prediction, the packing of transmembrane helices was modeled at the residue and atom levels, respectively. For predictions at the residue level, the helix-helix and helix-membrane interactions were described by a set of knowledge-based energy functions. For predictions at the atom level, CHARMM19 force field was used. To facilitate the system to overcome energy barriers, the Wang–Landau method was employed, where a random walk is performed in the energy space with a uniform probability. Native-like structures were predicted at both levels for two model systems, each of which consists of two transmembrane helices. Interestingly, consistent results were obtained from simulations at the residue and atom levels for the same system, strongly suggesting the feasibility of a hierarchical approach for membrane protein structure predictions.

scholarly journals The repetitive local sampling and the local distribution theory

2021 ◽  
Author(s):  
PU TIAN
Keyword(s):  
Molecular Simulation ◽  
Structure Prediction ◽  
Materials Science ◽  
Molecular System ◽  
Potential Of Mean Force ◽  
Distribution Theory ◽  
Local Distribution ◽  
Structural Refinement ◽  
Molecular Systems ◽  
Local Sampling

Molecular simulation is a mature and versatile tool set widely utilized in many subjects with more than 30,000 publications each year. However, its methodology development has been struggling with a tradeoff between accuracy/resolution and speed, significant improvement of both beyond present state of the art is necessary to reliably substitute many expensive and laborious experiments in molecular biology, materials science and nanotechnology. Previously, the ubiquitous issue regarding severe wasting of computational resources in all forms of molecular simulations due to repetitive local sampling was raised, and the local free energy landscape approach was proposed to address it. This approach is derived from a simple idea of first learning local distributions, and followed by dynamic assembly of which to infer global joint distribution of a target molecular system. When compared with conventional explicit solvent molecular dynamics simulations, a simple and approximate implementation of this theory in protein structural refinement harvested acceleration of about six orders of magnitude without loss of accuracy. While this initial test revealed tremendous benefits for addressing repetitive local sampling, there are some implicit assumptions need to be articulated. Here, I present a more thorough discussion of repetitive local sampling; potential options for learning local distributions; a more general formulation with potential extension to simulation of near equilibrium molecular systems; the prospect of developing computation driven molecular science; the connection to mainstream residue pair distance distribution based protein structure prediction/refinement; and the fundamental difference of utilizing averaging from conventional molecular simulation framework based on potential of mean force. This more general development is termed the local distribution theory to release the limitation of strict thermodynamic equilibrium in its potential wide application in general soft condensed molecular systems.

Image Labeling by Integration of Local Co-Occurrence Histogram and Global Features

2014 ◽  
Vol 18 (4) ◽  
pp. 511-517
Author(s):  
Takuto Omiya ◽  
◽  
Kazuhiro Hotta
Keyword(s):  
Posterior Probability ◽  
Probability Distributions ◽  
Texture Features ◽  
Bag Of Words ◽  
Global Features ◽  
Image Labeling ◽  
Local Probability ◽  
Local And Global Features ◽  
Conventional Methods ◽  
Labeling Method

In this paper, we perform image labeling based on the probabilistic integration of local and global features. Several conventional methods label pixels or regions using features extracted from local regions and local contextual relationships between neighboring regions. However, labeling results tend to depend on local viewpoints. To overcome this problem, we propose an image labeling method that utilizes both local and global features. We compute the posterior probability distributions of the local and global features independently, and they are integrated by the product. To compute the probability of the global region (entire image), Bag-of-Words is used. In contrast, local cooccurrence between color and texture features is used to compute local probability. In the experiments, we use the MSRC21 dataset. The result demonstrates that the use of global viewpoint significantly improves labeling accuracy.

Probabilistische Zustandsanalyse von tragendem Mauerwerk / A Probabilistic Evaluation Method for Structural Masonry

1997 ◽  
Vol 3 (6) ◽  
pp. 553-568
Author(s):  
L. Schueremans ◽  
D. Van Gemert ◽  
J. Van Dyck
Keyword(s):  
Evaluation Method ◽  
Failure Modes ◽  
Probability Distributions ◽  
Probabilistic Method ◽  
Limit State ◽  
Evaluation Instrument ◽  
Visual Evaluation ◽  
Probabilistic Evaluation ◽  
Local Probability ◽  
State Functions

Abstract A probabilistic method to evaluate the reliability of structural masonry elements is presented. Local probability of failure, different failure modes and corresponding limit state functions, probability distributions of basic variables and model uncertainty are discussed. A graphical probability mapping is presented as an easilyaccessible, visual evaluation instrument in the restoration decision process. The proposed methodology is illustrated on tested shear panels, reported in literature.

Asteroid and Comet Impact Speeds upon Mars: Significance for Panspermia and the Supply of Organics

1997 ◽  
Vol 161 ◽  
pp. 197-201 ◽  
Author(s):  
Duncan Steel
Keyword(s):  
Probability Distributions ◽  
Regions Of Interest ◽  
Significant Fraction ◽  
Organic Chemicals ◽  
Impact Speed ◽  
The Mean ◽  
The Impact

AbstractWhilst lithopanspermia depends upon massive impacts occurring at a speed above some limit, the intact delivery of organic chemicals or other volatiles to a planet requires the impact speed to be below some other limit such that a significant fraction of that material escapes destruction. Thus the two opposite ends of the impact speed distributions are the regions of interest in the bioastronomical context, whereas much modelling work on impacts delivers, or makes use of, only the mean speed. Here the probability distributions of impact speeds upon Mars are calculated for (i) the orbital distribution of known asteroids; and (ii) the expected distribution of near-parabolic cometary orbits. It is found that cometary impacts are far more likely to eject rocks from Mars (over 99 percent of the cometary impacts are at speeds above 20 km/sec, but at most 5 percent of the asteroidal impacts); paradoxically, the objects impacting at speeds low enough to make organic/volatile survival possible (the asteroids) are those which are depleted in such species.

Introduction

1978 ◽  
Vol 36 (3) ◽  
pp. 543-544
Author(s):  
W. Bernard
Keyword(s):  
Molecular Weight ◽  
Electron Microscope ◽  
Nucleic Acid ◽  
Gene Mapping ◽  
Molecular Genetics ◽  
Cell Biology ◽  
Gene Activity ◽  
Close Connection ◽  
Basic Information ◽  
Simple Systems

In comparison to many other fields of ultrastructural research in Cell Biology, the successful exploration of genes and gene activity with the electron microscope in higher organisms is a late conquest. Nucleic acid molecules of Prokaryotes could be successfully visualized already since the early sixties, thanks to the Kleinschmidt spreading technique - and much basic information was obtained concerning the shape, length, molecular weight of viral, mitochondrial and chloroplast nucleic acid. Later, additonal methods revealed denaturation profiles, distinction between single and double strandedness and the use of heteroduplexes-led to gene mapping of relatively simple systems carried out in close connection with other methods of molecular genetics.

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