scholarly journals Loop modeling: Sampling, filtering, and scoring

2008 ◽  
Vol 70 (3) ◽  
pp. 834-843 ◽  
Author(s):  
Cinque S. Soto ◽  
Marc Fasnacht ◽  
Jiang Zhu ◽  
Lucy Forrest ◽  
Barry Honig
Keyword(s):  
Loop Modeling

Protein Loop Modeling with Optimized Backbone Potential Functions

2012 ◽  
Vol 8 (5) ◽  
pp. 1820-1827 ◽  
Author(s):  
Shide Liang ◽  
Chi Zhang ◽  
Jamica Sarmiento ◽  
Daron M. Standley
Keyword(s):  
Potential Functions ◽  
Loop Modeling

A frequency approach for current loop modeling with a PWM converter

1998 ◽  
Vol 34 (5) ◽  
pp. 1000-1014 ◽  
Author(s):  
P.P. Robet ◽  
M. Gautier ◽  
C. Bergmann
Keyword(s):  
Current Loop ◽  
Loop Modeling ◽  
Pwm Converter

scholarly journals A Constraint Solver for Flexible Protein Model

2013 ◽  
Vol 48 ◽  
pp. 953-1000 ◽  
Author(s):  
F. Campeotto ◽  
A. Dal Palù ◽  
A. Dovier ◽  
F. Fioretto ◽  
E. Pontelli
Keyword(s):  
Structure Prediction ◽  
Dimensional Space ◽  
Protein Structures ◽  
Empirical Evaluation ◽  
Geometric Constraints ◽  
Conformational Space ◽  
Loop Modeling ◽  
Geometric Properties ◽  
Constraint Solver ◽  
Multi Body

This paper proposes the formalization and implementation of a novel class of constraints aimed at modeling problems related to placement of multi-body systems in the 3-dimensional space. Each multi-body is a system composed of body elements, connected by joint relationships and constrained by geometric properties. The emphasis of this investigation is the use of multi-body systems to model native conformations of protein structures---where each body represents an entity of the protein (e.g., an amino acid, a small peptide) and the geometric constraints are related to the spatial properties of the composing atoms. The paper explores the use of the proposed class of constraints to support a variety of different structural analysis of proteins, such as loop modeling and structure prediction. The declarative nature of a constraint-based encoding provides elaboration tolerance and the ability to make use of any additional knowledge in the analysis studies. The filtering capabilities of the proposed constraints also allow to control the number of representative solutions that are withdrawn from the conformational space of the protein, by means of criteria driven by uniform distribution sampling principles. In this scenario it is possible to select the desired degree of precision and/or number of solutions. The filtering component automatically excludes configurations that violate the spatial and geometric properties of the composing multi-body system. The paper illustrates the implementation of a constraint solver based on the multi-body perspective and its empirical evaluation on protein structure analysis problems.

KORP: knowledge-based 6D potential for fast protein and loop modeling

2019 ◽  
Vol 35 (17) ◽  
pp. 3013-3019 ◽  
Author(s):  
José Ramón López-Blanco ◽  
Pablo Chacón
Keyword(s):  
Structure Prediction ◽  
Protein Structures ◽  
Joint Probability ◽  
Protein Modeling ◽  
Supplementary Information ◽  
Joint Probability Distribution ◽  
Loop Modeling ◽  
Statistical Potentials ◽  
Knowledge Based ◽  
Backbone Atoms

Abstract Motivation Knowledge-based statistical potentials constitute a simpler and easier alternative to physics-based potentials in many applications, including folding, docking and protein modeling. Here, to improve the effectiveness of the current approximations, we attempt to capture the six-dimensional nature of residue–residue interactions from known protein structures using a simple backbone-based representation. Results We have developed KORP, a knowledge-based pairwise potential for proteins that depends on the relative position and orientation between residues. Using a minimalist representation of only three backbone atoms per residue, KORP utilizes a six-dimensional joint probability distribution to outperform state-of-the-art statistical potentials for native structure recognition and best model selection in recent critical assessment of protein structure prediction and loop-modeling benchmarks. Compared with the existing methods, our side-chain independent potential has a lower complexity and better efficiency. The superior accuracy and robustness of KORP represent a promising advance for protein modeling and refinement applications that require a fast but highly discriminative energy function. Availability and implementation http://chaconlab.org/modeling/korp. Supplementary information Supplementary data are available at Bioinformatics online.

Theoretical and Experimental Determination of Geometry Deviation in Continuous Path Controlled OD Grinding Processes

2011 ◽  
Vol 223 ◽  
pp. 784-793 ◽  
Author(s):  
Berend Denkena ◽  
Ruben Fischer
Keyword(s):  
Process Model ◽  
Simulation Method ◽  
Model Verification ◽  
Parameter Study ◽  
Force Model ◽  
Grinding Process ◽  
Continuous Path ◽  
Loop Modeling ◽  
Machine Model ◽  
Structural Dynamic

A simulation method for the continuous path controlled grinding process is described in this paper. The process model allows a virtual analysis of the process parameters, different input conditions and their influence concerning workpiece geometry. The machine structure is represented by a structural dynamic system. The dynamic influence of the machine axes is considered by its control system parameters. In this machine model, the positioning error of the axes can be calculated in relation to the reference position. This is used to compute the local engagement numerically. The process force is estimated by means of an empirical grinding force model. Hence, the overall process force can be calculated and fed back into the dynamic model. Closed-loop modeling allows to rate the influence of different parameters with respect to process errors. Finally, a parameter study and the model verification under real conditions are presented on the basis of the wheel head oscillating pin grinding process of crankshafts.

LOCA with Consequential or Delayed LOOP: Modeling of Accident Sequences and Associated Core Damage Frequency

2000 ◽  
Vol 131 (3) ◽  
pp. 297-318 ◽  
Author(s):  
Gerardo Martinez-Guridi ◽  
Pranab Samanta ◽  
Tsong-Lun Chu ◽  
Ji-Wu Yang
Keyword(s):  
Loop Modeling ◽  
Core Damage ◽  
Accident Sequences
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