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相关论文: Thermodynamic Sampling of Molecular Conformations

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The standard Kernel Quadrature method for numerical integration with random point sets (also called Bayesian Monte Carlo) is known to converge in root mean square error at a rate determined by the ratio $s/d$, where $s$ and $d$ encode the…

机器学习 · 统计学 2017-08-01 Francois-Xavier Briol , Chris J. Oates , Jon Cockayne , Wilson Ye Chen , Mark Girolami

We introduce a formulation for normal mode analyses of globular proteins that significantly improves on an earlier, 1-parameter formulation (M. Tirion, PRL 77, 1905 (1996)) that characterized the slow modes associated with protein data bank…

生物物理 · 物理学 2015-04-01 Monique M. Tirion , Daniel ben-Avraham

By simulating the first order globule-crystal transition of a flexible homopolymer chain, both by collision dynamics and Monte Carlo with non-kinetic moves, we show that the effective and the thermodynamic transition temperatures are…

软凝聚态物质 · 物理学 2017-08-23 S. Ruzicka , D. Quigley , M. P. Allen

There are several simple criteria of folding to a native state in model proteins. One of them involves crossing of a threshold value of the RMSD distance away from the native state. Another checks whether all native contacts are…

生物大分子 · 定量生物学 2009-11-10 Joanna I. Kwiecinska , Marek Cieplak

The protein folding problem must ultimately be solved on all length scales from the atomic up through a hierarchy of complicated structures. By analyzing the stability of the folding process using physics and mathematics, this paper shows…

生物物理 · 物理学 2015-05-28 Walter Simmons , Joel L. Weiner

The Dynamic Monte Carlo (DMC) method is an established molecular simulation technique for the analysis of the dynamics in colloidal suspensions. An excellent alternative to Brownian Dynamics or Molecular Dynamics simulation, DMC is…

软凝聚态物质 · 物理学 2020-07-15 Fabián A. García Daza , Alejandro Cuetos , Alessandro Patti

Within the frame of an effective, coarse-grained hydrophobic-polar protein model, we employ multicanonical Monte Carlo simulations to investigate free-energy landscapes and folding channels of exemplified heteropolymer sequences, which are…

软凝聚态物质 · 物理学 2009-11-13 Stefan Schnabel , Michael Bachmann , Wolfhard Janke

Many inverse problems in nuclear fusion and high-energy astrophysics research, such as the optimization of tokamak reactor geometries or the inference of black hole parameters from interferometric images, necessitate high-dimensional…

机器学习 · 计算机科学 2025-05-09 Jonathan Gorard , Ammar Hakim , Hong Qin , Kyle Parfrey , Shantenu Jha

Background: Designing amino acid sequences that are stable in a given target structure amounts to maximizing a conditional probability. A straightforward approach to accomplish this is a nested Monte Carlo where the conformation space is…

软凝聚态物质 · 物理学 2016-08-31 Anders Irbäck , Carsten Peterson , Frank Potthast , Erik Sandelin

A reduced model, which can fold both helix and sheet structures, is proposed to study the problem of protein folding. The goal of this model is to find an unbiased effective potential that has included the effects of water and at the same…

软凝聚态物质 · 物理学 2007-05-23 Nan-yow Chen

In this letter, the possible dynamic scaling properties of protein molecules in folding are investigated theoretically by assuming that the protein molecules are percolated networks. It is shown that the fractal character and the fractal…

凝聚态物理 · 物理学 2007-05-23 Liang-Jian Zou , X. G. Gong , Zheng-Gang Zhu

Monte Carlo simulations are performed for the S = 1/2 XY and ferro- and antiferromagnetic Heisenberg model in two dimensions using the loop algorithm. Thermodynamic properties of all these models are investigated in wide temperature range.…

统计力学 · 物理学 2013-02-07 A. K. Murtazaev , M. A. Magomedov

We present iterative Monte Carlo algorithm for which the temperature variable is attracted by a critical point. The algorithm combines techniques of single histogram reweighting and linear filtering. The 2d Ising model of ferromagnet is…

统计力学 · 物理学 2015-06-24 M. Gmitra , D. Horvath

A microscopic model of adsorption in cluster forming systems with competing interaction is considered. The adsorption process is described by the master equation and modelled by a kinetic Monte Carlo method. The evolution of the particle…

软凝聚态物质 · 物理学 2021-09-07 E. Bildanau , V. Vikhrenko

These lectures will address two questions. Is there a simple variational principle underlying the existence of secondary motifs in the native state of proteins? Is there a general approach which can qualitatively capture the salient…

统计力学 · 物理学 2007-05-23 Jay Banavar , Amos Maritan , Cristian Micheletti , Flavio Seno

We review some of our recent results obtained within the scope of simple lattice models and Monte Carlo simulations that illustrate the role of native geometry in the folding kinetics of two state folders.

生物大分子 · 定量生物学 2007-05-23 P. F. N. Faisca , M. M. Telo da Gama

In Markov Chain Monte Carlo (MCMC) simulations, the thermal equilibria quantities are estimated by ensemble average over a sample set containing a large number of correlated samples. These samples are selected in accordance with the…

数据分析、统计与概率 · 物理学 2015-01-08 J. Li , P. Vignal , S. Sun , V. M. Calo

Coarse-grained (lattice-) models have a long tradition in aiding efforts to decipher the physical or biological complexity of proteins. Despite the simplicity of these models, however, numerical simulations are often computationally very…

软凝聚态物质 · 物理学 2012-09-14 Thomas Wüst , David P. Landau

The extent of coupling between the folding of a protein and its binding to a substrate varies from protein to protein. Some proteins have highly structured native states in solution, while others are natively disordered and only fold fully…

软凝聚态物质 · 物理学 2012-05-16 Brenda M. Rubenstein , Ivan Coluzza , Mark A. Miller

As an example of topic where biology and physics meet, we present the issue of protein folding and stability, and the development of thermodynamics-based bioinformatics tools that predict the stability and thermal resistance of proteins and…

生物大分子 · 定量生物学 2016-03-15 Fabrizio Pucci , Marianne Rooman