scholarly journals Accelerating the Convergence of Replica Exchange Simulations Using Gibbs Sampling and Adaptive Temperature Sets

2015 ◽  
Vol 68 ◽  
pp. 125-129 ◽  
Author(s):  
Thomas Vogel ◽  
Danny Perez
Keyword(s):  
Gibbs Sampling ◽  
Replica Exchange

Computational Investigations of the Transmembrane Italian-Mutant (E22K) 3A\(\beta_{11 - 40}\) in Aqueous Solution

2018 ◽  
Vol 28 (3) ◽  
pp. 265 ◽  
Author(s):  
Son Tung Ngo
Keyword(s):  
Structural Changes ◽  
Replica Exchange ◽  
Aβ Oligomers ◽  
Replica Exchange Molecular Dynamics ◽  
Charged Residue ◽  
Intermolecular Contacts ◽  
Structural Details ◽  
Positively Charged ◽  
Dynamic Fluctuation ◽  
Good Agreement

The Amyloid beta (Aβ) oligomers are characterized as critical cytotoxic materials in Alzheimer’s disease (AD) pathogenesis. Structural details of transmembrane oligomers are inevitably necessary to design/search potential inhibitor due to treat AD. However, the experimental detections for structural modify of low-order Aβ oligomers are precluded due to the extremely dynamic fluctuation of the oligomers. In this project, the transmembrane Italian-mutant (E22K) 3Aβ11-40 (tmE22K 3Aβ11-40) was extensively investigated upon the temperature replica exchange molecular dynamics (REMD) simulations. The structural changes of the trimer when replacing the negative charged residue E22 by a positively charged residue K were monitored over simulation intervals. The oligomer size was turned to be larger and the increase of β-content was recorded. The momentous gain of intermolecular contacts with DPPC molecules implies that tmE22K 3Aβ11-40 easier self-inserts into the membrane than the WT one. Furthermore, the tighter interaction between constituting monomers was indicated implying that the E22K mutation probably enhances the Aβ fibril formation. The results are in good agreement with experiments that E22K amyloid is self-aggregate faster than the WT form. Details information of tmE22K trimer structure and kinetics probably yield the understanding of AD mechanism.

Gibbs Sampling for Marginal Posterior Expectations

1991 ◽  
Author(s):  
Alan E. Gelfand ◽  
Adrian F. Smith
Keyword(s):  
Gibbs Sampling

The enhanced sampling in parallel finite-time dynamics method with replica exchange

2021 ◽  
Vol 263 ◽  
pp. 107911
Author(s):  
Chudong Xu ◽  
Shengdong Lu ◽  
Yongfeng Kong ◽  
Wanjie Xiong
Keyword(s):  
Finite Time ◽  
Replica Exchange ◽  
Enhanced Sampling ◽  
Time Dynamics ◽  
Dynamics Method

Molecular Mechanism of Amyloidogenicity and Neurotoxicity of a Pro-aggregated Tau Mutant in the Presence of Histidine Tautomerism via Replica-Exchange Simulation.

2021 ◽  
Author(s):  
Sompriya Chatterjee ◽  
Abbas Salimi ◽  
Jin Yong Lee
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Mechanism ◽  
Replica Exchange

The accumulation of ΔK280 tau mutant resulting in neurotoxic oligomeric aggregates is an important but yet mysterious procedure in Alzheimer’s disease (AD) development. Recently, we proposed a histidine tautomerization hypothesis...

Model of the influence of Internet finance on monetary policy based on gibbs sampling and vector autoregression

2020 ◽  
pp. 1-11
Author(s):  
Hui Wang ◽  
Huang Shiwang
Keyword(s):  
Monetary Policy ◽  
Gibbs Sampling ◽  
Posterior Distribution ◽  
Estimation Method ◽  
Financial Supervision ◽  
Var Model ◽  
Model Parameters ◽  
State Variables ◽  
Frequency Data ◽  
Internet Finance

The various parts of the traditional financial supervision and management system can no longer meet the current needs, and further improvement is urgently needed. In this paper, the low-frequency data is regarded as the missing of the high-frequency data, and the mixed frequency VAR model is adopted. In order to overcome the problems caused by too many parameters of the VAR model, this paper adopts the Bayesian estimation method based on the Minnesota prior to obtain the posterior distribution of each parameter of the VAR model. Moreover, this paper uses methods based on Kalman filtering and Kalman smoothing to obtain the posterior distribution of latent state variables. Then, according to the posterior distribution of the VAR model parameters and the posterior distribution of the latent state variables, this paper uses the Gibbs sampling method to obtain the mixed Bayes vector autoregressive model and the estimation of the state variables. Finally, this article studies the influence of Internet finance on monetary policy with examples. The research results show that the method proposed in this article has a certain effect.

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