Context-Driven Model Refinement

Improved Sampling Strategies for Protein Model Refinement based on Molecular Dynamics Simulation

10.26434/chemrxiv.13299197.v1 ◽

2020 ◽

Author(s):

Lim Heo ◽

Collin Arbour ◽

Michael Feig

Keyword(s):

Molecular Dynamics ◽

Molecular Dynamics Simulation ◽

Structure Prediction ◽

Protein Structures ◽

Conformational Space ◽

Dynamics Simulation ◽

Model Refinement ◽

Protein Model ◽

Lower Accuracy ◽

Simulation Based

Protein structures provide valuable information for understanding biological processes. Protein structures can be determined by experimental methods such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict protein structures. Those methods utilize protein structure databases for structure prediction via template-based modeling or for training machine-learning models to generate predictions. Structure prediction for proteins distant from proteins with known structures often results in lower accuracy with respect to the true physiological structures. Physics-based protein model refinement methods can be applied to improve model accuracy in the predicted models. Refinement methods rely on conformational sampling around the predicted structures, and if structures closer to the native states are sampled, improvements in the model quality become possible. Molecular dynamics simulations have been especially successful for improving model qualities but although consistent refinement can be achieved, the improvements in model qualities are still moderate. To extend the refinement performance of a simulation-based protocol, we explored new schemes that focus on an optimized use of biasing functions and the application of increased simulation temperatures. In addition, we tested the use of alternative initial models so that the simulations can explore conformational space more broadly. Based on the insight of this analysis we are proposing a new refinement protocol that significantly outperformed previous state-of-the-art molecular dynamics simulation-based protocols in the benchmark tests described here. <br>

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Using field observations to inform thermal hydrology models of permafrost dynamics with ATS (v0.83)

Geoscientific Model Development Discussions ◽

10.5194/gmdd-8-3235-2015 ◽

2015 ◽

Vol 8 (4) ◽

pp. 3235-3292 ◽

Cited By ~ 5

Author(s):

A. L. Atchley ◽

S. L. Painter ◽

D. R. Harp ◽

E. T. Coon ◽

C. J. Wilson ◽

...

Keyword(s):

Carbon Sink ◽

Thermal Response ◽

Field Measurements ◽

Early Summer ◽

Active Layer Thickness ◽

Earth System ◽

Model Refinement ◽

System Models ◽

Developed Surface ◽

Earth System Models

Abstract. Climate change is profoundly transforming the carbon-rich Arctic tundra landscape, potentially moving it from a carbon sink to a carbon source by increasing the thickness of soil that thaws on a seasonal basis. However, the modeling capability and precise parameterizations of the physical characteristics needed to estimate projected active layer thickness (ALT) are limited in Earth System Models (ESMs). In particular, discrepancies in spatial scale between field measurements and Earth System Models challenge validation and parameterization of hydrothermal models. A recently developed surface/subsurface model for permafrost thermal hydrology, the Advanced Terrestrial Simulator (ATS), is used in combination with field measurements to calibrate and identify fine scale controls of ALT in ice wedge polygon tundra in Barrow, Alaska. An iterative model refinement procedure that cycles between borehole temperature and snow cover measurements and simulations functions to evaluate and parameterize different model processes necessary to simulate freeze/thaw processes and ALT formation. After model refinement and calibration, reasonable matches between simulated and measured soil temperatures are obtained, with the largest errors occurring during early summer above ice wedges (e.g. troughs). The results suggest that properly constructed and calibrated one-dimensional thermal hydrology models have the potential to provide reasonable representation of the subsurface thermal response and can be used to infer model input parameters and process representations. The models for soil thermal conductivity and snow distribution were found to be the most sensitive process representations. However, information on lateral flow and snowpack evolution might be needed to constrain model representations of surface hydrology and snow depth.

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Model Refinement Learning and an Example on Channel Estimation With Universal Noise Model

IEEE Journal on Selected Areas in Communications ◽

10.1109/jsac.2020.3036964 ◽

2021 ◽

Vol 39 (1) ◽

pp. 31-46

Author(s):

Hui-Ming Wang ◽

Jia-Cheng Jiang ◽

Yu-Ning Wang

Keyword(s):

Channel Estimation ◽

Noise Model ◽

Model Refinement

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Rapid protein model refinement by deep learning

Nature Computational Science ◽

10.1038/s43588-021-00104-0 ◽

2021 ◽

Author(s):

Osama Abdin ◽

Philip M. Kim

Keyword(s):

Deep Learning ◽

Model Refinement ◽

Protein Model

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Program construction for macromolecule atomic model refinement based on the fast Fourier transform and fast differentiation algorithms; erratum

Acta Crystallographica Section A Foundations of Crystallography ◽

10.1107/s0108767386099889 ◽

1986 ◽

Vol 42 (1) ◽

pp. 57-57

Author(s):

V. Yu. Lunin ◽

A. G. Urzhumstev

Keyword(s):

Fourier Transform ◽

Fast Fourier Transform ◽

Atomic Model ◽

Model Refinement ◽

Acta Cryst ◽

Program Construction

In Lunin & Urzhumtsev [Acta Cryst. (1985), A41, 327-333] references to Lifshitz (Agarwal, 1981) on pages 327 and 329 should be amended to Lifchitz (Agarwal, 1981).

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