Density-cluster NMA: A new protein decomposition technique for coarse-grained normal mode analysis

2012 ◽  
Vol 80 (7) ◽  
pp. 1766-1779 ◽  
Author(s):  
Omar N. A. Demerdash ◽  
Julie C. Mitchell
Keyword(s):  
Normal Mode ◽  
Normal Mode Analysis ◽  
Coarse Grained ◽  
Mode Analysis ◽  
Decomposition Technique ◽  
New Protein

scholarly journals A force field for virtual atom molecular mechanics of proteins

2009 ◽  
Vol 106 (37) ◽  
pp. 15667-15672 ◽  
Author(s):  
Anil Korkut ◽  
Wayne A. Hendrickson
Keyword(s):  
Force Field ◽  
Molecular Mechanics ◽  
Normal Mode ◽  
Conformational Changes ◽  
Normal Mode Analysis ◽  
Coarse Grained ◽  
Mode Analysis ◽  
Biological Macromolecules ◽  
Molecular Mechanics Calculations ◽  
Low Degrees

Activities of many biological macromolecules involve large conformational transitions for which crystallography can specify atomic details of alternative end states, but the course of transitions is often beyond the reach of computations based on full-atomic potential functions. We have developed a coarse-grained force field for molecular mechanics calculations based on the virtual interactions of Cα atoms in protein molecules. This force field is parameterized based on the statistical distribution of the energy terms extracted from crystallographic data, and it is formulated to capture features dependent on secondary structure and on residue-specific contact information. The resulting force field is applied to energy minimization and normal mode analysis of several proteins. We find robust convergence in minimizations to low energies and energy gradients with low degrees of structural distortion, and atomic fluctuations calculated from the normal mode analyses correlate well with the experimental B-factors obtained from high-resolution crystal structures. These findings suggest that the virtual atom force field is a suitable tool for various molecular mechanics applications on large macromolecular systems undergoing large conformational changes.

A minimal coarse-grained model for the low-frequency normal mode analysis of icosahedral viral capsids

Soft Matter ◽  
2020 ◽  
Vol 16 (14) ◽  
pp. 3443-3455 ◽  
Author(s):  
M. Martín-Bravo ◽  
J. M. Gomez Llorente ◽  
J. Hernández-Rojas
Keyword(s):  
Structural Properties ◽  
Normal Mode ◽  
Normal Mode Analysis ◽  
Low Frequency ◽  
Coarse Grained ◽  
Mode Analysis ◽  
Viral Capsids ◽  
Mode Spectrum ◽  
Coarse Grained Model

A minimal coarse-grained model unveils relevant structural properties of icosahedral viral capsids when fitted to reproduce their low-frequency normal-mode spectrum.

scholarly journals A minimalist network model for coarse-grained normal mode analysis and its application to biomolecular x-ray crystallography

2008 ◽  
Vol 105 (40) ◽  
pp. 15358-15363 ◽  
Author(s):  
Mingyang Lu ◽  
Jianpeng Ma
Keyword(s):  
Normal Mode ◽  
Network Model ◽  
Normal Mode Analysis ◽  
Low Frequency ◽  
Coarse Grained ◽  
Mode Analysis ◽  
Conformational Sampling ◽  
Structural Refinement ◽  
X Ray ◽  
X Ray Crystallography

In this article, we report a method for coarse-grained normal mode analysis called the minimalist network model. The main features of the method are that it can deliver accurate low-frequency modes on structures without undergoing initial energy minimization and that it also retains the details of molecular interactions. The method does not require any additional adjustable parameters after coarse graining and is computationally very fast. Tests on modeling the experimentally measured anisotropic displacement parameters in biomolecular x-ray crystallography demonstrate that the method can consistently perform better than other commonly used methods including our own one. We expect this method to be effective for applications such as structural refinement and conformational sampling.

scholarly journals The NRGTEN Python package: an extensible toolkit for coarse-grained normal mode analysis of proteins, nucleic acids, small molecules and their complexes

2021 ◽  
Author(s):  
Olivier Mailhot ◽  
Rafael Najmanovich
Keyword(s):  
Small Molecules ◽  
Normal Mode ◽  
Chemical Nature ◽  
Normal Mode Analysis ◽  
Coarse Grained ◽  
Mode Analysis ◽  
Specific Chemical ◽  
Atomic Interactions ◽  
Analysis Models ◽  
Python Package

Abstract Summary The Najmanovich Research Group Toolkit for Elastic Networks (NRGTEN) is a Python toolkit that implements four different NMA models in addition to popular and novel metrics to benchmark and measure properties from these models. Furthermore, the toolkit is available as a public Python package and is easily extensible for the development or implementation of additional normal mode analysis models. The inclusion of the Elastic Network Contact Model developed in our group within NRGTEN is noteworthy, owing to its account for the specific chemical nature of atomic interactions. Availability and implementation https://github.com/gregorpatof/nrgten_package/.

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