Protocol To Make Protein NMR Structures Amenable to Stable Long Time Scale Molecular Dynamics Simulations

2014 ◽  
Vol 10 (4) ◽  
pp. 1781-1787 ◽  
Author(s):  
Da-Wei Li ◽  
Rafael Brüschweiler
2019 ◽  
Vol 10 (35) ◽  
pp. 8100-8107 ◽  
Author(s):  
Julia Westermayr ◽  
Michael Gastegger ◽  
Maximilian F. S. J. Menger ◽  
Sebastian Mai ◽  
Leticia González ◽  
...  

Machine learning enables excited-state molecular dynamics simulations including nonadiabatic couplings on nanosecond time scales.


2015 ◽  
Vol 137 (20) ◽  
pp. 6506-6516 ◽  
Author(s):  
Lorenzo Sborgi ◽  
Abhinav Verma ◽  
Stefano Piana ◽  
Kresten Lindorff-Larsen ◽  
Michele Cerminara ◽  
...  

2014 ◽  
Vol 169 ◽  
pp. 119-142 ◽  
Author(s):  
Matthieu Dreher ◽  
Jessica Prevoteau-Jonquet ◽  
Mikael Trellet ◽  
Marc Piuzzi ◽  
Marc Baaden ◽  
...  

The amount of data generated by molecular dynamics simulations of large molecular assemblies and the sheer size and complexity of the systems studied call for new ways to analyse, steer and interact with such calculations. Traditionally, the analysis is performed off-line once the huge amount of simulation results have been saved to disks, thereby stressing the supercomputer I/O systems, and making it increasingly difficult to handle post-processing and analysis from the scientist's office. The ExaViz framework is an alternative approach developed to couple the simulation with analysis tools to process the data as close as possible to their source of creation, saving a reduced, more manageable and pre-processed data set to disk. ExaViz supports a large variety of analysis and steering scenarios. Our framework can be used for live sessions (simulations short enough to be fully followed by the user) as well as batch sessions (long-time batch executions). During interactive sessions, at runtime, the user can display plots from analysis, visualise the molecular system and steer the simulation with a haptic device. We also emphasise how a CAVE-like immersive environment could be used to leverage such simulations, offering a large display surface to view and intuitively navigate the molecular system.


2021 ◽  
Author(s):  
Markus Schneider ◽  
Iris Antes

Computational methods play a key role for investigating allosteric mechanisms in proteins, with the potential of generating valuable insights for innovative drug design. Here we present the SenseNet ("Structure ENSEmble NETworks") framework for analysis of protein structure networks, which differs from established network models by focusing on interaction timelines obtained by molecular dynamics simulations. This approach is evaluated by predicting allosteric residues reported by NMR experiments in the PDZ2 domain of hPTP1e, a reference system for which previous computational predictions have shown considerable variance. We applied two models based on the mutual information between interaction timelines to estimate the conformational influence of each residue on its local environment. In terms of accuracy our prediction model is comparable to the top performing model published for this system, but by contrast benefits from its independence from NMR structures. Our results are complementary to experimental data and the consensus of previous predictions, demonstrating the potential of our new analysis tool SenseNet. Biochemical interpretation of our model suggests that allosteric residues in the PDZ2 domain form two distinct clusters of contiguous sidechain surfaces. SenseNet is provided as a plugin for the network analysis software Cytoscape, allowing for ease of future application and contributing to a system of compatible tools bridging the fields of system and structural biology.


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