Abstract
In the last years, we have been observing remarkable improvements in the field of protein dynamics. Indeed, we can now study protein dynamics in atomistic details over several timescales with a rich portfolio of experimental and computational techniques. On one side, this provides us with the possibility to validate simulation methods and physical models against a broad range of experimental observables. On the other side, it also allows a complementary and comprehensive view on protein structure and dynamics. What is needed now is a better understanding of the link between the dynamic properties that we observe and the functional properties of these important cellular machines. To make progresses in this direction, we need to improve the physical models used to describe proteins and solvent in molecular dynamics, as well as to strengthen the integration of experiments and simulations to overcome their own limitations. Moreover, now that we have the means to study protein dynamics in great details, we need new tools to understand the information embedded in the protein ensembles and in their dynamic signature. With this aim in mind, we should enrich the current tools for analysis of biomolecular simulations with attention to the effects that can be propagated over long distances and are often associated to important biological functions. In this context, approaches inspired by network analysis can make an important contribution to the analysis of molecular dynamics simulations.
Highlights
Reviewed by: Javier Arsuaga, University of California at Davis, USA Chang-Chun Ling, University of Calgary, Canada
That we have the means to study protein dynamics in great details, we need new tools to understand the information embedded in the protein ensembles and in their dynamic signature
Thirty-eight years are passed since the first molecular dynamics (MD) simulation of a biomolecule, i.e., the bovine pancreatic trypsin inhibitor, was published (McCammon et al, 1977) and even if that was a short and in vacuum simulation, that work has a remarkable impact on the way in which we look at biomolecular structures
Summary
We can study protein dynamics in atomistic details over several timescales with a rich portfolio of experimental and computational techniques On one side, this provides us with the possibility to validate simulation methods and physical models against a broad range of experimental observables. That we have the means to study protein dynamics in great details, we need new tools to understand the information embedded in the protein ensembles and in their dynamic signature. With this aim in mind, we should enrich the current tools for analysis of biomolecular simulations with attention to the effects that can be propagated over long distances and are often associated to important biological functions.
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