Mapping energy transport networks in proteins
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Authors
Leitner, David M.
Yamato, Takahisa
Issue Date
2018
Type
Book Chapter
Language
Keywords
Alternative Title
Abstract
The response of proteins to chemical reactions or impulsive excitation that occurs within
the molecule has fascinated chemists for decades.1-3
In recent years ultrafast X-ray
studies have provided ever more detailed information about the evolution of protein
structural change following ligand photolysis,4-5 and time-resolved IR and Raman
techniques, e.g., have provided detailed pictures of the nature and rate of energy transport
in peptides and proteins,6-11 including recent advances in identifying transport through
individual amino acids of several heme proteins.12-14 Computational tools to locate
energy transport pathways in proteins have also been advancing.
15 Energy transport
pathways in proteins have since some time been identified by molecular dynamics (MD)
simulations,
16-17 and more recent efforts have focused on the development of coarse
graining approaches,
18-29 some of which have exploited analogies to thermal transport in
other molecular materials.
30-35 With the identification of pathways in proteins and protein
2
complexes, network analysis has been applied to locate residues that control protein
dynamics and possibly allostery,36-38 where chemical reactions at one binding site
mediate reactions at distance sites of the protein.
39-61 In this chapter we review
approaches for locating computationally energy transport networks in proteins. We
present background into energy and thermal transport in condensed phase and
macromolecules that underlies the approaches we discuss before turning to a description
of the approaches themselves. We also illustrate the application of the computational
methods for locating energy transport networks and simulating energy dynamics in
proteins with several examples.
Description
This preprint was submitted to Reviews in Computational Chemistry. The final version will be published as a chapter in Reviews in Computational Chemistry volume 31.