Fluctuation correlations as major determinants of structure- and dynamics-driven allosteric effects
文献情報
Miao Yu, Yixin Chen, Zi-Le Wang
Allosteric control is essential for regulating biological functions whereby stimuli such as ligand binding at one site on a protein cause a response at a distant functional site. Correlations between different sites in proteins have been used widely in identifying allosteric sites and pathways, and in designing allosteric drugs. However, the deterministic connection between correlations and allostery remains unsolved, especially considering that there are various types of correlations. Here, we combine perturbation-theory analysis and numerical calculations to study both structure- and dynamics-driven allosteric effects in an anisotropic network model (ANM). The results reveal that the allosteries are determined by the correlation (covariance) of distance fluctuations, but are irrelevant to the usual displacement correlations or time-delayed correlations. Dynamics-driven allostery is weaker than structure-driven allostery by at least one to two orders of magnitude. The intrinsic allostery capacity decays with distance by an exponential law, with the resulting characteristic distance parameter lying in the range of 7–10 Å for structure-driven allostery and 4–5 Å for dynamics-driven allostery. The importance of the cutoff distance of the ANM is also addressed.
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掲載誌
Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.









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