Ligand Binding Introduces Significant Allosteric Shifts in the Locations of Protein Fluctuations

Allostery is usually considered to be a mechanism for transmission of signals associated with physical or dynamic changes in some part of a protein. Here, we investigate the changes in fluctuations across the protein upon ligand binding based on the fluctuations computed with elastic network models. These results suggest that binding reduces the fluctuations at the binding site but increases fluctuations at remote sites, but not to fully compensating extents. If there were complete conservation of entropy, then only the enthalpies of binding would matter and not the entropies; however this does not appear to be the case. Experimental evidence also suggests that energies and entropies of binding can compensate but that the extent of compensation varies widely from case to case. Our results do however always show transmission of an allosteric signal to distant locations where the fluctuations are increased. These fluctuations could be used to compute entropies to improve evaluations of the thermodynamics of binding. We also show the allosteric relationship between peptide binding in the GroEL trans-ring that leads directly to the release of GroES from the GroEL-GroES cis-ring. This finding provides an example of how calculating these changes to protein dynamics induced by the binding of an allosteric ligand can regulate protein function and mechanism.

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New Mechanistic Insights on Carbon Nanotubes’ Nanotoxicity Using Isolated Submitochondrial Particles, Molecular Docking, and Nano-QSTR Approaches

Biology ◽

10.3390/biology10030171 ◽

2021 ◽

Vol 10 (3) ◽

pp. 171

Author(s):

Michael González-Durruthy ◽

Riccardo Concu ◽

Juan M. Ruso ◽

M. Natália D. S. Cordeiro

Keyword(s):

Carbon Nanotubes ◽

Network Models ◽

Single Walled Carbon Nanotubes ◽

Docking Studies ◽

Fractal Surface ◽

Submitochondrial Particles ◽

Elastic Network ◽

Elastic Network Models ◽

Walled Carbon Nanotubes

Single-walled carbon nanotubes can induce mitochondrial F0F1-ATPase nanotoxicity through inhibition. To completely characterize the mechanistic effect triggering the toxicity, we have developed a new approach based on the combination of experimental and computational study, since the use of only one or few techniques may not fully describe the phenomena. To this end, the in vitro inhibition responses in submitochondrial particles (SMP) was combined with docking, elastic network models, fractal surface analysis, and Nano-QSTR models. In vitro studies suggest that inhibition responses in SMP of F0F1-ATPase enzyme were strongly dependent on the concentration assay (from 3 to 5 µg/mL) for both pristine and COOH single-walled carbon nanotubes types (SWCNT). Besides, both SWCNTs show an interaction inhibition pattern mimicking the oligomycin A (the specific mitochondria F0F1-ATPase inhibitor blocking the c-ring F0 subunit). Performed docking studies denote the best crystallography binding pose obtained for the docking complexes based on the free energy of binding (FEB) fit well with the in vitro evidence from the thermodynamics point of view, following an affinity order such as: FEB (oligomycin A/F0-ATPase complex) = −9.8 kcal/mol > FEB (SWCNT-COOH/F0-ATPase complex) = −6.8 kcal/mol ~ FEB (SWCNT-pristine complex) = −5.9 kcal/mol, with predominance of van der Waals hydrophobic nano-interactions with key F0-ATPase binding site residues (Phe 55 and Phe 64). Elastic network models and fractal surface analysis were performed to study conformational perturbations induced by SWCNT. Our results suggest that interaction may be triggering abnormal allosteric responses and signals propagation in the inter-residue network, which could affect the substrate recognition ligand geometrical specificity of the F0F1-ATPase enzyme in order (SWCNT-pristine > SWCNT-COOH). In addition, Nano-QSTR models have been developed to predict toxicity induced by both SWCNTs, using results of in vitro and docking studies. Results show that this method may be used for the fast prediction of the nanotoxicity induced by SWCNT, avoiding time- and money-consuming techniques. Overall, the obtained results may open new avenues toward to the better understanding and prediction of new nanotoxicity mechanisms, rational drug design-based nanotechnology, and potential biomedical application in precision nanomedicine.

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Edge-based formulation of elastic network models

Physical Review Research ◽

10.1103/physrevresearch.1.033211 ◽

2019 ◽

Vol 1 (3) ◽

Cited By ~ 1

Author(s):

Maxwell Hodges ◽

Sophia N. Yaliraki ◽

Mauricio Barahona

Keyword(s):

Network Models ◽

Elastic Network ◽

Elastic Network Models ◽

Edge Based

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Chapter 3 Fishing for Functional Motions with Elastic Network Models

Annual Reports in Computational Chemistry ◽

10.1016/s1574-1400(07)03003-4 ◽

2007 ◽

pp. 31-39

Author(s):

A.J. Rader

Keyword(s):

Network Models ◽

Elastic Network ◽

Elastic Network Models

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Coarse-Grained Models Reveal Functional Dynamics - I. Elastic Network Models – Theories, Comparisons and Perspectives

Bioinformatics and Biology Insights ◽

10.4137/bbi.s460 ◽

2008 ◽

Vol 2 ◽

pp. BBI.S460 ◽

Cited By ~ 21

Author(s):

Lee-Wei Yang ◽

Choon-Peng Chng

Keyword(s):

Conformational Changes ◽

Conformational Dynamics ◽

Computational Cost ◽

Network Models ◽

Coarse Graining ◽

Coarse Grained ◽

Elastic Network ◽

Elastic Network Models ◽

Time Space ◽

Functional Dynamics

In this review, we summarize the progress on coarse-grained elastic network models (CG-ENMs) in the past decade. Theories were formulated to allow study of conformational dynamics in time/space frames of biological interest. Several highlighted models and their underlined hypotheses are introduced in physical depth. Important ENM offshoots, motivated to reproduce experimental data as well as to address the slow-mode-encoded configurational transitions, are also introduced. With the theoretical developments, computational cost is significantly reduced due to simplified potentials and coarse-grained schemes. Accumulating wealth of data suggest that ENMs agree equally well with experiment in describing equilibrium dynamics despite their distinct potentials and levels of coarse-graining. They however do differ in the slowest motional components that are essential to address large conformational changes of functional significance. The difference stems from the dissimilar curvatures of the harmonic energy wells described for each model. We also provide our views on the predictability of ‘open to close’ (open→close) transitions of biomolecules on the basis of conformational selection theory. Lastly, we address the limitations of the ENM formalism which are partially alleviated by the complementary CG-MD approach, to be introduced in the second paper of this two-part series.

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