Modeling hydrogen exchange of proteins by a multiscale method*

Zhu, Wentao; Li, Wenfei; Wang, Wei

doi:10.1088/1674-1056/abe377

Cited by 3 publications

(3 citation statements)

References 64 publications

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“…The down-to-up conformational transitions of the S-proteins involve a high free energy barrier, and therefore, the related timescale for sampling such rare events is far beyond the capability of conventional MD simulations. , Although one can speed up the sampling of conformational transition events by introducing biasing potential toward the target state, it tends to distort the kinetics and leads to unphysical transition pathways . As a solution, Harada and Kitao developed a parallel cascade selection molecular dynamics (PaCS-MD) method based on a genetic-type algorithm, which can generate conformational transition pathways without applying biasing potential .…”

Section: Methodsmentioning

confidence: 99%

Activation Pathways and Free Energy Landscapes of the SARS-CoV-2 Spike Protein

Qian

Yang

et al. 2021

ACS Omega

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uses a spike protein (S-protein) to recognize the receptor protein ACE2 of human cells and initiate infection, during which the conformational transition of the S-protein from inactive (down) state to active (up) state is one of the key molecular events determining the infectivity but the underlying mechanism remains poorly understood. In this work, we investigated the activation pathways and free energy landscape of the S-protein of SARS-CoV-2 and compared with those of the closely related counterpart SARS-CoV using molecular dynamics simulations. Our results revealed a large difference between the activation pathways of the two S-proteins. The transition from inactive to an active state for the S-protein of SARS-CoV-2 is more cooperative, involving simultaneous disruptions of several key interfacial hydrogen bonds, and the transition encounters a much higher free energy barrier. In addition, the conformational equilibrium of the SARS-CoV-2 S-protein is more biased to the inactive state compared to that of the SARS-CoV S-protein, suggesting the transient feature of the active state before binding to the receptor protein of the host cell. The key interactions contributing to the difference of the activation pathways and free energy landscapes were discussed. The results provide insights into the molecular mechanism involved in the initial stage of the SARS-CoV-2 infection.

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Section: Methodsmentioning

confidence: 99%

Activation Pathways and Free Energy Landscapes of the SARS-CoV-2 Spike Protein

Qian

Yang

et al. 2021

ACS Omega

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“…Ideally, the molecular simulations using CG models, in which the degrees of freedom are much reduced and all-atom structures are not available, should be able to reproduce the results of all-atom simulations (e.g., free energy estimations) as close as possible. In addition, the accurate estimation of the residue-wise SASA values for CG structures may enable direct comparisons between the results of CG simulations of biomolecules and some experimental observables which rely on the solvent exposure extent of the residues. − For example, the nucleotide-wise protection factors from the hydroxyl radical footprinting experiment can be used to characterize the SASA values of the RNA backbone and therefore the structural features of the intermediate state involved in the folding or functioning processes, which can be well captured by CG simulations as shown in a recent work . Undoubtedly, the distinguished performances of DeepCGSA can drastically improve the accuracy and application scope of CG computational methods in the studies of three-dimensional structure predictions, docking, drug design, and molecular simulations of biological processes.…”

Section: Discussionmentioning

confidence: 96%

Accurate Estimation of Solvent Accessible Surface Area for Coarse-Grained Biomolecular Structures with Deep Learning

Dong

Gong

2021

J. Phys. Chem. B

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Coarse-grained (CG) models of biomolecules have been widely used in protein/ribonucleic acid (RNA) three-dimensional structure prediction, docking, drug design, and molecular simulations due to their superiority in computational efficiency. Most of these applications strongly depend on the reasonable estimation of solvation free energy, which requires the accurate calculation of solvent accessible surface area (SASA). Although algorithms for SASA calculations with all-atom protein and RNA structures have been well-established, accurately estimating the SASA based on CG structures is extremely challenging. In this work, we developed a deep learning-based SASA estimator (DeepCGSA), which can provide almost perfect SASA estimation based on CG structures of protein and RNA molecules. Extensive testing analysis showed that for three types of widely used CG protein models, including the Cα-based, Cα–Cβ, and Martini models, the correlation coefficients between the predicted values and the reference values can be as high as 0.95–0.99, which perform dramatically better than available methods. In addition, the new method can be used for CG RNA structures and unfolded protein structures with much improved accuracy. We anticipate that DeepCGSA will be highly useful in the protein/RNA structure prediction, drug design, and other applications, in which accurate estimations of SASA for CG biomolecular structures are critically important.

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“…[25][26][27] For large systems involving the time scale longer then microsecond, coarse-grained models are often useful due to its high sampling efficiency. [28][29][30] In this work, we used a coarse-grained model to describe the folding of substrate protein and the conformational changes of the molecular chaperone. In the coarse-grained model, each spherical bead represents a residue and it locates at the c α position.…”

Section: Coarse-grained Protein Modelmentioning

confidence: 99%

Effect of chaperone–client interaction strength on Hsp70-mediated protein folding

Zou¹,

Lu²,

Xu³

2023

Chinese Phys. B

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Protein folding in crowding cellular environment often relies on the assistance of various chaperones. Hsp70 is one of the most ubiquitous chaperones in cells. Previous studies showed that the chaperone-client interactions at the open state tend to remodel the protein folding energy landscape and direct the protein folding as a foldase. In this work, we further investigate how the chaperone-client interaction strength modulates the foldase function of Hsp70 by using molecular simulations. The results showed that the time of substrate folding (including the whole folding step and substrate release step) has a non-monotonic dependence on the interaction strength. With the increasing of the chaperone-client interaction strength, the folding time decreases first, and then increases. More detailed analysis showed that when the chaperone-client interaction is too strong, even small number of chaperones-client contacts can maintain the substrate bound with the chaperone. The sampling of the transient chaperones-client complex with sparse inter-molecule contacts makes the client protein have chance to access the misfolded state even it is bound with chaperone. The current results suggest that the interaction strength is an important factor controlling the Hsp70 chaperoning function.

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Modeling hydrogen exchange of proteins by a multiscale method*

Cited by 3 publications

References 64 publications

Activation Pathways and Free Energy Landscapes of the SARS-CoV-2 Spike Protein

Activation Pathways and Free Energy Landscapes of the SARS-CoV-2 Spike Protein

Accurate Estimation of Solvent Accessible Surface Area for Coarse-Grained Biomolecular Structures with Deep Learning

Effect of chaperone–client interaction strength on Hsp70-mediated protein folding

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