Unraveling the Coupling between Conformational Changes and Ligand Binding in Ribose Binding Protein Using Multiscale Molecular Dynamics and Free-Energy Calculations

Ren, Weitong; Dokainish, Hisham; Shinobu, Ai; Oshima, Hiraku; Sugita, Yuji

doi:10.1021/acs.jpcb.0c11600

Cited by 8 publications

(8 citation statements)

References 73 publications

(169 reference statements)

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“…Finally, both modes triggered in BGT upon UDP binding correspond to the hinge-bending motion (Supporting Information Figure S3E), agreeing with the deduction made using crystallographic structures in apo and holo states . The excellent correspondence between the set of slow normal modes triggered upon ligand binding identified here and the direction of actual conformational change, as determined from the knowledge of the experimental crystal structure − and other simulation methods, ,,, establishes their utility in our protocol for the deduction of the protein conformational change. Also, since only a small number of modes were found sufficient, this can be a viable route in the absence of the knowledge of the end state for selecting a suitable low-dimensional CV set for other enhanced sampling methods.…”

Section: Resultssupporting

confidence: 80%

Computation of the Protein Conformational Transition Pathway on Ligand Binding by Linear Response-Driven Molecular Dynamics

Punia

Goel

2022

J. Chem. Theory Comput.

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While extremely important for relating the protein structure to its biological function, determination of the protein conformational transition pathway upon ligand binding is made difficult due to the transient nature of intermediates, a large and rugged conformational space, and coupling between protein dynamics and ligand−protein interactions. Existing methods that rely on prior knowledge of the bound (holo) state structure are restrictive. A second concern relates to the correspondence of intermediates obtained to the metastable states on the apo → holo transition pathway. Here, we have taken the protein apo structure and ligand-binding site as only inputs and combined an elastic network model (ENM) representation of the protein Hamiltonian with linear response theory (LRT) for protein−ligand interactions to identify the set of slow normal modes of protein vibrations that have a high overlap with the direction of the protein conformational change. The structural displacement along the chosen direction was performed using excited normal modes molecular dynamics (MDeNM) simulations rather than by the direct use of LRT. Herein, the MDeNM excitation velocity was optimized on-the-fly on the basis of its coupling to protein dynamics and ligand−protein interactions. Thus, a determined set of structures was validated against crystallographic and simulation data on four protein−ligand systems, namely, adenylate kinase− di(adenosine-5′)pentaphosphate, ribose binding protein−β-D-ribopyranose, DNA β-glucosyltransferase−uridine-5′-diphosphate, and G-protein α subunit−guanosine-5′-triphosphate, which present important differences in protein conformational heterogeneity, ligand binding mechanism, viz. induced-fit or conformational selection, extent, and nonlinearity in protein conformational changes upon ligand binding, and presence of allosteric effects. The obtained set of intermediates was used as an input to path metadynamics simulations to obtain the free energy profile for the apo → holo transition.

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Section: Resultssupporting

confidence: 80%

Computation of the Protein Conformational Transition Pathway on Ligand Binding by Linear Response-Driven Molecular Dynamics

Punia

Goel

2022

J. Chem. Theory Comput.

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“…It has been reported that both proteins induce large-amplitude domain motion between open and closed structures. In addition, it is difficult to promote each open–closed transition with cMD because it is a rare-event. , Therefore, we applied aMD-GERBIL to these proteins to sample their open–closed transitions with reasonable computational costs.…”

Section: Results and Discussionmentioning

confidence: 99%

“…In addition, it is difficult to promote each open−closed transition with cMD because it is a rare-event. 52,53 Therefore, we applied aMD-GERBIL to these proteins to sample their open−closed transitions with reasonable computational costs.…”

Section: Structural Validation By the G-factormentioning

confidence: 99%

Structural Validation by the G-Factor Properly Regulates Boost Potentials Imposed in Conformational Sampling of Proteins

Yasuda

Morita

Shigeta

et al. 2022

J. Chem. Inf. Model.

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Free energy landscapes (FELs) of proteins are indispensable for evaluating thermodynamic properties. Molecular dynamics (MD) simulation is a computational method for calculating FELs; however, conventional MD simulation frequently fails to search a broad conformational subspace due to its accessible timescale, which results in the calculation of an unreliable FEL. To search a broad subspace, an external bias can be imposed on a protein system, and biased sampling tends to cause a strong perturbation that might collapse the protein structures, indicating that the strength of the external bias should be properly regulated. This regulation can be challenging, and empirical parameters are frequently employed to impose an optimal bias. To address this issue, several methods regulate the external bias by referring to system energies. Herein, we focused on protein structural information for this regulation. In this study, a well-established structural indicator (the G-factor) was used to obtain structural information. Based on the G-factor, we proposed a scheme for regulating biased sampling, which is referred to as a G-factor-based external bias limiter (GERBIL). With GERBIL, the configurations were structurally validated by the G-factor during biased sampling. As an example of biased sampling, an accelerated MD (aMD) simulation was adopted in GERBIL (aMD-GERBIL), whereby the aMD simulation was repeatedly performed by increasing the strength of the boost potential. Furthermore, the configurations sampled by the aMD simulation were structurally validated by their G-factor values, and aMD-GERBIL stopped increasing the strength of the boost potential when the sampled configurations were regarded as low-quality (collapsed) structures. This structural validation is regarded as a “Brake” of the boost potential. For demonstrations, aMD-GERBIL was applied to globular proteins (ribose binding and maltose-binding proteins) to promote their large-amplitude open–closed transitions and successfully identify their domain motions.

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“…Hence, the antibody bindings of S-protein can be explained based on the conformer selection mechanism ( Csermely et al, 2010 ). The exploration of conformational diversity of S-protein together with binding free-energy calculations followed by docking simulation would provide valuable structural insights with possible antibody bindings ( Ren et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

The inherent flexibility of receptor binding domains in SARS-CoV-2 spike protein

Dokainish¹,

Mori³

et al. 2022

eLife

Self Cite

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Spike (S) protein is the primary antigenic target for neutralization and vaccine development for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It decorates the virus surface and undergoes large motions of its receptor binding domains (RBDs) to enter the host cell. Here, we observe Down, one-Up, one-Open, and two-Up-like structures in enhanced molecular dynamics simulations, and characterize the transition pathways via inter-domain interactions. Transient salt-bridges between RBDA and RBDC and the interaction with glycan at N343B support RBDA motions from Down to one-Up. Reduced interactions between RBDA and RBDB in one-Up induce RBDB motions toward two-Up. The simulations overall agree with cryo-EM structure distributions and FRET experiments and provide hidden functional structures, namely, intermediates along Down to one-Up transition with druggable cryptic pockets as well as one-Open with a maximum exposed RBD. The inherent flexibility of S-protein thus provides essential information for antiviral drug rational design or vaccine development.

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Unraveling the Coupling between Conformational Changes and Ligand Binding in Ribose Binding Protein Using Multiscale Molecular Dynamics and Free-Energy Calculations

Cited by 8 publications

References 73 publications

Computation of the Protein Conformational Transition Pathway on Ligand Binding by Linear Response-Driven Molecular Dynamics

Computation of the Protein Conformational Transition Pathway on Ligand Binding by Linear Response-Driven Molecular Dynamics

Structural Validation by the G-Factor Properly Regulates Boost Potentials Imposed in Conformational Sampling of Proteins

The inherent flexibility of receptor binding domains in SARS-CoV-2 spike protein

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