A post-process to estimate an approximated minimal free energy path based on local centroids

Morita, Rikuri; Shigeta, Yasuteru; Harada, Ryuhei

doi:10.1016/j.cplett.2021.139003

Cited by 4 publications

(9 citation statements)

References 25 publications

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“…In addition to the 2D FELs, the one-dimensional (1D) FELs were calculated for the RBP and MBP along the grids of the strings used in this path-sampling method (Figure c,d). Since the crystal structure (1BA2) is an open mutant, the free-energy value of 1BA2 was slightly higher than that of 1URP, as reported in a previous study . For further analysis, the open and closed states of the RBP were clearly separated along the 1D free energy profile as a double-well shape (Figure c), while that of the MBP was not clearly separated because the free energy profile of the MBP monotonically increased from the open to closed state (Figure d).…”

Section: Results and Discussionsupporting

confidence: 56%

“…2D FELs as a function of PC1 and PC2 for (a) RBP and (b) MBP. On both 2D FELs, each minimum free-energy pathway (MFEP) was sampled by the post-string method . Each MFEP is represented as a string consisting of yellow beads on each 2D FEL.…”

Section: Results and Discussionmentioning

confidence: 99%

“…Next, the minimum free-energy paths (MFEPs) between the open and closed crystal structures of the RBP and MBP were searched using a path sampling method . This path sampling method allowed for the search for an MFEP on a preliminarily calculated FEL.…”

Section: Results and Discussionmentioning

confidence: 99%

“…Since the crystal structure (1BA2) 40 is an open mutant, the freeenergy value of 1BA2 was slightly higher than that of 1URP, as reported in a previous study. 61 For further analysis, the open and closed states of the RBP were clearly separated along the 1D free energy profile as a double-well shape (Figure 6c), while that of the MBP was not clearly separated because the free energy profile of the MBP monotonically increased from the open to closed state (Figure 6d). Both 1D free energy profiles indicated that the RBP tended to make a clear transition between the open and closed states, but the MBP fluctuated around the open state without showing a clear open−closed transition; therefore, the structural properties of both proteins might differ.…”

Section: Msm Constructionmentioning

confidence: 99%

“…Next, the minimum free-energy paths (MFEPs) between the open and closed crystal structures of the RBP and MBP were searched using a path sampling method. 61 This path sampling method allowed for the search for an MFEP on a preliminarily calculated FEL. Here, the open−closed transition paths of both proteins were searched for MFEPs on each 2D FEL using this path sampling method.…”

Section: Msm Constructionmentioning

confidence: 99%

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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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Section: Results and Discussionsupporting

confidence: 56%

Section: Results and Discussionmentioning

confidence: 99%

Section: Results and Discussionmentioning

confidence: 99%

Section: Msm Constructionmentioning

confidence: 99%

Section: Msm Constructionmentioning

confidence: 99%

See 3 more Smart Citations

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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Determination of the Association between Mesotrione Sensitivity and Conformational Change of 4-Hydroxyphenylpyruvate Dioxygenase via Free-Energy Analyses

Munei

Hengphasatporn

Hori

et al. 2023

J. Agric. Food Chem.

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One widely known herbicide target is 4-hydroxyphenylpyruvate dioxygenase (HPPD). Avena sativa HPPD is less sensitive to mesotrione (herbicide) than Arabidopsis thaliana HPPD. HPPD inhibitor-sensitivity is governed by the dynamic behavior of the C-terminal α-helix of HPPD (H11) in closed and open forms. However, the specific relationship between the plant inhibitor sensitivity and H11 dynamic behavior remains unclear. Herein, we determined the conformational changes in H11 to understand the inhibitor−sensitivity mechanism based on free-energy calculations using molecular dynamics simulations. The calculated free-energy landscapes revealed that Arabidopsis thaliana HPPD preferred the open form of H11 in the apo form and the closed-like form in complex with mesotrione, whereas Avena sativa HPPD exhibited the opposite tendency. We also identified some important residues involved in the dynamic behavior of H11. Therefore, inhibitor sensitivity is governed by indirect interactions due to the protein flexibility caused by the conformational changes of H11.

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Conformational flexibility driving charge-selective substrate translocation across a bacterial transporter

Vikraman,

Majumdar,

et al. 2024

Chem. Sci.

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A post-process to estimate an approximated minimal free energy path based on local centroids

Cited by 4 publications

References 25 publications

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

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

Determination of the Association between Mesotrione Sensitivity and Conformational Change of 4-Hydroxyphenylpyruvate Dioxygenase via Free-Energy Analyses

Conformational flexibility driving charge-selective substrate translocation across a bacterial transporter

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