Probing protein-protein interactions. The ribose-binding protein in bacterial transport and chemotaxis.

Bjorkman, A.J.; Binnie, R. A.; Zhang, H; Cole, L. B.; Hermodson, Mark A.; Mowbray, Sherry L.

doi:10.1016/s0021-9258(18)43798-2

Cited by 82 publications

(40 citation statements)

References 29 publications

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“…They transform each other around the hinge by a large bending motion. Understanding the mechanism of conformation changes, PBPs have been extensively explored by crystallography [ 1 , 2 , 3 , 4 , 5 ], NMR [ 6 , 7 , 8 ], MD simulation [ 9 , 10 , 11 ], Monte Carlo (MC) method [ 12 ], elastic network model [ 13 , 14 , 15 ] and other biophysical methods [ 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Ligand-induced structural changes analysis of ribose-binding protein as studied by molecular dynamics simulations

Cao

et al. 2021

THC

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BACKGROUND: The ribose-binding protein (RBP) from Escherichia coli is one of the representative structures of periplasmic binding proteins. Binding of ribose at the cleft between two domains causes a conformational change corresponding to a closure of two domains around the ligand. The RBP has been crystallized in the open and closed conformations. OBJECTIVE: With the complex trajectory as a control, our goal was to study the conformation changes induced by the detachment of the ligand, and the results have been revealed from two computational tools, MD simulations and elastic network models. METHODS: Molecular dynamics (MD) simulations were performed to study the conformation changes of RBP starting from the open-apo, closed-holo and closed-apo conformations. RESULTS: The evolution of the domain opening angle θ clearly indicates large structural changes. The simulations indicate that the closed states in the absence of ribose are inclined to transition to the open states and that ribose-free RBP exists in a wide range of conformations. The first three dominant principal motions derived from the closed-apo trajectories, consisting of rotating, bending and twisting motions, account for the major rearrangement of the domains from the closed to the open conformation. CONCLUSIONS: The motions showed a strong one-to-one correspondence with the slowest modes from our previous study of RBP with the anisotropic network model (ANM). The results obtained for RBP contribute to the generalization of robustness for protein domain motion studies using either the ANM or PCA for trajectories obtained from MD.

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Section: Introductionmentioning

confidence: 99%

Ligand-induced structural changes analysis of ribose-binding protein as studied by molecular dynamics simulations

Cao

et al. 2021

THC

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“…To prepare for the subsequent enhanced sampling, three MD simulations were performed, in as short as 100 ns each, for (1) the ligand-bound/closed form (termed herein as “holo”), (2) the ligand-unbound/closed form (termed herein simply as “close”), and (3) the ligand-unbound/open form (termed herein as “open”). The holo and open simulation models were taken from the crystal structures of the Protein data Bank (PDB) entries 2dri [ 28 ] and 1ba2 [ 29 ], respectively. The R67D point mutation in the holo form was converted into a wild type.…”

Section: Methodsmentioning

confidence: 99%

Multiscale Enhanced Sampling Using Machine Learning

Moritsugu

2021

Life

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Multiscale enhanced sampling (MSES) allows for an enhanced sampling of all-atom protein structures by coupling with the accelerated dynamics of the associated coarse-grained (CG) model. In this paper, we propose an MSES extension to replace the CG model with the dynamics on the reduced subspace generated by a machine learning approach, the variational autoencoder (VAE). The molecular dynamic (MD) trajectories of the ribose-binding protein (RBP) in both the closed and open forms were used as the input by extracting the inter-residue distances as the structural features in order to train the VAE model, allowing the encoded latent layer to characterize the difference in the structural dynamics of the closed and open forms. The interpolated data characterizing the RBP structural change in between the closed and open forms were thus efficiently generated in the low-dimensional latent space of the VAE, which was then decoded into the time-series data of the inter-residue distances and was useful for driving the structural sampling at an atomistic resolution via the MSES scheme. The free energy surfaces on the latent space demonstrated the refinement of the generated data that had a single basin into the simulated data containing two closed and open basins, thus illustrating the usefulness of the MD simulation together with the molecular mechanics force field in recovering the correct structural ensemble.

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“…In the ADK case, the open and closed structures are chain A in PDB entry 4AKE (4AKE:A) [ 29 ] and 1AKE:A [ 30 ], respectively. In the RBP case, the open and closed structures are 1BA2:A [ 31 ] and 2DRI:A [ 32 ], respectively. In addition, we used several experimental intermediate structures of ADK whose PDB code 1ZIN, 1ZIO, 1ZIP [ 33 ], and 1DVR [ 34 ] to experimentally evaluate the ICONGENI simulation results.…”

Section: Methodsmentioning

confidence: 99%

Protein conformational transitions explored by a morphing approach based on normal mode analysis in internal coordinates

Lee

Park

et al. 2021

PLoS ONE

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Large-scale conformational changes are essential for proteins to function properly. Given that these transition events rarely occur, however, it is challenging to comprehend their underlying mechanisms through experimental and theoretical approaches. In this study, we propose a new computational methodology called internal coordinate normal mode-guided elastic network interpolation (ICONGENI) to predict conformational transition pathways in proteins. Its basic approach is to sample intermediate conformations by interpolating the interatomic distance between two end-point conformations with the degrees of freedom constrained by the low-frequency dynamics afforded by normal mode analysis in internal coordinates. For validation of ICONGENI, it is applied to proteins that undergo open-closed transitions, and the simulation results (i.e., simulated transition pathways) are compared with those of another technique, to demonstrate that ICONGENI can explore highly reliable pathways in terms of thermal and chemical stability. Furthermore, we generate an ensemble of transition pathways through ICONGENI and investigate the possibility of using this method to reveal the transition mechanisms even when there are unknown metastable states on rough energy landscapes.

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Probing protein-protein interactions. The ribose-binding protein in bacterial transport and chemotaxis.

Cited by 82 publications

References 29 publications

Ligand-induced structural changes analysis of ribose-binding protein as studied by molecular dynamics simulations

Ligand-induced structural changes analysis of ribose-binding protein as studied by molecular dynamics simulations

Multiscale Enhanced Sampling Using Machine Learning

Protein conformational transitions explored by a morphing approach based on normal mode analysis in internal coordinates

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