Normal‐mode driven exploration of protein domain motions

Sanejouand, Yves‐Henri

doi:10.1002/jcc.26755

Cited by 3 publications

(3 citation statements)

References 78 publications

(89 reference statements)

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“…They observed larger motions with their method than to free MD, respecting the overall secondary structure content of T4L. In a recent study 67 , ENM modes are used as geometry constraints to explore large protein motions, enabling the protein to accurately “jump” between macrostates described by the combination of a few modes. The results for T4L showed that the combination of the 3 lowest-frequency modes is required to describe the transition between the PDBs 177L (closed) to 178L (open) states.…”

Section: Resultsmentioning

confidence: 99%

“…Due to the stochasticity of thermal motions, NM eigenvectors move away from the original directions when used to displace the protein, since the structure evolves into other potential energy wells. Therefore, the displacement along the modes is valid for small distances, but the displacement along greater distances may deform the structure of the protein if no care is taken 67 . Many methods were recently developed to took advantage of low-frequency motions to accelerate molecular dynamics simulations and enhance protein sampling.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, the displacement along the modes is valid for small distances, but the displacement along greater distances may deform the structure of the protein if no care is taken 67 . Many methods were recently developed to took advantage of low-frequency motions to accelerate molecular dynamics simulations and enhance protein sampling.…”

Section: Mgt: Broader Sampling Of Globular Domain and Tm Helix Stablementioning

confidence: 99%

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Adaptive collective motions: a hybrid method to improve conformational sampling with molecular dynamics and normal modes

Resende-Lara

Costa

Dudas

et al. 2022

Preprint

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Protein function is closely related to its structure and dynamics. Due to its large number of degrees of freedom, proteins adopt a large number of conformations, which describe a highly complex potential energy landscape. Considering the huge ensemble of conformations in dynamic equilibrium in solution, detailed investigation of proteins dynamics is extremely costly. Therefore, a significant number of different methods have emerged in order to improve the conformational sampling of biomolecules. One of these methods is Molecular Dynamics with excited Normal Modes (MDeNM) in which normal modes are used as collective variables in molecular dynamics. Here, we present a new implementation of the MDeNM method that allows a continuously controlled kinetic excitation energy in the normal mode space, while taking into account the natural constraints imposed either by the structure or the environment. These implementations prevent unphysical structural distortions. We tested the new approach on bacteriophage's T4 lysozyme, Gallus gallus hen egg-white lysozyme and Staphylococcus aureus membrane-bound transglycosylase. Our results showed that the new approach outperformed free MD sampling and preserved the structural features comparatively to the original MDeNM approach. We also observed that by adaptively changing the excitation direction during calculations, proteins follow new transition paths preventing structural distortions.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Mgt: Broader Sampling Of Globular Domain and Tm Helix Stablementioning

confidence: 99%

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Adaptive collective motions: a hybrid method to improve conformational sampling with molecular dynamics and normal modes

Resende-Lara

Costa

Dudas

et al. 2022

Preprint

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Protein Fluctuations in Response to Random External Forces

2022

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Elastic network models (ENMs) have been widely used in the last decades to investigate protein motions and dynamics. There the intrinsic fluctuations based on the isolated structures are obtained from the normal modes of these elastic networks, and they generally show good agreement with the B-factors extracted from X-ray crystallographic experiments, which are commonly considered to be indicators of protein flexibility. In this paper, we propose a new approach to analyze protein fluctuations and flexibility, which has a more appropriate physical basis. It is based on the application of random forces to the protein ENM to simulate the effects of collisions of solvent on a protein structure. For this purpose, we consider both the Cα-atom coarse-grained anisotropic network model (ANM) and an elastic network augmented with points included for the crystallized waters. We apply random forces to these protein networks everywhere, as well as only on the protein surface alone. Despite the randomness of the directions of the applied perturbations, the computed average displacements of the protein network show a remarkably good agreement with the experimental B-factors. In particular, for our set of 919 protein structures, we find that the highest correlation with the B-factors is obtained when applying forces to the external surface of the water-augmented ANM (an overall gain of 3% in the Pearson’s coefficient for the entire dataset, with improvements up to 30% for individual proteins), rather than when evaluating the fluctuations obtained from the normal modes of a standard Cα-atom coarse-grained ANM. It follows that protein fluctuations should be considered not just as the intrinsic fluctuations of the internal dynamics, but also equally well as responses to external solvent forces, or as a combination of both.

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Low-Frequency Harmonic Perturbations Drive Protein Conformational Changes

Scaramozzino

Piana

Lacidogna

et al. 2021

IJMS

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Protein dynamics has been investigated since almost half a century, as it is believed to constitute the fundamental connection between structure and function. Elastic network models (ENMs) have been widely used to predict protein dynamics, flexibility and the biological mechanism, from which remarkable results have been found regarding the prediction of protein conformational changes. Starting from the knowledge of the reference structure only, these conformational changes have been usually predicted either by looking at the individual mode shapes of vibrations (i.e., by considering the free vibrations of the ENM) or by applying static perturbations to the protein network (i.e., by considering a linear response theory). In this paper, we put together the two previous approaches and evaluate the complete protein response under the application of dynamic perturbations. Harmonic forces with random directions are applied to the protein ENM, which are meant to simulate the single frequency-dependent components of the collisions of the surrounding particles, and the protein response is computed by solving the dynamic equations in the underdamped regime, where mass, viscous damping and elastic stiffness contributions are explicitly taken into account. The obtained motion is investigated both in the coordinate space and in the sub-space of principal components (PCs). The results show that the application of perturbations in the low-frequency range is able to drive the protein conformational change, leading to remarkably high values of direction similarity. Eventually, this suggests that protein conformational change might be triggered by external collisions and favored by the inherent low-frequency dynamics of the protein structure.

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Normal‐mode driven exploration of protein domain motions

Cited by 3 publications

References 78 publications

Adaptive collective motions: a hybrid method to improve conformational sampling with molecular dynamics and normal modes

Adaptive collective motions: a hybrid method to improve conformational sampling with molecular dynamics and normal modes

Protein Fluctuations in Response to Random External Forces

Low-Frequency Harmonic Perturbations Drive Protein Conformational Changes

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