Efficient prediction of protein conformational pathways based on the hybrid elastic network model

Seo, Sangjae; Jang, Yunho; Qian, Pengfei; Liu, Wing Kam; Choi, Jae Boong; Lim, Byeong Soo; Kim, Moon Ki

doi:10.1016/j.jmgm.2013.10.009

Cited by 11 publications

(13 citation statements)

References 58 publications

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“…These results are again consistent with those reported earlier 48,51,54,87–91 and the computational cost to obtain those results is minimal. Figure 7c depicts the conformational variability of the complex using 32 highest-weight ClustENM conformers.…”

Section: Resultssupporting

confidence: 92%

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ClustENM: ENM-Based Sampling of Essential Conformational Space at Full Atomic Resolution

Kurkcuoglu

Bahar

Doruker

2016

J. Chem. Theory Comput.

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Accurate sampling of conformational space and, in particular, the transitions between functional substates has been a challenge in molecular dynamic (MD) simulations of large biomolecular systems. We developed an Elastic Network Model (ENM)-based computational method, ClustENM, for sampling large conformational changes of biomolecules with various sizes and oligomerization states. ClustENM is an iterative method that combines ENM with energy minimization and clustering steps. It is an unbiased technique, which requires only an initial structure as input, and no information about the target conformation. To test the performance of ClustENM, we applied it to six biomolecular systems: adenylate kinase (AK), calmodulin, p38 MAP kinase, HIV-1 reverse transcriptase (RT), triosephosphate isomerase (TIM), and the 70S ribosomal complex. The generated ensembles of conformers determined at atomic resolution show good agreement with experimental data (979 structures resolved by X-ray and/or NMR) and encompass the subspaces covered in independent MD simulations for TIM, p38, and RT. ClustENM emerges as a computationally efficient tool for characterizing the conformational space of large systems at atomic detail, in addition to generating a representative ensemble of conformers that can be advantageously used in simulating substrate/ligand-binding events.

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Section: Resultssupporting

confidence: 92%

“…Especially the mechanism of translocation still needs clarification. 87,88 Several methods have been used to investigate ribosome dynamics including ENM, 48,51,54,89,90 coarse-grained MD 91 and MD using a multibasin structure-based model. 87 …”

Section: Resultsmentioning

confidence: 99%

ClustENM: ENM-Based Sampling of Essential Conformational Space at Full Atomic Resolution

Kurkcuoglu

Bahar

Doruker

2016

J. Chem. Theory Comput.

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“…Importance sampling and steered MD can help study rare events such as visiting high‐energy intermediates, which revealed the (un‐)zipping of Adenylate Kinase . Normal mode analysis (NMA) and elastic network models (ENM) approximate the underlying force‐field with quadratic potentials, at various resolutions of pairwise interactions in Cartesian or internal coordinates …”

Section: Introductionmentioning

confidence: 99%

“…Adenylate Kinase. 14 Normal mode analysis (NMA 15,16 ) and elastic network models (ENM 17,18 ) approximate the underlying force-field with quadratic potentials, at various resolutions of pairwise interactions 19 in Cartesian or internal coordinates. 20,21 Sampling-based exploration of conformation space similarly has made strides in bridging the gap between efficiency and accuracy in analysis of structural dynamics.…”

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confidence: 99%

Frustration-guided motion planning reveals conformational transitions in proteins

et al. 2017

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Proteins exist as conformational ensembles, exchanging between substates to perform their function. Advances in experimental techniques yield unprecedented access to structural snapshots of their conformational landscape. However, computationally modeling how proteins use collective motions to transition between substates is challenging owing to a rugged landscape and large energy barriers. Here, we present a new, robotics-inspired motion planning procedure called dCC-RRT that navigates the rugged landscape between substates by introducing dynamic, interatomic constraints to modulate frustration. The constraints balance non-native contacts and flexibility, and instantaneously redirect the motion towards sterically favorable conformations. On a test set of eight proteins determined in two conformations separated by, on average, 7.5 Å root mean square deviation (RMSD), our pathways reduced the Cα atom RMSD to the goal conformation by 78%, outperforming peer methods. We then applied dCC-RRT to examine how collective, small-scale motions of four side-chains in the active site of cyclophilin A propagate through the protein. dCC-RRT uncovered a spatially contiguous network of residues linked by steric interactions and collective motion connecting the active site to a recently proposed, non-canonical capsid binding site 25 Å away, rationalizing NMR and multi-temperature crystallography experiments. In all, dCC-RRT can reveal detailed, all-atom molecular mechanisms for small and large amplitude motions. Source code and binaries are freely available at https://github.com/ExcitedStates/KGS/.

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“…The interpolated ENM (iENM) was proposed by constructing a double-well potential function from the ENMs of two end-point structures [ 27 ]. To improve conventional coarse-grained ENM-based methods without the loss of physical reality, the hybrid ENI considers the rigidity information of conformation when generating transition pathways [ 28 ]. In addition, the ANMPathway used two end-point ENMs and constructed two-state potential resulting in a cusp hypersurface [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Normal mode-guided transition pathway generation in proteins

et al. 2017

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The biological function of proteins is closely related to its structural motion. For instance, structurally misfolded proteins do not function properly. Although we are able to experimentally obtain structural information on proteins, it is still challenging to capture their dynamics, such as transition processes. Therefore, we need a simulation method to predict the transition pathways of a protein in order to understand and study large functional deformations. Here, we present a new simulation method called normal mode-guided elastic network interpolation (NGENI) that performs normal modes analysis iteratively to predict transition pathways of proteins. To be more specific, NGENI obtains displacement vectors that determine intermediate structures by interpolating the distance between two end-point conformations, similar to a morphing method called elastic network interpolation. However, the displacement vector is regarded as a linear combination of the normal mode vectors of each intermediate structure, in order to enhance the physical sense of the proposed pathways. As a result, we can generate more reasonable transition pathways geometrically and thermodynamically. By using not only all normal modes, but also in part using only the lowest normal modes, NGENI can still generate reasonable pathways for large deformations in proteins. This study shows that global protein transitions are dominated by collective motion, which means that a few lowest normal modes play an important role in this process. NGENI has considerable merit in terms of computational cost because it is possible to generate transition pathways by partial degrees of freedom, while conventional methods are not capable of this.

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Efficient prediction of protein conformational pathways based on the hybrid elastic network model

Cited by 11 publications

References 58 publications

ClustENM: ENM-Based Sampling of Essential Conformational Space at Full Atomic Resolution

ClustENM: ENM-Based Sampling of Essential Conformational Space at Full Atomic Resolution

Frustration-guided motion planning reveals conformational transitions in proteins

Normal mode-guided transition pathway generation in proteins

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