Transition Of Adenylate Kinase Research Articles

Conformational Changes of Protein Analyzed Based on Structural Perturbation Method

Proteins perform their biological functions through their conformational changes due to ligand-binding. Though atomistic simulations have allowed for understanding the conformational dynamics of proteins, they are computationally restrictive in revealing the conformational transition pathway of proteins. In this work, we consider an elastic network model (ENM) with introducing the structural perturbation method, which mimics the breakage or formation of native contacts during the conformational changes, for gaining insight into the conformational transition pathway of proteins. It is shown that ENM with structural perturbation method enables the characterization of the conformational transition of adenylate kinase as a model protein. In addition, the low-frequency normal modes of adenylate kinase are found to play a role in its conformational transition. Our study sheds light on ENM with structural perturbation method for studying the conformational transitions of large protein complexes.

Effects of Catalytic Action and Ligand Binding on Conformational Ensembles of Adenylate Kinase.

Crystal structures of adenylate kinase (AdK) from Escherichia coli capture two states: an "open" conformation (apo) obtained in the absence of ligands and a "closed" conformation in which ligands are bound. Other AdK crystal structures suggest intermediate conformations that may lie on the transition pathway between these two states. To characterize the transition from open to closed states in solution, X-ray solution scattering data were collected from AdK in the apo form and with progressively increasing concentrations of five different ligands. Scattering data from apo AdK are consistent with scattering predicted from the crystal structure of AdK in the open conformation. In contrast, data from AdK samples saturated with Ap5A do not agree with that calculated from AdK in the closed conformation. Using cluster analysis of available structures, we selected representative structures in five conformational states: open, partially open, intermediate, partially closed, and closed. We used these structures to estimate the relative abundances of these states for each experimental condition. X-ray solution scattering data obtained from AdK with AMP are dominated by scattering from AdK in the open conformation. For AdK in the presence of high concentrations of ATP and ADP, the conformational ensemble shifts to a mixture of partially open and closed states. Even when AdK is saturated with Ap5A, a significant proportion of AdK remains in a partially open conformation. These results are consistent with an induced-fit model in which the transition of AdK from an open state to a closed state is initiated by ATP binding.

Quantifying Macromolecular Transition Paths with Path Similarity Analysis

Diverse classes of proteins function through large-scale conformational changes; sophisticated enhanced sampling methods have been proposed to generate these macromolecular transition paths. As such paths are curves in a high-dimensional space, they have been difficult to compare quantitatively, a prerequisite to, for instance, assess the quality of different sampling algorithms. The Path Similarity Analysis (PSA) approach [1] alleviates these difficulties without relying on low-dimensional projections by utilizing the full information in 3N-dimensional trajectories in configuration space. PSA employs the Hausdorff or Frechet path (distance) metrics—adopted from computational geometry—enabling us to quantify path (dis)similarity, while the new concept of a Hausdorff-pair map permits the extraction of atomic-scale determinants responsible for path differences. Combined with clustering techniques, PSA facilitates the comparison of many paths, including collections of transition ensembles. We use the closed-to-open transition of the enzyme adenylate kinase (AdK)—a commonly used testbed for the assessment enhanced sampling algorithms [2]—to examine multiple microsecond equilibrium molecular dynamics (MD) transitions of AdK in its substrate-free form alongside transition ensembles from the MD-based dynamic importance sampling (DIMS-MD) and targeted MD (TMD) methods, and a geometrical targeting algorithm (FRODA). A Hausdorff pairs analysis of these ensembles revealed, for instance, that differences in DIMS-MD and FRODA paths were mediated by a set of conserved salt bridges whose charge-charge interactions are fully modeled in DIMS-MD but not in FRODA. We illustrate how existing trajectory analysis methods relying on pre-defined collective variables (such as native contacts or geometric quantities) can be used synergistically with PSA, as well as how PSA can be applied to more complex systems such as membrane transporter proteins.[1] Seyler, Kumar, Thorpe, Beckstein. PLoS Comput Biol (2015), http://dx.doi.org/10.1371/journal.pcbi.1004568; [2] Seyler and Beckstein. Mol Simul 40:855-877 (2014), http://dx.doi.org/10.1080/08927022.2014.919497.

Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways.

Diverse classes of proteins function through large-scale conformational changes and various sophisticated computational algorithms have been proposed to enhance sampling of these macromolecular transition paths. Because such paths are curves in a high-dimensional space, it has been difficult to quantitatively compare multiple paths, a necessary prerequisite to, for instance, assess the quality of different algorithms. We introduce a method named Path Similarity Analysis (PSA) that enables us to quantify the similarity between two arbitrary paths and extract the atomic-scale determinants responsible for their differences. PSA utilizes the full information available in 3N-dimensional configuration space trajectories by employing the Hausdorff or Fréchet metrics (adopted from computational geometry) to quantify the degree of similarity between piecewise-linear curves. It thus completely avoids relying on projections into low dimensional spaces, as used in traditional approaches. To elucidate the principles of PSA, we quantified the effect of path roughness induced by thermal fluctuations using a toy model system. Using, as an example, the closed-to-open transitions of the enzyme adenylate kinase (AdK) in its substrate-free form, we compared a range of protein transition path-generating algorithms. Molecular dynamics-based dynamic importance sampling (DIMS) MD and targeted MD (TMD) and the purely geometric FRODA (Framework Rigidity Optimized Dynamics Algorithm) were tested along with seven other methods publicly available on servers, including several based on the popular elastic network model (ENM). PSA with clustering revealed that paths produced by a given method are more similar to each other than to those from another method and, for instance, that the ENM-based methods produced relatively similar paths. PSA was applied to ensembles of DIMS MD and FRODA trajectories of the conformational transition of diphtheria toxin, a particularly challenging example. For the AdK transition, the new concept of a Hausdorff-pair map enabled us to extract the molecular structural determinants responsible for differences in pathways, namely a set of conserved salt bridges whose charge-charge interactions are fully modelled in DIMS MD but not in FRODA. PSA has the potential to enhance our understanding of transition path sampling methods, validate them, and to provide a new approach to analyzing conformational transitions.

On the Roles of Substrate Binding and Hinge Unfolding in Conformational Changes of Adenylate Kinase

We characterized the conformational change of adenylate kinase (AK) between open and closed forms by conducting five all-atom molecular-dynamics simulations, each of 100 ns duration. Different initial structures and substrate binding configurations were used to probe the pathways of AK conformational change in explicit solvent, and no bias potential was applied. A complete closed-to-open and a partial open-to-closed transition were observed, demonstrating the direct impact of substrate-mediated interactions on shifting protein conformation. The sampled configurations suggest two possible pathways for connecting the open and closed structures of AK, affirming the prediction made based on available x-ray structures and earlier works of coarse-grained modeling. The trajectories of the all-atom molecular-dynamics simulations revealed the complexity of protein dynamics and the coupling between different domains during conformational change. Calculations of solvent density and density fluctuations surrounding AK did not show prominent variation during the transition between closed and open forms. Finally, we characterized the effects of local unfolding of an important hinge near Pro 177 on the closed-to-open transition of AK and identified a novel mechanism by which hinge unfolding modulates protein conformational change. The local unfolding of Pro 177 hinge induces alternative tertiary contacts that stabilize the closed structure and prevent the opening transition.

2P070 ストリング法によるアデニル酸キナーゼの構造変化解析(蛋白質-物性(安定性,折れたたみなど),第48回日本生物物理学会年会)

2P070 ストリング法によるアデニル酸キナーゼの構造変化解析(蛋白質-物性(安定性,折れたたみなど),第48回日本生物物理学会年会)

Modeling the Open-to-closed Transition of Adenylate Kinase: All-atom Molecular Dynamics Simulations and a Double-Well Network Model

An intrinsic property of protein is the ability to undergo conformational changes upon ligand binding. In this work, we study Adenylate Kinase (AKE), an important enzyme controlling the balance of ATP in prokaryotic cells. X-ray crystallography indicates that AKE has two distinct conformations, open and closed, depending on whether it is bound with substrates (ATP and AMP). Conformation difference in AKE can be determined by the relative position of two separate domains, the lid domain and the NMP binding domain, to the core. In this work, all-atom molecular dynamics (MD) simulations and coarse-grained modeling are used to elucidate the effects of ligand binding on AKE conformation. Results based on four 100ns all-atom trajectories indicate that ATP binding induced the closing of lid domain and suggest that the relative population between closed to open structure is increased. The closing of NMP binding domain, however, is found to be more specific and may require a timescale longer than 100ns to close. The mechanical property of a hinge region is found to correlate with lid closing; residues in this region may be mutated to alter the rate of conformational change and hence enzyme catalysis. This prediction agrees well with the results of recent single molecule experiments. Using a double-well network coarse-grained model, multiple pathways of open-to-closed transition can be found. Motions of lid-domain and NMP binding domain are not concerted and may be treated as two distinct events. This picture is different from the result of using elastic network model and agrees better with atomistic simulations. In addition to open-to-closed transition, solvation structures and intrinsic mechanical properties of AKE are also characterized to identify key residues that may control the conformational change of AKE from mechanical perspectives.

1P-093 局所的フォールディングが、アデニル酸キナーゼのアロステリック転移に与える影響(蛋白質・物性(1),第46回日本生物物理学会年会)

1P-093 局所的フォールディングが、アデニル酸キナーゼのアロステリック転移に与える影響(蛋白質・物性(1),第46回日本生物物理学会年会)

1P-092 アデニル酸キナーゼのリガンド結合を伴うアロステリック転移のカメレオンGoモデルによる研究(蛋白質・物性(1),第46回日本生物物理学会年会)

1P-092 アデニル酸キナーゼのリガンド結合を伴うアロステリック転移のカメレオンGoモデルによる研究(蛋白質・物性(1),第46回日本生物物理学会年会)

Coarse-Grained Free Energy Functions for Studying Protein Conformational Changes: A Double-Well Network Model

In this work, a double-well network model (DWNM) is presented for generating a coarse-grained free energy function that can be used to study the transition between reference conformational states of a protein molecule. Compared to earlier work that uses a single, multidimensional double-well potential to connect two conformational states, the DWNM uses a set of interconnected double-well potentials for this purpose. The DWNM free energy function has multiple intermediate states and saddle points, and is hence a “rough” free energy landscape. In this implementation of the DWNM, the free energy function is reduced to an elastic-network model representation near the two reference states. The effects of free energy function roughness on the reaction pathways of protein conformational change is demonstrated by applying the DWNM to the conformational changes of two protein systems: the coil-to-helix transition of the DB-loop in G-actin and the open-to-closed transition of adenylate kinase. In both systems, the rough free energy function of the DWNM leads to the identification of distinct minimum free energy paths connecting two conformational states. These results indicate that while the elastic-network model captures the low-frequency vibrational motions of a protein, the roughness in the free energy function introduced by the DWNM can be used to characterize the transition mechanism between protein conformations.