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Kim J, Moon S, Romo TD, Yang Y, Bae E, Phillips GN. Conformational dynamics of adenylate kinase in crystals. STRUCTURAL DYNAMICS (MELVILLE, N.Y.) 2024; 11:014702. [PMID: 38389978 PMCID: PMC10883716 DOI: 10.1063/4.0000205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 12/14/2023] [Indexed: 02/24/2024]
Abstract
Adenylate kinase is a ubiquitous enzyme in living systems and undergoes dramatic conformational changes during its catalytic cycle. For these reasons, it is widely studied by genetic, biochemical, and biophysical methods, both experimental and theoretical. We have determined the basic crystal structures of three differently liganded states of adenylate kinase from Methanotorrus igneus, a hyperthermophilic organism whose adenylate kinase is a homotrimeric oligomer. The multiple copies of each protomer in the asymmetric unit of the crystal provide a unique opportunity to study the variation in the structure and were further analyzed using advanced crystallographic refinement methods and analysis tools to reveal conformational heterogeneity and, thus, implied dynamic behaviors in the catalytic cycle.
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Affiliation(s)
- Junhyung Kim
- Department of Agricultural Biotechnology, Seoul National University, Seoul 08826, South Korea
| | - Sojin Moon
- Department of Agricultural Biotechnology, Seoul National University, Seoul 08826, South Korea
| | - Tod D Romo
- Department of Biochemistry and Biophysics, University of Rochester Medical Center, Rochester, New York 14642, USA
| | - Yifei Yang
- Departments of BioSciences, Rice University, Houston, Texas 77005, USA
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2
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Tian H, Jiang X, Xiao S, La Force H, Larson EC, Tao P. LAST: Latent Space-Assisted Adaptive Sampling for Protein Trajectories. J Chem Inf Model 2023; 63:67-75. [PMID: 36472885 PMCID: PMC9904845 DOI: 10.1021/acs.jcim.2c01213] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Molecular dynamics (MD) simulation is widely used to study protein conformations and dynamics. However, conventional simulation suffers from being trapped in some local energy minima that are hard to escape. Thus, most of the computational time is spent sampling in the already visited regions. This leads to an inefficient sampling process and further hinders the exploration of protein movements in affordable simulation time. The advancement of deep learning provides new opportunities for protein sampling. Variational autoencoders are a class of deep learning models to learn a low-dimensional representation (referred to as the latent space) that can capture the key features of the input data. Based on this characteristic, we proposed a new adaptive sampling method, latent space-assisted adaptive sampling for protein trajectories (LAST), to accelerate the exploration of protein conformational space. This method comprises cycles of (i) variational autoencoder training, (ii) seed structure selection on the latent space, and (iii) conformational sampling through additional MD simulations. The proposed approach is validated through the sampling of four structures of two protein systems: two metastable states of Escherichia coli adenosine kinase (ADK) and two native states of Vivid (VVD). In all four conformations, seed structures were shown to lie on the boundary of conformation distributions. Moreover, large conformational changes were observed in a shorter simulation time when compared with structural dissimilarity sampling (SDS) and conventional MD (cMD) simulations in both systems. In metastable ADK simulations, LAST explored two transition paths toward two stable states, while SDS explored only one and cMD neither. In VVD light state simulations, LAST was three times faster than cMD simulation with a similar conformational space. Overall, LAST is comparable to SDS and is a promising tool in adaptive sampling. The LAST method is publicly available at https://github.com/smu-tao-group/LAST to facilitate related research.
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Affiliation(s)
- Hao Tian
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas75206, United States
| | - Xi Jiang
- Department of Statistical Science, Southern Methodist University, Dallas, Texas75206, United States
| | - Sian Xiao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas75206, United States
| | - Hunter La Force
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas75206, United States
| | - Eric C Larson
- Department of Computer Science, Southern Methodist University, Dallas, Texas75206, United States
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas75206, United States
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3
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Shinobu A, Kobayashi C, Matsunaga Y, Sugita Y. Coarse-Grained Modeling of Multiple Pathways in Conformational Transitions of Multi-Domain Proteins. J Chem Inf Model 2021; 61:2427-2443. [PMID: 33956432 DOI: 10.1021/acs.jcim.1c00286] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Large-scale conformational transitions in multi-domain proteins are often essential for their functions. To investigate the transitions, it is necessary to explore multiple potential pathways, which involve different intermediate structures. Here, we present a multi-basin (MB) coarse-grained (CG) structure-based Go̅ model for describing transitions in proteins with more than two moving domains. This model is an extension of our dual-basin Go̅ model in which system-dependent parameters are determined systematically using the multistate Bennett acceptance ratio method. In the MB Go̅ model for multi-domain proteins, we assume that intermediate structures may have partial inter-domain native contacts. This approach allows us to search multiple transition pathways that involve distinct intermediate structures using the CG molecular dynamics (MD) simulations. We apply this scheme to an enzyme, adenylate kinase (AdK), which has three major domains and can move along two different pathways. Using the optimized mixing parameters for each pathway, AdK shows frequent transitions between the Open, Closed, and the intermediate basins and samples a wide variety of conformations within each basin. The explored multiple transition pathways could be compared with experimental data and examined in more detail by atomistic MD simulations.
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Affiliation(s)
- Ai Shinobu
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047, Japan
| | - Chigusa Kobayashi
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe, Hyogo 650-0047, Japan
| | - Yasuhiro Matsunaga
- Graduate School of Science and Engineering, Saitama University, Saitama 338-8570, Japan
| | - Yuji Sugita
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047, Japan.,Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe, Hyogo 650-0047, Japan.,Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama 351-0198, Japan
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4
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Stiller JB, Kerns SJ, Hoemberger M, Cho YJ, Otten R, Hagan MF, Kern D. Probing the Transition State in Enzyme Catalysis by High-Pressure NMR Dynamics. Nat Catal 2019; 2:726-734. [PMID: 32159076 DOI: 10.1038/s41929-019-0307-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Protein conformational changes are frequently essential for enzyme catalysis, and in several cases, shown to be the limiting factor for overall catalytic speed. However, a structural understanding of corresponding transition states, needed to rationalize the kinetics, remains obscure due to their fleeting nature. Here, we determine the transition-state ensemble of the rate-limiting conformational transition in the enzyme adenylate kinase, by a synergistic approach between experimental high-pressure NMR relaxation during catalysis and molecular dynamics simulations. By comparing homologous kinases evolved under ambient or high pressure in the deep-sea, we detail transition state ensembles that differ in solvation as directly measured by the pressure dependence of catalysis. Capturing transition-state ensembles begins to complete the catalytic energy landscape that is generally characterized by structures of all intermediates and frequencies of transitions among them.
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Affiliation(s)
- John B Stiller
- Department of Biochemistry and Howard Hughes Medical Institute, Brandeis University, Waltham, Massachusetts 02452, United States
| | - S Jordan Kerns
- Department of Biochemistry and Howard Hughes Medical Institute, Brandeis University, Waltham, Massachusetts 02452, United States.,Present addresses: S.J.K. 27 Drydock Ave, Boston MA 02110 , M.H. 225 Binney St, Cambridge, MA 02142, Y.J.C. 733 Concord Ave Cambridge, MA 02138
| | - Marc Hoemberger
- Department of Biochemistry and Howard Hughes Medical Institute, Brandeis University, Waltham, Massachusetts 02452, United States.,Present addresses: S.J.K. 27 Drydock Ave, Boston MA 02110 , M.H. 225 Binney St, Cambridge, MA 02142, Y.J.C. 733 Concord Ave Cambridge, MA 02138
| | - Young-Jin Cho
- Department of Biochemistry and Howard Hughes Medical Institute, Brandeis University, Waltham, Massachusetts 02452, United States.,Present addresses: S.J.K. 27 Drydock Ave, Boston MA 02110 , M.H. 225 Binney St, Cambridge, MA 02142, Y.J.C. 733 Concord Ave Cambridge, MA 02138
| | - Renee Otten
- Department of Biochemistry and Howard Hughes Medical Institute, Brandeis University, Waltham, Massachusetts 02452, United States
| | - Michael F Hagan
- Department of Physics, Brandeis University, Waltham, Massachusetts 02452, United States
| | - Dorothee Kern
- Department of Biochemistry and Howard Hughes Medical Institute, Brandeis University, Waltham, Massachusetts 02452, United States
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Lambrughi M, Tiberti M, Allega MF, Sora V, Nygaard M, Toth A, Salamanca Viloria J, Bignon E, Papaleo E. Analyzing Biomolecular Ensembles. Methods Mol Biol 2019; 2022:415-451. [PMID: 31396914 DOI: 10.1007/978-1-4939-9608-7_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Several techniques are available to generate conformational ensembles of proteins and other biomolecules either experimentally or computationally. These methods produce a large amount of data that need to be analyzed to identify structure-dynamics-function relationship. In this chapter, we will cover different tools to unveil the information hidden in conformational ensemble data and to guide toward the rationalization of the data. We included routinely used approaches such as dimensionality reduction, as well as new methods inspired by high-order statistics and graph theory.
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Affiliation(s)
- Matteo Lambrughi
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Matteo Tiberti
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Maria Francesca Allega
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Valentina Sora
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Mads Nygaard
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Agota Toth
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Juan Salamanca Viloria
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Emmanuelle Bignon
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Elena Papaleo
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark.
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6
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Thompson HN, Thompson CE, Andrade Caceres R, Dardenne LE, Netz PA, Stassen H. Prion protein conversion triggered by acidic condition: a molecular dynamics study through different force fields. J Comput Chem 2018; 39:2000-2011. [DOI: 10.1002/jcc.25380] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 05/15/2018] [Accepted: 05/26/2018] [Indexed: 11/06/2022]
Affiliation(s)
- Helen Nathalia Thompson
- Departamento de Físico-Química, Instituto de Química; Universidade Federal do Rio Grande do Sul; 91501-970 Porto Alegre Rio Grande do Sul Brazil
| | - Claudia Elizabeth Thompson
- Departamento de Farmacociências; Universidade Federal de Ciências da Saúde de Porto Alegre; 90050-170 Porto Alegre Rio Grande do Sul Brazil
| | - Rafael Andrade Caceres
- Departamento de Farmacociências; Universidade Federal de Ciências da Saúde de Porto Alegre; 90050-170 Porto Alegre Rio Grande do Sul Brazil
| | | | - Paulo Augusto Netz
- Departamento de Físico-Química, Instituto de Química; Universidade Federal do Rio Grande do Sul; 91501-970 Porto Alegre Rio Grande do Sul Brazil
| | - Hubert Stassen
- Departamento de Físico-Química, Instituto de Química; Universidade Federal do Rio Grande do Sul; 91501-970 Porto Alegre Rio Grande do Sul Brazil
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Childers MC, Daggett V. Validating Molecular Dynamics Simulations against Experimental Observables in Light of Underlying Conformational Ensembles. J Phys Chem B 2018; 122:6673-6689. [PMID: 29864281 DOI: 10.1021/acs.jpcb.8b02144] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Far from the static, idealized conformations deposited into structural databases, proteins are highly dynamic molecules that undergo conformational changes on temporal and spatial scales that may span several orders of magnitude. These conformational changes, often intimately connected to the functional roles that proteins play, may be obscured by traditional biophysical techniques. Over the past 40 years, molecular dynamics (MD) simulations have complemented these techniques by providing the "hidden" atomistic details that underlie protein dynamics. However, there are limitations of the degree to which molecular simulations accurately and quantitatively describe protein motions. Here we show that although four molecular dynamics simulation packages (AMBER, GROMACS, NAMD, and ilmm) reproduced a variety of experimental observables for two different proteins (engrailed homeodomain and RNase H) equally well overall at room temperature, there were subtle differences in the underlying conformational distributions and the extent of conformational sampling obtained. This leads to ambiguity about which results are correct, as experiment cannot always provide the necessary detailed information to distinguish between the underlying conformational ensembles. However, the results with different packages diverged more when considering larger amplitude motion, for example, the thermal unfolding process and conformational states sampled, with some packages failing to allow the protein to unfold at high temperature or providing results at odds with experiment. While most differences between MD simulations performed with different packages are attributed to the force fields themselves, there are many other factors that influence the outcome, including the water model, algorithms that constrain motion, how atomic interactions are handled, and the simulation ensemble employed. Here four different MD packages were tested each using best practices as established by the developers, utilizing three different protein force fields and three different water models. Differences between the simulated protein behavior using two different packages but the same force field, as well as two different packages with different force fields but the same water models and approaches to restraining motion, show how other factors can influence the behavior, and it is incorrect to place all the blame for deviations and errors on force fields or to expect improvements in force fields alone to solve such problems.
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Affiliation(s)
- Matthew Carter Childers
- Department of Bioengineering , University of Washington , Seattle , Washington 98195-5013 , United States
| | - Valerie Daggett
- Department of Bioengineering , University of Washington , Seattle , Washington 98195-5013 , United States
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8
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Zheng Y, Cui Q. Multiple Pathways and Time Scales for Conformational Transitions in apo-Adenylate Kinase. J Chem Theory Comput 2018; 14:1716-1726. [PMID: 29378407 DOI: 10.1021/acs.jctc.7b01064] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The open/close transition in adenylate kinase (AK) is regarded as a representative example for large-scale conformational transition in proteins, yet its mechanism remains unclear despite numerous experimental and computational studies. Using extensive (∼50 μs) explicit solvent atomistic simulations and Markov state analysis, we shed new lights on the mechanism of this transition in the apo form of AK. The closed basin of apo AK features an open NMP domain while the LID domain closes and rotates toward it. Therefore, although the computed structural properties of the closed ensemble are consistent with previously reported FRET and PRE measurements, our simulations suggest that NMP closure is likely to follow AMP binding, in contrast to the previous interpretation of FRET and PRE data that the apo state was able to sample the fully closed conformation for "ligand selection". The closed state ensemble is found to be kinetically heterogeneous; multiple pathways and time scales are associated with the open/close transition, providing new clues to the disparate time scales observed in different experiments. Besides interdomain interactions, a novel mutual information analysis identifies specific intradomain interactions that correlate strongly to transition kinetics, supporting observations from previous chimera experiments. While our results underscore the role of internal domain properties in determining the kinetics of open/close transition in apo AK, no evidence is observed for any significant degree of local unfolding during the transition. These observations about AK have general implications to our view of conformational states, transition pathways, and time scales of conformational changes in proteins. The key features and time scales of observed transition pathways are robust and similar from simulations using two popular fixed charge force fields.
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Affiliation(s)
- Yuqing Zheng
- Graduate Program in Biophysics and Department of Chemistry , University of Wisconsin-Madison , 1101 University Avenue , Madison , Wisconsin 53706 , United States
| | - Qiang Cui
- Graduate Program in Biophysics and Department of Chemistry , University of Wisconsin-Madison , 1101 University Avenue , Madison , Wisconsin 53706 , United States
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9
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Nygaard M, Terkelsen T, Vidas Olsen A, Sora V, Salamanca Viloria J, Rizza F, Bergstrand-Poulsen S, Di Marco M, Vistesen M, Tiberti M, Lambrughi M, Jäättelä M, Kallunki T, Papaleo E. The Mutational Landscape of the Oncogenic MZF1 SCAN Domain in Cancer. Front Mol Biosci 2016; 3:78. [PMID: 28018905 PMCID: PMC5156680 DOI: 10.3389/fmolb.2016.00078] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Accepted: 11/17/2016] [Indexed: 11/24/2022] Open
Abstract
SCAN domains in zinc-finger transcription factors are crucial mediators of protein-protein interactions. Up to 240 SCAN-domain encoding genes have been identified throughout the human genome. These include cancer-related genes, such as the myeloid zinc finger 1 (MZF1), an oncogenic transcription factor involved in the progression of many solid cancers. The mechanisms by which SCAN homo- and heterodimers assemble and how they alter the transcriptional activity of zinc-finger transcription factors in cancer and other diseases remain to be investigated. Here, we provide the first description of the conformational ensemble of the MZF1 SCAN domain cross-validated against NMR experimental data, which are probes of structure and dynamics on different timescales. We investigated the protein-protein interaction network of MZF1 and how it is perturbed in different cancer types by the analyses of high-throughput proteomics and RNASeq data. Collectively, we integrated many computational approaches, ranging from simple empirical energy functions to all-atom microsecond molecular dynamics simulations and network analyses to unravel the effects of cancer-related substitutions in relation to MZF1 structure and interactions.
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Affiliation(s)
- Mads Nygaard
- Computational Biology Laboratory and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - Thilde Terkelsen
- Computational Biology Laboratory and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - André Vidas Olsen
- Computational Biology Laboratory and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - Valentina Sora
- Computational Biology Laboratory and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - Juan Salamanca Viloria
- Computational Biology Laboratory and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - Fabio Rizza
- Department of Biomedical Sciences, University of Padua Padua, Italy
| | - Sanne Bergstrand-Poulsen
- Computational Biology Laboratory and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - Miriam Di Marco
- Computational Biology Laboratory and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - Mette Vistesen
- Cell Stress and Survival Unit and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - Matteo Tiberti
- Department of Chemistry and Biochemistry, School of Biological and Chemical Sciences, Queen Mary University of London London, UK
| | - Matteo Lambrughi
- Computational Biology Laboratory and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - Marja Jäättelä
- Unit of Cell Death and Metabolism and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - Tuula Kallunki
- Unit of Cell Death and Metabolism and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - Elena Papaleo
- Computational Biology Laboratory and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
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