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Liu N, Mikhailovskii O, Skrynnikov NR, Xue Y. Simulating diffraction photographs based on molecular dynamics trajectories of a protein crystal: a new option to examine structure-solving strategies in protein crystallography. IUCRJ 2023; 10:16-26. [PMID: 36598499 PMCID: PMC9812212 DOI: 10.1107/s2052252522011198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
A molecular dynamics (MD)-based pipeline has been designed and implemented to emulate the entire process of collecting diffraction photographs and calculating crystallographic structures of proteins from them. Using a structure of lysozyme solved in-house, a supercell comprising 125 (5 × 5 × 5) crystal unit cells containing a total of 1000 protein molecules and explicit interstitial solvent was constructed. For this system, two 300 ns MD trajectories at 298 and 250 K were recorded. A series of snapshots from these trajectories were then used to simulate a fully realistic set of diffraction photographs, which were further fed into the standard pipeline for structure determination. The resulting structures show very good agreement with the underlying MD model not only in terms of coordinates but also in terms of B factors; they are also consistent with the original experimental structure. The developed methodology should find a range of applications, such as optimizing refinement protocols to solve crystal structures and extracting dynamics information from diffraction data or diffuse scattering.
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Affiliation(s)
- Ning Liu
- School of Life Sciences, Tsinghua University, Beijing 100084, People’s Republic of China
| | - Oleg Mikhailovskii
- Laboratory of Biomolecular NMR, St Petersburg State University, St Petersburg, Russian Federation
- Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA
| | - Nikolai R. Skrynnikov
- Laboratory of Biomolecular NMR, St Petersburg State University, St Petersburg, Russian Federation
- Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA
| | - Yi Xue
- School of Life Sciences, Tsinghua University, Beijing 100084, People’s Republic of China
- Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, People’s Republic of China
- Tsinghua University–Peking University Joint Center for Life Sciences, Tsinghua University, Beijing 100084, People’s Republic of China
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Zang Y, Wang H, Hao D, Kang Y, Zhang J, Li X, Zhang L, Yang Z, Zhang S. p38α Kinase Auto-Activation through Its Conformational Transition Induced by Tyr323 Phosphorylation. J Chem Inf Model 2022; 62:6639-6648. [PMID: 36394912 DOI: 10.1021/acs.jcim.2c00236] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
p38α is a key serine/threonine kinase that can enable atypical auto-activation through Zap70 phosphorylation and initiate T cell receptor signaling. The auto-activation plays an important role in autoimmune diseases. Although the classical activation mechanism of p38α has been studied in-depth, the atypical activation mechanism of Y323 phosphorylation-induced p38α auto-activation remains largely unexplained, especially the regulatory effects of phosphorylation on different sites (Y323 vs T180). From the X-ray experimental data, we identified the inactive and active states of p38α using principal component analysis. To understand the auto-activation process and the internal driving mechanism, a computational paradigm that couples the targeted molecular dynamics simulations, the String Method, and the umbrella sampling strategy were employed to generate the conformational landscape of p38α, including p38α T180-Y323, p38α T180-pY323, and p38α pT180-pY323 systems (pT180/pY323: phosphorylated T180/Y323). We explored that pY323 could change the conformational distribution and promote the conformational transition of p38α from the inactive state to the active state. Auto-activation of p38α is regulated by pY323 through destabilization of the hydrophobic core structure and aided by R173. This study will further explain the conformational transition of p38α induced by Y323 phosphorylation and provide insights into the universal molecular auto-activation mechanism of the p38 subfamily at the atomic level.
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Affiliation(s)
- Yongjian Zang
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an710049, China
| | - He Wang
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an710049, China
| | - Dongxiao Hao
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an710049, China
| | - Ying Kang
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an710049, China
| | - Jianwen Zhang
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an710049, China
| | - Xuhua Li
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an710049, China
| | - Lei Zhang
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an710049, China
| | - Zhiwei Yang
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an710049, China
| | - Shengli Zhang
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an710049, China
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Shekhar M, Terashi G, Gupta C, Sarkar D, Debussche G, Sisco NJ, Nguyen J, Mondal A, Vant J, Fromme P, Van Horn WD, Tajkhorshid E, Kihara D, Dill K, Perez A, Singharoy A. CryoFold: determining protein structures and data-guided ensembles from cryo-EM density maps. MATTER 2021; 4:3195-3216. [PMID: 35874311 PMCID: PMC9302471 DOI: 10.1016/j.matt.2021.09.004] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Cryo-electron microscopy (EM) requires molecular modeling to refine structural details from data. Ensemble models arrive at low free-energy molecular structures, but are computationally expensive and limited to resolving only small proteins that cannot be resolved by cryo-EM. Here, we introduce CryoFold - a pipeline of molecular dynamics simulations that determines ensembles of protein structures directly from sequence by integrating density data of varying sparsity at 3-5 Å resolution with coarse-grained topological knowledge of the protein folds. We present six examples showing its broad applicability for folding proteins between 72 to 2000 residues, including large membrane and multi-domain systems, and results from two EMDB competitions. Driven by data from a single state, CryoFold discovers ensembles of common low-energy models together with rare low-probability structures that capture the equilibrium distribution of proteins constrained by the density maps. Many of these conformations, unseen by traditional methods, are experimentally validated and functionally relevant. We arrive at a set of best practices for data-guided protein folding that are controlled using a Python GUI.
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Affiliation(s)
- Mrinal Shekhar
- Center for Biophysics and Quantitative Biology, Department of Biochemistry, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Chitrak Gupta
- The School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
- The Biodesign Institute Center for Structural Discovery, Arizona State University, Tempe, AZ 85281, USA
| | - Daipayan Sarkar
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
- The School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Gaspard Debussche
- Department of Mathematics and Computer Sciences, Grenoble INP, 38000 Grenoble, France
| | - Nicholas J Sisco
- The School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
- The Biodesign Institute Virginia G. Piper Center for Personalized Diagnostics, Arizona State University, Tempe, AZ 85281, USA
| | - Jonathan Nguyen
- The School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
- The Biodesign Institute Center for Structural Discovery, Arizona State University, Tempe, AZ 85281, USA
| | - Arup Mondal
- Chemistry Department, Quantum Theory Project, University of Florida, Gainesville, Florida, 32611, USA
| | - John Vant
- The School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
- The Biodesign Institute Center for Structural Discovery, Arizona State University, Tempe, AZ 85281, USA
| | - Petra Fromme
- The School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
- The Biodesign Institute Center for Structural Discovery, Arizona State University, Tempe, AZ 85281, USA
| | - Wade D Van Horn
- The School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
- The Biodesign Institute Virginia G. Piper Center for Personalized Diagnostics, Arizona State University, Tempe, AZ 85281, USA
| | - Emad Tajkhorshid
- Center for Biophysics and Quantitative Biology, Department of Biochemistry, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Ken Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Alberto Perez
- Chemistry Department, Quantum Theory Project, University of Florida, Gainesville, Florida, 32611, USA
| | - Abhishek Singharoy
- The School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
- The Biodesign Institute Center for Structural Discovery, Arizona State University, Tempe, AZ 85281, USA
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Cole GB, Bateman TJ, Moraes TF. The surface lipoproteins of gram-negative bacteria: Protectors and foragers in harsh environments. J Biol Chem 2021; 296:100147. [PMID: 33277359 PMCID: PMC7857515 DOI: 10.1074/jbc.rev120.008745] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 12/02/2020] [Accepted: 12/04/2020] [Indexed: 11/06/2022] Open
Abstract
Gram-negative pathogens are enveloped by an outer membrane that serves as a double-edged sword: On the one hand, it provides a layer of protection for the bacterium from environmental insults, including other bacteria and the host immune system. On the other hand, it restricts movement of vital nutrients into the cell and provides a plethora of antigens that can be detected by host immune systems. One strategy used to overcome these limitations is the decoration of the outer surface of gram-negative bacteria with proteins tethered to the outer membrane through a lipid anchor. These surface lipoproteins (SLPs) fulfill critical roles in immune evasion and nutrient acquisition, but as more bacterial genomes are sequenced, we are beginning to discover their prevalence and their different roles and mechanisms and importantly how we can exploit them as antimicrobial targets. This review will focus on representative SLPs that gram-negative bacteria use to overcome host innate immunity, specifically the areas of nutritional immunity and complement system evasion. We elaborate on the structures of some notable SLPs required for binding target molecules in hosts and how this information can be used alongside bioinformatics to understand mechanisms of binding and in the discovery of new SLPs. This information provides a foundation for the development of therapeutics and the design of vaccine antigens.
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Affiliation(s)
- Gregory B Cole
- Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada
| | - Thomas J Bateman
- Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada
| | - Trevor F Moraes
- Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada.
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Vant JW, Sarkar D, Streitwieser E, Fiorin G, Skeel R, Vermaas JV, Singharoy A. Data-guided Multi-Map variables for ensemble refinement of molecular movies. J Chem Phys 2020; 153:214102. [PMID: 33291927 PMCID: PMC7714525 DOI: 10.1063/5.0022433] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 11/09/2020] [Indexed: 12/17/2022] Open
Abstract
Driving molecular dynamics simulations with data-guided collective variables offer a promising strategy to recover thermodynamic information from structure-centric experiments. Here, the three-dimensional electron density of a protein, as it would be determined by cryo-EM or x-ray crystallography, is used to achieve simultaneously free-energy costs of conformational transitions and refined atomic structures. Unlike previous density-driven molecular dynamics methodologies that determine only the best map-model fits, our work employs the recently developed Multi-Map methodology to monitor concerted movements within equilibrium, non-equilibrium, and enhanced sampling simulations. Construction of all-atom ensembles along the chosen values of the Multi-Map variable enables simultaneous estimation of average properties, as well as real-space refinement of the structures contributing to such averages. Using three proteins of increasing size, we demonstrate that biased simulation along the reaction coordinates derived from electron densities can capture conformational transitions between known intermediates. The simulated pathways appear reversible with minimal hysteresis and require only low-resolution density information to guide the transition. The induced transitions also produce estimates for free energy differences that can be directly compared to experimental observables and population distributions. The refined model quality is superior compared to those found in the Protein Data Bank. We find that the best quantitative agreement with experimental free-energy differences is obtained using medium resolution density information coupled to comparatively large structural transitions. Practical considerations for probing the transitions between multiple intermediate density states are also discussed.
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Affiliation(s)
- John W. Vant
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85281, USA
| | | | - Ellen Streitwieser
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85281, USA
| | - Giacomo Fiorin
- Theoretical Molecular Biophysics Laboratory, National Heart, Lung and Blood Institute, National Institutes of Health, 10 Center Drive, Bethesda, Maryland 20814, USA
| | - Robert Skeel
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona 85281, USA
| | - Josh V. Vermaas
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, USA
| | - Abhishek Singharoy
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85281, USA
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