1
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Bairagya HR. Dynamics of nucleoplasm in human leukemia cells: A thrust towards designing anti-leukemic agents. J Mol Graph Model 2024; 131:108807. [PMID: 38908255 DOI: 10.1016/j.jmgm.2024.108807] [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] [Received: 03/04/2024] [Revised: 04/20/2024] [Accepted: 06/02/2024] [Indexed: 06/24/2024]
Abstract
The human inosine monophosphate dehydrogenase (hIMPDH) is a metabolic enzyme that possesses a unique ability to self-assemble into higher-order structures, forming cytoophidia. The hIMPDH II isoform is more active in chronic myeloid leukemia (CML) cancer cells, making it a promising target for anti-leukemic therapy. However, the structural details and molecular mechanisms of the dynamics of hIMPDHcytoophidia assembly in vitro need to be better understood, and it is crucial to reconstitute the computational nucleoplasm model with cytophilic-like polymers in vitro to characterize their structure and function. Finally, a computational model and its dynamics of the nucleoplasm for CML cells have been proposed in this short review. This research on nucleoplasm aims to aid the scientific community's understanding of how metabolic enzymes like hIMPDH function in cancer and normal cells. However, validating and justifying the computational results from modeling and simulation with experimental data is essential. The new insights gained from this research could explain the structure/topology, geometrical, and electronic consequences of hIMPDH inhibitors on leukemic and normal cells. They could lead to further advancements in the knowledge of nucleoplasmic chemical reaction dynamics.
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Affiliation(s)
- Hridoy R Bairagya
- Computational Drug Design and Bio-molecular Simulation Lab, Department of Bioinformatics, Maulana Abul Kalam Azad University of Technology, West Bengal, 741249, India.
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2
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Kern NR, Lee J, Choi YK, Im W. CHARMM-GUI Multicomponent Assembler for modeling and simulation of complex multicomponent systems. Nat Commun 2024; 15:5459. [PMID: 38937468 PMCID: PMC11211406 DOI: 10.1038/s41467-024-49700-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 06/17/2024] [Indexed: 06/29/2024] Open
Abstract
Atomic-scale molecular modeling and simulation are powerful tools for computational biology. However, constructing models with large, densely packed molecules, non-water solvents, or with combinations of multiple biomembranes, polymers, and nanomaterials remains challenging and requires significant time and expertise. Furthermore, existing tools do not support such assemblies under the periodic boundary conditions (PBC) necessary for molecular simulation. Here, we describe Multicomponent Assembler in CHARMM-GUI that automates complex molecular assembly and simulation input preparation under the PBC. In this work, we demonstrate its versatility by preparing 6 challenging systems with varying density of large components: (1) solvated proteins, (2) solvated proteins with a pre-equilibrated membrane, (3) solvated proteins with a sheet-like nanomaterial, (4) solvated proteins with a sheet-like polymer, (5) a mixed membrane-nanomaterial system, and (6) a sheet-like polymer with gaseous solvent. Multicomponent Assembler is expected to be a unique cyberinfrastructure to study complex interactions between small molecules, biomacromolecules, polymers, and nanomaterials.
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Affiliation(s)
- Nathan R Kern
- Department of Computer Science & Engineering, Lehigh University, Bethlehem, PA, USA
| | - Jumin Lee
- Department of Biological Sciences, Lehigh University, Bethlehem, PA, USA
| | - Yeol Kyo Choi
- Department of Biological Sciences, Lehigh University, Bethlehem, PA, USA
| | - Wonpil Im
- Department of Computer Science & Engineering, Lehigh University, Bethlehem, PA, USA.
- Department of Biological Sciences, Lehigh University, Bethlehem, PA, USA.
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.
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3
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Yu I, Mori T, Matsuoka D, Surblys D, Sugita Y. SPANA: Spatial decomposition analysis for cellular-scale molecular dynamics simulations. J Comput Chem 2024; 45:498-505. [PMID: 37966727 DOI: 10.1002/jcc.27260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/31/2023] [Accepted: 11/02/2023] [Indexed: 11/16/2023]
Abstract
The rapid increase in computational power with the latest supercomputers has enabled atomistic molecular dynamics (MDs) simulations of biomolecules in biological membrane, cytoplasm, and other cellular environments. These environments often contain a million or more atoms to be simulated simultaneously. Therefore, their trajectory analyses involve heavy computations that can become a bottleneck in the computational studies. Spatial decomposition analysis (SPANA) is a set of analysis tools in the Generalized-Ensemble Simulation System (GENESIS) software package that can carry out MD trajectory analyses of large-scale biological simulations using multiple CPU cores in parallel. SPANA applies the spatial decomposition of a large biological system to distribute structural and dynamical analyses into individual CPU cores, which reduces the computational time and the memory size, significantly. SPANA opens new possibilities for detailed atomistic analyses of biomacromolecules as well as solvent water molecules, ions, and metabolites in MD simulation trajectories of very large biological systems containing more than millions of atoms in cellular environments.
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Affiliation(s)
- Isseki Yu
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama, Japan
- Department of Bioinformatics, Maebashi Institute of Technology, Maebashi, Gunma, Japan
| | - Takaharu Mori
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama, Japan
| | - Daisuke Matsuoka
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama, Japan
| | - Donatas Surblys
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama, Japan
| | - Yuji Sugita
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama, Japan
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo, Japan
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe, Hyogo, Japan
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4
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Kaizu K, Takahashi K. Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico. Dev Growth Differ 2023; 65:554-564. [PMID: 37856476 DOI: 10.1111/dgd.12897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 09/06/2023] [Accepted: 10/14/2023] [Indexed: 10/21/2023]
Abstract
With advances in high-throughput, large-scale in vivo measurement and genome modification techniques at the single-nucleotide level, there is an increasing demand for the development of new technologies for the flexible design and control of cellular systems. Computer-aided design is a powerful tool to design new cells. Whole-cell modeling aims to integrate various cellular subsystems, determine their interactions and cooperative mechanisms, and predict comprehensive cellular behaviors by computational simulations on a genome-wide scale. It has been applied to prokaryotes, yeasts, and higher eukaryotic cells, and utilized in a wide range of applications, including production of valuable substances, drug discovery, and controlled differentiation. Whole-cell modeling, consisting of several thousand elements with diverse scales and properties, requires innovative model construction, simulation, and analysis techniques. Furthermore, whole-cell modeling has been extended to multiple scales, including high-resolution modeling at the single-nucleotide and single-amino acid levels and multicellular modeling of tissues and organs. This review presents an overview of the current state of whole-cell modeling, discusses the novel computational and experimental technologies driving it, and introduces further developments toward multihierarchical modeling on a whole-genome scale.
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Affiliation(s)
- Kazunari Kaizu
- RIKEN Center for Biosystems Dynamics Research, Osaka, Japan
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5
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Samuel Russell PP, Alaeen S, Pogorelov TV. In-Cell Dynamics: The Next Focus of All-Atom Simulations. J Phys Chem B 2023; 127:9863-9872. [PMID: 37793083 PMCID: PMC10874638 DOI: 10.1021/acs.jpcb.3c05166] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/06/2023]
Abstract
The cell is a crowded space where large biomolecules and metabolites are in continuous motion. Great strides have been made in in vitro studies of protein dynamics, folding, and protein-protein interactions, and much new data are emerging of how they differ in the cell. In this Perspective, we highlight the current progress in atomistic modeling of in-cell environments, both bacteria and mammals, with emphasis on classical all-atom molecular dynamics simulations. These simulations have been recently used to capture and characterize functional and non-functional protein-protein interactions, protein folding dynamics of small proteins with varied topologies, and dynamics of metabolites. We further discuss the challenges and efforts for updating modern force fields critical to the progress of cellular environment simulations. We also briefly summarize developments in relevant state-of-the-art experimental techniques. As computational and experimental methodologies continue to progress and produce more directly comparable data, we are poised to capture the complex atomistic picture of the cell.
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Affiliation(s)
- Premila P Samuel Russell
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Sepehr Alaeen
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Taras V Pogorelov
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- School of Chemical Sciences, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- National Center for Supercomputing Applications, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
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6
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Khalid S, Brandner AF, Juraschko N, Newman KE, Pedebos C, Prakaash D, Smith IPS, Waller C, Weerakoon D. Computational microbiology of bacteria: Advancements in molecular dynamics simulations. Structure 2023; 31:1320-1327. [PMID: 37875115 DOI: 10.1016/j.str.2023.09.012] [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] [Received: 07/27/2023] [Revised: 09/04/2023] [Accepted: 09/28/2023] [Indexed: 10/26/2023]
Abstract
Microbiology is traditionally considered within the context of wet laboratory methodologies. Computational techniques have a great potential to contribute to microbiology. Here, we describe our loose definition of "computational microbiology" and provide a short survey focused on molecular dynamics simulations of bacterial systems that fall within this definition. It is our contention that increased compositional complexity and realistic levels of molecular crowding within simulated systems are key for bridging the divide between experimental and computational microbiology.
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Affiliation(s)
- Syma Khalid
- Department of Biochemistry, University of Oxford, OX1 3QU Oxford, UK; School of Chemistry, University of Southampton, SO17 1BJ Southampton, UK.
| | - Astrid F Brandner
- Department of Biochemistry, University of Oxford, OX1 3QU Oxford, UK
| | - Nikolai Juraschko
- Department of Biochemistry, University of Oxford, OX1 3QU Oxford, UK; Artificial Intelligence and Informatics, The Rosalind Franklin Institute, Didcot, UK
| | - Kahlan E Newman
- School of Chemistry, University of Southampton, SO17 1BJ Southampton, UK
| | - Conrado Pedebos
- Department of Biochemistry, University of Oxford, OX1 3QU Oxford, UK; Programa de Pós-Graduação em Biociências (PPGBio), Universidade Federal de Ciências da Saúde de Porto Alegre - UFCSPA, Porto Alegre, Brazil
| | - Dheeraj Prakaash
- Department of Biochemistry, University of Oxford, OX1 3QU Oxford, UK
| | - Iain P S Smith
- School of Chemistry, University of Southampton, SO17 1BJ Southampton, UK
| | - Callum Waller
- School of Chemistry, University of Southampton, SO17 1BJ Southampton, UK
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7
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Kern NR, Lee J, Choi YK, Im W. CHARMM-GUI Multicomponent Assembler for Modeling and Simulation of Complex Multicomponent Systems. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.30.555590. [PMID: 37693396 PMCID: PMC10491218 DOI: 10.1101/2023.08.30.555590] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Atomic-scale molecular modeling and simulation are powerful tools for computational biology. However, constructing models with large, densely packed molecules, non-water solvents, or with combinations of multiple biomembranes, polymers, and nanomaterials remains challenging and requires significant time and expertise. Furthermore, existing tools do not support such assemblies under the periodic boundary conditions (PBC) necessary for molecular simulation. Here, we describe Multicomponent Assembler in CHARMM-GUI that automates complex molecular assembly and simulation input preparation under the PBC. We demonstrate its versatility by preparing 6 challenging systems with varying density of large components: (1) solvated proteins, (2) solvated proteins with a pre-equilibrated membrane, (3) solvated proteins with a sheet-like nanomaterial, (4) solvated proteins with a sheet-like polymer, (5) a mixed membrane-nanomaterial system, and (6) a sheet-like polymer with gaseous solvent. Multicomponent Assembler is expected to be a unique cyberinfrastructure to facilitate innovative studies of complex interactions between small (organic and inorganic) molecules, biomacromolecules, polymers, and nanomaterials.
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Affiliation(s)
- Nathan R. Kern
- Department of Computer Science & Engineering, Lehigh University, Bethlehem, PA, USA
| | - Jumin Lee
- Department of Biological Sciences, Lehigh University, Bethlehem, PA, USA
| | - Yeol Kyo Choi
- Department of Biological Sciences, Lehigh University, Bethlehem, PA, USA
| | - Wonpil Im
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science & Engineering, Lehigh University, Bethlehem, PA, USA
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8
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Delgado A, Vera-Villalobos J, Paz JL, Lossada C, Hurtado-León ML, Marrero-Ponce Y, Toro-Mendoza J, Alvarado YJ, González-Paz L. Macromolecular crowding impact on anti-CRISPR AcrIIC3/NmeCas9 complex: Insights from scaled particle theory, molecular dynamics, and elastic networks models. Int J Biol Macromol 2023:125113. [PMID: 37257544 DOI: 10.1016/j.ijbiomac.2023.125113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 06/02/2023]
Abstract
The coupling of Cas9 and its inhibitor AcrIIC3, both from the bacterium Neisseria meningitidis (Nme), form a homodimer of the (NmeCas9/AcrIIC3)2 type. This coupling was studied to assess the impact of their interaction with the crowders in the following environments: (1) homogeneous crowded, (2) heterogeneous, and (3) microheterogeneous cytoplasmic. For this, statistical thermodynamic models based on the scaled particle theory (SPT) were used, considering the attractive and repulsive protein-crowders contributions and the stability of the formation of spherocylindrical homodimers and the effects of changes in the size of spherical dimers were estimated. Studies based on models of dynamics, elastic networks, and statistical potentials to the formation of complexes NmeCas9/AcrIIC3 using PEG as the crowding agent support the predictions from SPT. Macromolecular crowding stabilizes the formation of the dimers, being more significant when the attractive protein-crowder interactions are weaker and the crowders are smaller. The coupling is favored towards the formation of spherical and compact dimers due to crowding addition (excluded-volume effects) and the thermodynamic stability of the dimers is markedly dependent on the size of the crowders. These results support the experimental mechanistic proposal of inhibition of NmeCas9 mediated by AcrIIC3.
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Affiliation(s)
- Ariana Delgado
- Instituto Venezolano de Investigaciones Científicas (IVIC), Centro de Biomedicina Molecular (CBM), Laboratorio de Química Biofísica Teórica y Experimental (LQBTE), 4001 Maracaibo, Zulia, Venezuela; Universidad del Zulia (LUZ), Facultad Experimental de Ciencias (FEC), Departamento de Química, Laboratorio de Química Teórica y Computacional (LQTC), 4001 Maracaibo, Venezuela
| | - Joan Vera-Villalobos
- Facultad de Ciencias Naturales y Matemáticas, Departamento de Química y Ciencias Ambientales, Laboratorio de Análisis Químico Instrumental (LAQUINS), Escuela Superior Politécnica del Litoral, Guayaquil, Ecuador
| | - José Luis Paz
- Departamento Académico de Química Inorgánica, Facultad de Química e Ingeniería Química, Universidad Nacional Mayor de San Marcos, Lima, Peru
| | - Carla Lossada
- Instituto Venezolano de Investigaciones Científicas (IVIC), Centro de Biomedicina Molecular (CBM), Laboratorio de Biocomputación (LB), 4001 Maracaibo, Zulia, Venezuela
| | - María Laura Hurtado-León
- Universidad del Zulia (LUZ), Facultad Experimental de Ciencias (FEC), Departamento de Biología, Laboratorio de Genética y Biología Molecular (LGBM), 4001 Maracaibo, Venezuela
| | - Yovani Marrero-Ponce
- Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas, Quito 170157, Pichincha, Ecuador; Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Baja California 22860, Mexico; Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Pichincha, Ecuador
| | - Jhoan Toro-Mendoza
- Instituto Venezolano de Investigaciones Científicas (IVIC), Centro de Biomedicina Molecular (CBM), Laboratorio de Química Biofísica Teórica y Experimental (LQBTE), 4001 Maracaibo, Zulia, Venezuela
| | - Ysaías J Alvarado
- Instituto Venezolano de Investigaciones Científicas (IVIC), Centro de Biomedicina Molecular (CBM), Laboratorio de Química Biofísica Teórica y Experimental (LQBTE), 4001 Maracaibo, Zulia, Venezuela.
| | - Lenin González-Paz
- Instituto Venezolano de Investigaciones Científicas (IVIC), Centro de Biomedicina Molecular (CBM), Laboratorio de Biocomputación (LB), 4001 Maracaibo, Zulia, Venezuela.
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9
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Aithani L, Alcaide E, Bartunov S, Cooper CDO, Doré AS, Lane TJ, Maclean F, Rucktooa P, Shaw RA, Skerratt SE. Advancing structural biology through breakthroughs in AI. Curr Opin Struct Biol 2023; 80:102601. [PMID: 37182397 DOI: 10.1016/j.sbi.2023.102601] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 03/06/2023] [Accepted: 04/03/2023] [Indexed: 05/16/2023]
Abstract
The past century has witnessed an exponential increase in our atomic-level understanding of molecular and cellular mechanisms from a structural perspective, with multiple landmark achievements contributing to the field. This, coupled with recent and continuing breakthroughs in artificial intelligence methods such as AlphaFold2, and enhanced computational power, is enabling our understanding of protein structure and function at unprecedented levels of accuracy and predictivity. Here, we describe some of the major recent advances across these fields, and describe, as these technologies coalesce, the potential to utilise our enhanced knowledge of intricate cellular and molecular systems to discover novel therapeutics to alleviate human suffering.
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Affiliation(s)
- Laksh Aithani
- CHARM Therapeutics Ltd., The Stanley Building, 7 St. Pancras Square, London, N1C 4AG, UK.
| | - Eric Alcaide
- CHARM Therapeutics Ltd., The Stanley Building, 7 St. Pancras Square, London, N1C 4AG, UK
| | - Sergey Bartunov
- CHARM Therapeutics Ltd., The Stanley Building, 7 St. Pancras Square, London, N1C 4AG, UK
| | - Christopher D O Cooper
- CHARM Therapeutics Ltd., B900, Babraham Research Campus, Babraham, Cambridge, CB22 3AT, UK
| | - Andrew S Doré
- CHARM Therapeutics Ltd., B900, Babraham Research Campus, Babraham, Cambridge, CB22 3AT, UK
| | - Thomas J Lane
- CHARM Therapeutics Ltd., B900, Babraham Research Campus, Babraham, Cambridge, CB22 3AT, UK
| | - Finlay Maclean
- CHARM Therapeutics Ltd., The Stanley Building, 7 St. Pancras Square, London, N1C 4AG, UK
| | - Prakash Rucktooa
- CHARM Therapeutics Ltd., B900, Babraham Research Campus, Babraham, Cambridge, CB22 3AT, UK
| | - Robert A Shaw
- CHARM Therapeutics Ltd., The Stanley Building, 7 St. Pancras Square, London, N1C 4AG, UK
| | - Sarah E Skerratt
- CHARM Therapeutics Ltd., B900, Babraham Research Campus, Babraham, Cambridge, CB22 3AT, UK.
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10
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Jung J, Kobayashi C, Sugita Y. Acceleration of generalized replica exchange with solute tempering simulations of large biological systems on massively parallel supercomputer. J Comput Chem 2023. [PMID: 37141320 DOI: 10.1002/jcc.27124] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 04/10/2023] [Accepted: 04/14/2023] [Indexed: 05/06/2023]
Abstract
Generalized replica exchange with solute tempering (gREST) is one of the enhanced sampling algorithms for proteins or other systems with rugged energy landscapes. Unlike the replica-exchange molecular dynamics (REMD) method, solvent temperatures are the same in all replicas, while solute temperatures are different and are exchanged frequently between replicas for exploring various solute structures. Here, we apply the gREST scheme to large biological systems containing over one million atoms using a large number of processors in a supercomputer. First, communication time on a multi-dimensional torus network is reduced by matching each replica to MPI processors optimally. This is applicable not only to gREST but also to other multi-copy algorithms. Second, energy evaluations, which are necessary for the multistate bennet acceptance ratio (MBAR) method for free energy estimations, are performed on-the-fly during the gREST simulations. Using these two advanced schemes, we observed 57.72 ns/day performance in 128-replica gREST calculations with 1.5 million atoms system using 16,384 nodes in Fugaku. These schemes implemented in the latest version of GENESIS software could open new possibilities to answer unresolved questions on large biomolecular complex systems with slow conformational dynamics.
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Affiliation(s)
- Jaewoon Jung
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe, Japan
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Saitama, Japan
| | - Chigusa Kobayashi
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe, Japan
| | - Yuji Sugita
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe, Japan
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Saitama, Japan
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
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11
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Dutta P, Roy P, Sengupta N. Effects of External Perturbations on Protein Systems: A Microscopic View. ACS OMEGA 2022; 7:44556-44572. [PMID: 36530249 PMCID: PMC9753117 DOI: 10.1021/acsomega.2c06199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 11/14/2022] [Indexed: 06/17/2023]
Abstract
Protein folding can be viewed as the origami engineering of biology resulting from the long process of evolution. Even decades after its recognition, research efforts worldwide focus on demystifying molecular factors that underlie protein structure-function relationships; this is particularly relevant in the era of proteopathic disease. A complex co-occurrence of different physicochemical factors such as temperature, pressure, solvent, cosolvent, macromolecular crowding, confinement, and mutations that represent realistic biological environments are known to modulate the folding process and protein stability in unique ways. In the current review, we have contextually summarized the substantial efforts in unveiling individual effects of these perturbative factors, with major attention toward bottom-up approaches. Moreover, we briefly present some of the biotechnological applications of the insights derived from these studies over various applications including pharmaceuticals, biofuels, cryopreservation, and novel materials. Finally, we conclude by summarizing the challenges in studying the combined effects of multifactorial perturbations in protein folding and refer to complementary advances in experiment and computational techniques that lend insights to the emergent challenges.
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Affiliation(s)
- Pallab Dutta
- Department
of Biological Sciences, Indian Institute
of Science Education and Research (IISER) Kolkata, Mohanpur741246, India
| | - Priti Roy
- Department
of Biological Sciences, Indian Institute
of Science Education and Research (IISER) Kolkata, Mohanpur741246, India
- Department
of Chemistry, Oklahoma State University, Stillwater, Oklahoma74078, United States
| | - Neelanjana Sengupta
- Department
of Biological Sciences, Indian Institute
of Science Education and Research (IISER) Kolkata, Mohanpur741246, India
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12
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Salahub DR. Multiscale molecular modelling: from electronic structure to dynamics of nanosystems and beyond. Phys Chem Chem Phys 2022; 24:9051-9081. [PMID: 35389399 DOI: 10.1039/d1cp05928a] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Important contemporary biological and materials problems often depend on interactions that span orders of magnitude differences in spatial and temporal dimensions. This Tutorial Review attempts to provide an introduction to such fascinating problems through a series of case studies, aimed at beginning researchers, graduate students, postdocs and more senior colleagues who are changing direction to focus on multiscale aspects of their research. The choice of specific examples is highly personal, with examples either chosen from our own work or outstanding multiscale efforts from the literature. I start with various embedding schemes, as exemplified by polarizable continuum models, 3-D RISM, molecular DFT and frozen-density embedding. Next, QM/MM (quantum mechanical/molecular mechanical) techniques are the workhorse of pm-to-nm/ps-to-ns simulations; examples are drawn from enzymes and from nanocatalysis for oil-sands upgrading. Using polarizable force-fields in the QM/MM framework represents a burgeoning subfield; with examples from ion channels and electron dynamics in molecules subject to strong external fields, probing the atto-second dynamics of the electrons with RT-TDDFT (real-time - time-dependent density functional theory) eventually coupled with nuclear motion through the Ehrenfest approximation. This is followed by a section on coarse graining, bridging dimensions from atoms to cells. The penultimate chapter gives a quick overview of multiscale approaches that extend into the meso- and macro-scales, building on atomistic and coarse-grained techniques to enter the world of materials engineering, on the one hand, and cell biology, on the other. A final chapter gives just a glimpse of the burgeoning impact of machine learning on the structure-dynamics front. I aim to capture the excitement of contemporary leading-edge breakthroughs in the description of physico-chemical systems and processes in complex environments, with only enough historical content to provide context and aid the next generation of methodological development. While I aim also for a clear description of the essence of methodological breakthroughs, equations are kept to a minimum and detailed formalism and implementation details are left to the references. My approach is very selective (case studies) rather than exhaustive. I think that these case studies should provide fodder to build as complete a reference tree on multiscale modelling as the reader may wish, through forward and backward citation analysis. I hope that my choices of cases will excite interest in newcomers and help to fuel the growth of multiscale modelling in general.
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Affiliation(s)
- Dennis R Salahub
- Department of Chemistry, Department of Physics and Astronomy, CMS-Centre for Molecular Simulation, IQST-Institute for Quantum Science and Technology, Quantum Alberta, University of Calgary, Calgary, Alberta, T2N 1N4, Canada.
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13
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Vermaas JV, Mayne CG, Shinn E, Tajkhorshid E. Assembly and Analysis of Cell-Scale Membrane Envelopes. J Chem Inf Model 2022; 62:602-617. [PMID: 34910495 PMCID: PMC8903035 DOI: 10.1021/acs.jcim.1c01050] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The march toward exascale computing will enable routine molecular simulation of larger and more complex systems, for example, simulation of entire viral particles, on the scale of approximately billions of atoms─a simulation size commensurate with a small bacterial cell. Anticipating the future hardware capabilities that will enable this type of research and paralleling advances in experimental structural biology, efforts are currently underway to develop software tools, procedures, and workflows for constructing cell-scale structures. Herein, we describe our efforts in developing and implementing an efficient and robust workflow for construction of cell-scale membrane envelopes and embedding membrane proteins into them. A new approach for construction of massive membrane structures that are stable during the simulations is built on implementing a subtractive assembly technique coupled with the development of a structure concatenation tool (fastmerge), which eliminates overlapping elements based on volumetric criteria rather than adding successive molecules to the simulation system. Using this approach, we have constructed two "protocells" consisting of MARTINI coarse-grained beads to represent cellular membranes, one the size of a cellular organelle and another the size of a small bacterial cell. The membrane envelopes constructed here remain whole during the molecular dynamics simulations performed and exhibit water flux only through specific proteins, demonstrating the success of our methodology in creating tight cell-like membrane compartments. Extended simulations of these cell-scale structures highlight the propensity for nonspecific interactions between adjacent membrane proteins leading to the formation of protein microclusters on the cell surface, an insight uniquely enabled by the scale of the simulations. We anticipate that the experiences and best practices presented here will form the basis for the next generation of cell-scale models, which will begin to address the addition of soluble proteins, nucleic acids, and small molecules essential to the function of a cell.
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Affiliation(s)
- Josh V. Vermaas
- Biosciences Center, National Renewable Energy Laboratory, Golden, CO 80401
| | - Christopher G. Mayne
- Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, Department of Biochemistry, and Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801
| | - Eric Shinn
- Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, Department of Biochemistry, and Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801
| | - Emad Tajkhorshid
- Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, Department of Biochemistry, and Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801
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14
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Maritan M, Autin L, Karr J, Covert MW, Olson AJ, Goodsell DS. Building Structural Models of a Whole Mycoplasma Cell. J Mol Biol 2022; 434:167351. [PMID: 34774566 PMCID: PMC8752489 DOI: 10.1016/j.jmb.2021.167351] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 11/04/2021] [Accepted: 11/05/2021] [Indexed: 02/01/2023]
Abstract
Building structural models of entire cells has been a long-standing cross-discipline challenge for the research community, as it requires an unprecedented level of integration between multiple sources of biological data and enhanced methods for computational modeling and visualization. Here, we present the first 3D structural models of an entire Mycoplasma genitalium (MG) cell, built using the CellPACK suite of computational modeling tools. Our model recapitulates the data described in recent whole-cell system biology simulations and provides a structural representation for all MG proteins, DNA and RNA molecules, obtained by combining experimental and homology-modeled structures and lattice-based models of the genome. We establish a framework for gathering, curating and evaluating these structures, exposing current weaknesses of modeling methods and the boundaries of MG structural knowledge, and visualization methods to explore functional characteristics of the genome and proteome. We compare two approaches for data gathering, a manually-curated workflow and an automated workflow that uses homologous structures, both of which are appropriate for the analysis of mesoscale properties such as crowding and volume occupancy. Analysis of model quality provides estimates of the regularization that will be required when these models are used as starting points for atomic molecular dynamics simulations.
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Affiliation(s)
- Martina Maritan
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA. https://twitter.com/MartinaMaritan
| | - Ludovic Autin
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA. https://twitter.com/grinche
| | - Jonathan Karr
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Arthur J Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA
| | - David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA; RCSB Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA.
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15
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Rahmaninejad H, Pace T, Chun BJ, Kekenes-Huskey PM. Crowding within synaptic junctions influences the degradation of nucleotides by CD39 and CD73 ectonucleotidases. Biophys J 2022; 121:309-318. [PMID: 34922916 PMCID: PMC8790186 DOI: 10.1016/j.bpj.2021.12.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 11/01/2021] [Accepted: 12/07/2021] [Indexed: 01/21/2023] Open
Abstract
Synapsed cells can communicate using exocytosed nucleotides like adenosine triphosphate (ATP). Ectonucleotidases localized to synaptic junctions degrade nucleotides into metabolites like adenosine monophosphate (AMP) or adenosine. Oftentimes nucleotide degradation occurs in a sequential manner, of which ATP degradation by CD39 and CD73 is a representative example. Here, CD39 first converts ATP and adenosine diphosphate (ADP) into AMP, after which AMP is dephosphorylated into adenosine by CD73. Hence, the concerted activity of CD39 and CD73 can help shape cellular responses to extracellular ATP. In a previous study, we demonstrated that coupled CD39 and CD73 activity within synapse-like junctions is strongly controlled by the enzymes' co-localization, their surface charge densities, and the electrostatic potential of the surrounding cell membranes. In this study, we demonstrate that crowders within synaptic junctions, which can include globular proteins like cytokines and membrane-bound proteins, impact coupled CD39 and CD73 ectonucleotidase activity and, in turn, the availability of intrasynapse ATP. Specifically, we developed a spatially explicit, reaction-diffusion model for the coupled conversion of ATP → AMP and AMP → adenosine in a model synaptic junction with crowders that is solved via the finite element method. Our modeling results suggest that the association rate for ATP to CD39 is strongly influenced by the density of intrasynaptic protein crowders, as increasing crowder density generally suppressed ATP association kinetics. Much of this suppression can be rationalized based on a loss of configurational entropy. The surface charges of crowders can further influence the association rate, with the surprising result that favorable crowder-nucleotide electrostatic interactions can yield CD39 association rates that are faster than crowder-free configurations. However, attractive crowder-nucleotide interactions decrease the rate and efficiency of adenosine production, which in turn increases the availability of ATP and AMP within the synapse relative to crowder-free configurations. These findings highlight how CD39 and CD73 ectonucleotidase activity, electrostatics, and crowding within synapses influence the availability of nucleotides for intercellular communication.
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Affiliation(s)
- Hadi Rahmaninejad
- Department of Physics, Virginia Tech, Blacksburg,Corresponding author
| | - Tom Pace
- Department of Cell & Molecular Physiology, Loyola University Chicago, Chicago,Corresponding author
| | - Byeong Jae Chun
- Department of Cell & Molecular Physiology, Loyola University Chicago, Chicago
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16
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Wilson E, Vant J, Layton J, Boyd R, Lee H, Turilli M, Hernández B, Wilkinson S, Jha S, Gupta C, Sarkar D, Singharoy A. Large-Scale Molecular Dynamics Simulations of Cellular Compartments. Methods Mol Biol 2021; 2302:335-356. [PMID: 33877636 DOI: 10.1007/978-1-0716-1394-8_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
Molecular dynamics or MD simulation is gradually maturing into a tool for constructing in vivo models of living cells in atomistic details. The feasibility of such models is bolstered by integrating the simulations with data from microscopic, tomographic and spectroscopic experiments on exascale supercomputers, facilitated by the use of deep learning technologies. Over time, MD simulation has evolved from tens of thousands of atoms to over 100 million atoms comprising an entire cell organelle, a photosynthetic chromatophore vesicle from a purple bacterium. In this chapter, we present a step-by-step outline for preparing, executing and analyzing such large-scale MD simulations of biological systems that are essential to life processes. All scripts are provided via GitHub.
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Affiliation(s)
- Eric Wilson
- The School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
| | - John Vant
- The School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
| | - Jacob Layton
- The School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
| | - Ryan Boyd
- The School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
| | - Hyungro Lee
- RADICAL, ECE, Rutgers University, Piscataway, NJ, USA
| | | | | | | | - Shantenu Jha
- RADICAL, ECE, Rutgers University, Piscataway, NJ, USA.,Brookhaven National Laboratory, Upton, NY, USA
| | - Chitrak Gupta
- The School of Molecular Sciences, Arizona State University, Tempe, AZ, USA.
| | - Daipayan Sarkar
- The School of Molecular Sciences, Arizona State University, Tempe, AZ, USA. .,Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
| | - Abhishek Singharoy
- The School of Molecular Sciences, Arizona State University, Tempe, AZ, USA.
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17
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Kadir SR, Lilja A, Gunn N, Strong C, Hughes RT, Bailey BJ, Rae J, Parton RG, McGhee J. Nanoscape, a data-driven 3D real-time interactive virtual cell environment. eLife 2021; 10:64047. [PMID: 34191720 PMCID: PMC8245131 DOI: 10.7554/elife.64047] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 06/04/2021] [Indexed: 12/15/2022] Open
Abstract
Our understanding of cellular and structural biology has reached unprecedented levels of detail, and computer visualisation techniques can be used to create three-dimensional (3D) representations of cells and their environment that are useful in both teaching and research. However, extracting and integrating the relevant scientific data, and then presenting them in an effective way, can pose substantial computational and aesthetic challenges. Here we report how computer artists, experts in computer graphics and cell biologists have collaborated to produce a tool called Nanoscape that allows users to explore and interact with 3D representations of cells and their environment that are both scientifically accurate and visually appealing. We believe that using Nanoscape as an immersive learning application will lead to an improved understanding of the complexities of cellular scales, densities and interactions compared with traditional learning modalities.
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Affiliation(s)
- Shereen R Kadir
- 3D Visualisation Aesthetics Lab, School of Art and Design, and the ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, University of New South Wales, Sydney, Australia
| | - Andrew Lilja
- 3D Visualisation Aesthetics Lab, School of Art and Design, and the ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, University of New South Wales, Sydney, Australia
| | - Nick Gunn
- 3D Visualisation Aesthetics Lab, School of Art and Design, and the ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, University of New South Wales, Sydney, Australia
| | - Campbell Strong
- 3D Visualisation Aesthetics Lab, School of Art and Design, and the ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, University of New South Wales, Sydney, Australia
| | - Rowan T Hughes
- 3D Visualisation Aesthetics Lab, School of Art and Design, and the ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, University of New South Wales, Sydney, Australia
| | - Benjamin J Bailey
- 3D Visualisation Aesthetics Lab, School of Art and Design, and the ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, University of New South Wales, Sydney, Australia
| | - James Rae
- Institute for Molecular Bioscience, ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, and Centre for Microscopy and Microanalysis, University of Queensland, Brisbane, Australia
| | - Robert G Parton
- Institute for Molecular Bioscience, ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, and Centre for Microscopy and Microanalysis, University of Queensland, Brisbane, Australia
| | - John McGhee
- 3D Visualisation Aesthetics Lab, School of Art and Design, and the ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, University of New South Wales, Sydney, Australia
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18
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O'Donoghue SI. Grand Challenges in Bioinformatics Data Visualization. FRONTIERS IN BIOINFORMATICS 2021; 1:669186. [PMID: 36303723 PMCID: PMC9581027 DOI: 10.3389/fbinf.2021.669186] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 04/30/2021] [Indexed: 01/17/2023] Open
Affiliation(s)
- Seán I. O'Donoghue
- Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Kensington, NSW, Australia
- CSIRO Data61, Eveleigh, NSW, Australia
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19
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Stracy M, Schweizer J, Sherratt DJ, Kapanidis AN, Uphoff S, Lesterlin C. Transient non-specific DNA binding dominates the target search of bacterial DNA-binding proteins. Mol Cell 2021; 81:1499-1514.e6. [PMID: 33621478 PMCID: PMC8022225 DOI: 10.1016/j.molcel.2021.01.039] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 11/24/2020] [Accepted: 01/27/2021] [Indexed: 12/18/2022]
Abstract
Despite their diverse biochemical characteristics and functions, all DNA-binding proteins share the ability to accurately locate their target sites among the vast excess of non-target DNA. Toward identifying universal mechanisms of the target search, we used single-molecule tracking of 11 diverse DNA-binding proteins in living Escherichia coli. The mobility of these proteins during the target search was dictated by DNA interactions rather than by their molecular weights. By generating cells devoid of all chromosomal DNA, we discovered that the nucleoid is not a physical barrier for protein diffusion but significantly slows the motion of DNA-binding proteins through frequent short-lived DNA interactions. The representative DNA-binding proteins (irrespective of their size, concentration, or function) spend the majority (58%–99%) of their search time bound to DNA and occupy as much as ∼30% of the chromosomal DNA at any time. Chromosome crowding likely has important implications for the function of all DNA-binding proteins. Protein motion was compared between unperturbed cells and DNA-free cells Protein mobility was dictated by DNA interactions rather than molecular weight The nucleoid is not a physical barrier for protein diffusion The proteins studied spend most (58%–99%) of their search time bound to DNA
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Affiliation(s)
- Mathew Stracy
- Department of Biochemistry, University of Oxford, Oxford OX1 3QU, UK.
| | - Jakob Schweizer
- Max Planck Institute for Dynamics of Complex Technical Systems, 39106 Magdeburg, Germany
| | - David J Sherratt
- Department of Biochemistry, University of Oxford, Oxford OX1 3QU, UK
| | - Achillefs N Kapanidis
- Biological Physics Research Group, Clarendon Laboratory, Department of Physics, University of Oxford, Oxford OX1 3PU, UK
| | - Stephan Uphoff
- Department of Biochemistry, University of Oxford, Oxford OX1 3QU, UK.
| | - Christian Lesterlin
- Molecular Microbiology and Structural Biochemistry (MMSB), Université Lyon 1, CNRS, INSERM, UMR5086, 69007 Lyon, France.
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20
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Dutagaci B, Nawrocki G, Goodluck J, Ashkarran AA, Hoogstraten CG, Lapidus LJ, Feig M. Charge-driven condensation of RNA and proteins suggests broad role of phase separation in cytoplasmic environments. eLife 2021; 10:64004. [PMID: 33496264 PMCID: PMC7877912 DOI: 10.7554/elife.64004] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 01/25/2021] [Indexed: 02/06/2023] Open
Abstract
Phase separation processes are increasingly being recognized as important organizing mechanisms of biological macromolecules in cellular environments. Well-established drivers of phase separation are multi-valency and intrinsic disorder. Here, we show that globular macromolecules may condense simply based on electrostatic complementarity. More specifically, phase separation of mixtures between RNA and positively charged proteins is described from a combination of multiscale computer simulations with microscopy and spectroscopy experiments. Phase diagrams were mapped out as a function of molecular concentrations in experiment and as a function of molecular size and temperature via simulations. The resulting condensates were found to retain at least some degree of internal dynamics varying as a function of the molecular composition. The results suggest a more general principle for phase separation that is based primarily on electrostatic complementarity without invoking polymer properties as in most previous studies. Simulation results furthermore suggest that such phase separation may occur widely in heterogenous cellular environment between nucleic acid and protein components.
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Affiliation(s)
- Bercem Dutagaci
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, United States
| | - Grzegorz Nawrocki
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, United States
| | - Joyce Goodluck
- Department of Physics, Michigan State University, East Lansing, United States
| | - Ali Akbar Ashkarran
- Precision Health Program and Department of Radiology, Michigan State University, East Lansing, United States
| | - Charles G Hoogstraten
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, United States
| | - Lisa J Lapidus
- Department of Physics, Michigan State University, East Lansing, United States
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, United States
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21
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Marucci L, Barberis M, Karr J, Ray O, Race PR, de Souza Andrade M, Grierson C, Hoffmann SA, Landon S, Rech E, Rees-Garbutt J, Seabrook R, Shaw W, Woods C. Computer-Aided Whole-Cell Design: Taking a Holistic Approach by Integrating Synthetic With Systems Biology. Front Bioeng Biotechnol 2020; 8:942. [PMID: 32850764 PMCID: PMC7426639 DOI: 10.3389/fbioe.2020.00942] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 07/21/2020] [Indexed: 01/03/2023] Open
Abstract
Computer-aided design (CAD) for synthetic biology promises to accelerate the rational and robust engineering of biological systems. It requires both detailed and quantitative mathematical and experimental models of the processes to (re)design biology, and software and tools for genetic engineering and DNA assembly. Ultimately, the increased precision in the design phase will have a dramatic impact on the production of designer cells and organisms with bespoke functions and increased modularity. CAD strategies require quantitative models of cells that can capture multiscale processes and link genotypes to phenotypes. Here, we present a perspective on how whole-cell, multiscale models could transform design-build-test-learn cycles in synthetic biology. We show how these models could significantly aid in the design and learn phases while reducing experimental testing by presenting case studies spanning from genome minimization to cell-free systems. We also discuss several challenges for the realization of our vision. The possibility to describe and build whole-cells in silico offers an opportunity to develop increasingly automatized, precise and accessible CAD tools and strategies.
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Affiliation(s)
- Lucia Marucci
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom.,School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom.,Bristol Centre for Synthetic Biology (BrisSynBio), University of Bristol, Bristol, United Kingdom
| | - Matteo Barberis
- Systems Biology, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom.,Centre for Mathematical and Computational Biology, CMCB, University of Surrey, Guildford, United Kingdom.,Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Jonathan Karr
- Icahn Institute for Data Science and Genomic Technology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Oliver Ray
- Department of Computer Science, University of Bristol, Bristol, United Kingdom
| | - Paul R Race
- Bristol Centre for Synthetic Biology (BrisSynBio), University of Bristol, Bristol, United Kingdom.,School of Biochemistry, University of Bristol, Bristol, United Kingdom
| | - Miguel de Souza Andrade
- Brazilian Agricultural Research Corporation/National Institute of Science and Technology - Synthetic Biology, Brasília, Brazil.,Department of Cell Biology, Institute of Biological Sciences, University of Brasília, Brasília, Brazil
| | - Claire Grierson
- Bristol Centre for Synthetic Biology (BrisSynBio), University of Bristol, Bristol, United Kingdom.,School of Biological Sciences, University of Bristol, Bristol, United Kingdom
| | - Stefan Andreas Hoffmann
- Manchester Institute of Biotechnology, The University of Manchester, Manchester, United Kingdom
| | - Sophie Landon
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom.,Bristol Centre for Synthetic Biology (BrisSynBio), University of Bristol, Bristol, United Kingdom
| | - Elibio Rech
- Brazilian Agricultural Research Corporation/National Institute of Science and Technology - Synthetic Biology, Brasília, Brazil
| | - Joshua Rees-Garbutt
- Bristol Centre for Synthetic Biology (BrisSynBio), University of Bristol, Bristol, United Kingdom.,School of Biological Sciences, University of Bristol, Bristol, United Kingdom
| | - Richard Seabrook
- Elizabeth Blackwell Institute for Health Research (EBI), University of Bristol, Bristol, United Kingdom
| | - William Shaw
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Christopher Woods
- Bristol Centre for Synthetic Biology (BrisSynBio), University of Bristol, Bristol, United Kingdom.,School of Chemistry, University of Bristol, Bristol, United Kingdom
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22
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Goodsell DS, Olson AJ, Forli S. Art and Science of the Cellular Mesoscale. Trends Biochem Sci 2020; 45:472-483. [PMID: 32413324 PMCID: PMC7230070 DOI: 10.1016/j.tibs.2020.02.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 02/12/2020] [Accepted: 02/27/2020] [Indexed: 12/22/2022]
Abstract
Experimental information from microscopy, structural biology, and bioinformatics may be integrated to build structural models of entire cells with molecular detail. This integrative modeling is challenging in several ways: the intrinsic complexity of biology results in models with many closely packed and heterogeneous components; the wealth of available experimental data is scattered among multiple resources and must be gathered, reconciled, and curated; and computational infrastructure is only now gaining the capability of modeling and visualizing systems of this complexity. We present recent efforts to address these challenges, both with artistic approaches to depicting the cellular mesoscale, and development and application of methods to build quantitative models.
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Affiliation(s)
- David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA; Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA.
| | - Arthur J Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
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23
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Takahashi S, Sugimoto N. Stability prediction of canonical and non-canonical structures of nucleic acids in various molecular environments and cells. Chem Soc Rev 2020; 49:8439-8468. [DOI: 10.1039/d0cs00594k] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
This review provides the biophysicochemical background and recent advances in stability prediction of canonical and non-canonical structures of nucleic acids in various molecular environments and cells.
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Affiliation(s)
- Shuntaro Takahashi
- Frontier Institute for Biomolecular Engineering Research (FIBER)
- Konan University
- Kobe
- Japan
| | - Naoki Sugimoto
- Frontier Institute for Biomolecular Engineering Research (FIBER)
- Konan University
- Kobe
- Japan
- Graduate School of Frontiers of Innovative Research in Science and Technology (FIRST)
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24
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Oliveira Bortot L, Bashardanesh Z, van der Spoel D. Making Soup: Preparing and Validating Models of the Bacterial Cytoplasm for Molecular Simulation. J Chem Inf Model 2019; 60:322-331. [DOI: 10.1021/acs.jcim.9b00971] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Leandro Oliveira Bortot
- Laboratory of Biological Physics, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Avenida do Café s/n, 14040-903 Ribeirão Preto-SP, Brazil
| | - Zahedeh Bashardanesh
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-75124 Uppsala, Sweden
| | - David van der Spoel
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-75124 Uppsala, Sweden
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25
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Kompella VPS, Stansfield I, Romano MC, Mancera RL. Definition of the Minimal Contents for the Molecular Simulation of the Yeast Cytoplasm. Front Mol Biosci 2019; 6:97. [PMID: 31632983 PMCID: PMC6783697 DOI: 10.3389/fmolb.2019.00097] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 09/11/2019] [Indexed: 11/13/2022] Open
Abstract
The cytoplasm is a densely packed environment filled with macromolecules with hindered diffusion. Molecular simulation of the diffusion of biomolecules under such macromolecular crowding conditions requires the definition of a simulation cell with a cytoplasmic-like composition. This has been previously done for prokaryote cells (E. coli) but not for eukaryote cells such as yeast as a model organism. Yeast proteomics datasets vary widely in terms of cell growth conditions, the technique used to determine protein composition, the reported relative abundance of proteins, and the units in which abundances are reported. We determined that the gene ontology profiles of the most abundant proteins across these datasets are similar, but their abundances vary greatly. To overcome this problem, we chose five mass spectrometry proteomics datasets that fulfilled the following criteria: high internal consistency, consistency with published experimental data, and freedom from GFP-tagging artifacts. Using these datasets, the contents of a simulation cell containing a single 80S ribosome were defined, such that the macromolecular density and the mass ratio of ribosomal-to-cytoplasmic proteins were consistent with experiment and chosen datasets. Finally, multiple tRNAs were added, consistent with their experimentally-determined number in the yeast cell. The resulting composition can be readily used in molecular simulations representative of yeast cytoplasmic macromolecular crowding conditions to characterize a variety of phenomena, such as protein diffusion, protein-protein interactions and biological processes such as protein translation.
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Affiliation(s)
- Vijay Phanindra Srikanth Kompella
- School of Pharmacy and Biomedical Sciences, Curtin Health Innovation Research Institute, Curtin Institute for Computation, Curtin University, Perth, WA, Australia.,Physics Department, Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, United Kingdom
| | - Ian Stansfield
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, United Kingdom
| | - Maria Carmen Romano
- Physics Department, Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, United Kingdom.,Institute of Medical Sciences, University of Aberdeen, Aberdeen, United Kingdom
| | - Ricardo L Mancera
- School of Pharmacy and Biomedical Sciences, Curtin Health Innovation Research Institute, Curtin Institute for Computation, Curtin University, Perth, WA, Australia
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26
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Abstract
Comprehensive data about the composition and structure of cellular components have enabled the construction of quantitative whole-cell models. While kinetic network-type models have been established, it is also becoming possible to build physical, molecular-level models of cellular environments. This review outlines challenges in constructing and simulating such models and discusses near- and long-term opportunities for developing physical whole-cell models that can connect molecular structure with biological function.
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Affiliation(s)
- Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, USA;
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047, Japan
| | - Yuji Sugita
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047, Japan
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama 351-0198, Japan
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27
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Guin D, Gruebele M. Weak Chemical Interactions That Drive Protein Evolution: Crowding, Sticking, and Quinary Structure in Folding and Function. Chem Rev 2019; 119:10691-10717. [PMID: 31356058 DOI: 10.1021/acs.chemrev.8b00753] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
In recent years, better instrumentation and greater computing power have enabled the imaging of elusive biomolecule dynamics in cells, driving many advances in understanding the chemical organization of biological systems. The focus of this Review is on interactions in the cell that affect both biomolecular stability and function and modulate them. The same protein or nucleic acid can behave differently depending on the time in the cell cycle, the location in a specific compartment, or the stresses acting on the cell. We describe in detail the crowding, sticking, and quinary structure in the cell and the current methods to quantify them both in vitro and in vivo. Finally, we discuss protein evolution in the cell in light of current biophysical evidence. We describe the factors that drive protein evolution and shape protein interaction networks. These interactions can significantly affect the free energy, ΔG, of marginally stable and low-population proteins and, due to epistasis, direct the evolutionary pathways in an organism. We finally conclude by providing an outlook on experiments to come and the possibility of collaborative evolutionary biology and biophysical efforts.
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Affiliation(s)
- Drishti Guin
- Department of Chemistry , University of Illinois , Urbana , Illinois 61801 , United States
| | - Martin Gruebele
- Department of Chemistry , University of Illinois , Urbana , Illinois 61801 , United States.,Department of Physics , University of Illinois , Urbana , Illinois 61801 , United States.,Center for Biophysics and Quantitative Biology , University of Illinois , Urbana , Illinois 61801 , United States
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28
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Computational approaches to macromolecular interactions in the cell. Curr Opin Struct Biol 2019; 55:59-65. [PMID: 30999240 DOI: 10.1016/j.sbi.2019.03.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2018] [Accepted: 03/08/2019] [Indexed: 12/15/2022]
Abstract
Structural modeling of a cell is an evolving strategic direction in computational structural biology. It takes advantage of new powerful modeling techniques, deeper understanding of fundamental principles of molecular structure and assembly, and rapid growth of the amount of structural data generated by experimental techniques. Key modeling approaches to principal types of macromolecular assemblies in a cell already exist. The main challenge, along with the further development of these modeling approaches, is putting them together in a consistent, unified whole cell model. This opinion piece addresses the fundamental aspects of modeling macromolecular assemblies in a cell, and the state-of-the-art in modeling of the principal types of such assemblies.
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29
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Yildirim A, Brenner N, Sutherland R, Feig M. Role of protein interactions in stabilizing canonical DNA features in simulations of DNA in crowded environments. BMC BIOPHYSICS 2018; 11:8. [PMID: 30555686 PMCID: PMC6286541 DOI: 10.1186/s13628-018-0048-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 11/21/2018] [Indexed: 11/28/2022]
Abstract
Background Cellular environments are highly crowded with biological macromolecules resulting in frequent non-specific interactions. While the effect of such crowding on protein structure and dynamics has been studied extensively, very little is known how cellular crowding affects the conformational sampling of nucleic acids. Results The effect of protein crowding on the conformational preferences of DNA (deoxyribonucleic acid) is described from fully atomistic molecular dynamics simulations of systems containing a DNA dodecamer surrounded by protein crowders. From the simulations, it was found that DNA structures prefer to stay in B-like conformations in the presence of the crowders. The preference for B-like conformations results from non-specific interactions of crowder proteins with the DNA sugar-phosphate backbone. Moreover, the simulations suggest that the crowder interactions narrow the conformational sampling to canonical regions of the conformational space. Conclusions The overall conclusion is that crowding effects may stabilize the canonical features of DNA that are most important for biological function. The results are complementary to a previous study of DNA in reduced dielectric environments where reduced dielectric environments alone led to a conformational shift towards A-DNA. Such a shift was not observed here suggested that the reduced dielectric response of cellular environments is counteracted by non-specific interactions with protein crowders under in vivo conditions. Electronic supplementary material The online version of this article (10.1186/s13628-018-0048-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Asli Yildirim
- 1Department of Chemistry, Michigan State University, East Lansing, MI 48824 USA
| | - Nathalie Brenner
- 2Faculty of Mathematics and Natural Sciences, University of Düsseldorf, 40225 Düsseldorf, Germany.,3Department of Biochemistry & Molecular Biology, Michigan State University, 603 Wilson Road, Room BCH 218, East Lansing, MI 48824 USA
| | - Robert Sutherland
- 3Department of Biochemistry & Molecular Biology, Michigan State University, 603 Wilson Road, Room BCH 218, East Lansing, MI 48824 USA
| | - Michael Feig
- 3Department of Biochemistry & Molecular Biology, Michigan State University, 603 Wilson Road, Room BCH 218, East Lansing, MI 48824 USA
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30
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Klauda JB. Perspective: Computational modeling of accurate cellular membranes with molecular resolution. J Chem Phys 2018; 149:220901. [DOI: 10.1063/1.5055007] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Affiliation(s)
- Jeffery B. Klauda
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, Maryland 20742, USA
- Biophysics Graduate Program, University of Maryland, College Park, Maryland 20742, USA
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31
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Olson AJ. Perspectives on Structural Molecular Biology Visualization: From Past to Present. J Mol Biol 2018; 430:3997-4012. [PMID: 30009769 PMCID: PMC6186497 DOI: 10.1016/j.jmb.2018.07.009] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2018] [Revised: 07/03/2018] [Accepted: 07/06/2018] [Indexed: 12/15/2022]
Abstract
Visualization has been a key technology in the progress of structural molecular biology for as long as the field has existed. This perspective describes the nature of the visualization process in structural studies, how it has evolved over the years, and its relationship to the changes in technology that have supported and driven it. It focuses on how technical advances have changed the way we look at and interact with molecular structure, and how structural biology has fostered and challenged that technology.
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Affiliation(s)
- Arthur J Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.
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32
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Feig M, Nawrocki G, Yu I, Wang PH, Sugita Y. Challenges and opportunities in connecting simulations with experiments via molecular dynamics of cellular environments. JOURNAL OF PHYSICS. CONFERENCE SERIES 2018; 1036:012010. [PMID: 30613205 PMCID: PMC6319911 DOI: 10.1088/1742-6596/1036/1/012010] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Computer simulations are widely used to study molecular systems, especially in biology. As simulations have greatly increased in scale reaching cellular levels there are now significant challenges in managing, analyzing, and interpreting such data in comparison with experiments that are being discussed. Management challenges revolve around storing and sharing terabyte to petabyte scale data sets whereas the analysis of simulations of highly complex systems will increasingly require automated machine learning and artificial intelligence approaches. The comparison between simulations and experiments is furthermore complicated not just by the complexity of the data but also by difficulties in interpreting experiments for highly heterogeneous systems. As an example, the interpretation of NMR relaxation measurements and comparison with simulations for highly crowded systems is discussed.
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Affiliation(s)
- Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824 USA
- Quantitative Biology Center, RIKEN, Kobe, Japan
| | - Grzegorz Nawrocki
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824 USA
| | - Isseki Yu
- Theoretical Molecular Science Laboratory, RIKEN, Wako, Japan
- iTHES Research Group, RIKEN, Wako, Japan
| | - Po-hung Wang
- Theoretical Molecular Science Laboratory, RIKEN, Wako, Japan
| | - Yuji Sugita
- Quantitative Biology Center, RIKEN, Kobe, Japan
- Theoretical Molecular Science Laboratory, RIKEN, Wako, Japan
- iTHES Research Group, RIKEN, Wako, Japan
- Advanced Institute for Computational Science, RIKEN, Kobe, Japan
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33
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Yu I, Feig M, Sugita Y. High-Performance Data Analysis on the Big Trajectory Data of Cellular Scale All-atom Molecular Dynamics Simulations. ACTA ACUST UNITED AC 2018; 1036. [PMID: 30613206 DOI: 10.1088/1742-6596/1036/1/012009] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
The inside of a cell is highly crowded with a large number of macromolecules together with solvents and metabolites. To know the molecular-level behaviour of biomolecules in such dense crowding environment, we constructed full atomistic model of the cytoplasm of bacteria, and performed massive all-atom molecular dynamics (MD) simulations. On the other hand, to analyse such big MD data, we need significant computational power and efficient calculation methodology. Here, we introduce what and how we analyse the biomolecule properties from the big trajectory data produced by cellular scale all-atom MD simulations.
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Affiliation(s)
- Isseki Yu
- iTHES Research Group, RIKEN, Saitama, Japan.,Theoretical Molecular Science Laboratory, RIKEN, Saitama, Japan
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, United States
| | - Yuji Sugita
- iTHES Research Group, RIKEN, Saitama, Japan.,Theoretical Molecular Science Laboratory, RIKEN, Saitama, Japan.,Laboratory for Biomolecular Function Simulation, RIKEN Quantitative Biology Center, Kobe, Japan.,Computational Biophysics Research Team, RIKEN Advanced Institute for Computational Science, Kobe, Japan
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34
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Solernou A, Hanson BS, Richardson RA, Welch R, Read DJ, Harlen OG, Harris SA. Fluctuating Finite Element Analysis (FFEA): A continuum mechanics software tool for mesoscale simulation of biomolecules. PLoS Comput Biol 2018; 14:e1005897. [PMID: 29570700 PMCID: PMC5891030 DOI: 10.1371/journal.pcbi.1005897] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 04/09/2018] [Accepted: 11/27/2017] [Indexed: 12/31/2022] Open
Abstract
Fluctuating Finite Element Analysis (FFEA) is a software package designed to perform continuum mechanics simulations of proteins and other globular macromolecules. It combines conventional finite element methods with stochastic thermal noise, and is appropriate for simulations of large proteins and protein complexes at the mesoscale (length-scales in the range of 5 nm to 1 μm), where there is currently a paucity of modelling tools. It requires 3D volumetric information as input, which can be low resolution structural information such as cryo-electron tomography (cryo-ET) maps or much higher resolution atomistic co-ordinates from which volumetric information can be extracted. In this article we introduce our open source software package for performing FFEA simulations which we have released under a GPLv3 license. The software package includes a C ++ implementation of FFEA, together with tools to assist the user to set up the system from Electron Microscopy Data Bank (EMDB) or Protein Data Bank (PDB) data files. We also provide a PyMOL plugin to perform basic visualisation and additional Python tools for the analysis of FFEA simulation trajectories. This manuscript provides a basic background to the FFEA method, describing the implementation of the core mechanical model and how intermolecular interactions and the solvent environment are included within this framework. We provide prospective FFEA users with a practical overview of how to set up an FFEA simulation with reference to our publicly available online tutorials and manuals that accompany this first release of the package.
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Affiliation(s)
- Albert Solernou
- School of Physics and Astronomy, University of Leeds, Leeds, United Kingdom
| | - Benjamin S. Hanson
- School of Physics and Astronomy, University of Leeds, Leeds, United Kingdom
| | | | - Robert Welch
- School of Physics and Astronomy, University of Leeds, Leeds, United Kingdom
| | - Daniel J. Read
- School of Mathematics, University of Leeds, Leeds, United Kingdom
| | - Oliver G. Harlen
- School of Mathematics, University of Leeds, Leeds, United Kingdom
| | - Sarah A. Harris
- School of Physics and Astronomy, University of Leeds, Leeds, United Kingdom
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, United Kingdom
- * E-mail:
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35
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Singla J, McClary KM, White KL, Alber F, Sali A, Stevens RC. Opportunities and Challenges in Building a Spatiotemporal Multi-scale Model of the Human Pancreatic β Cell. Cell 2018; 173:11-19. [PMID: 29570991 PMCID: PMC6014618 DOI: 10.1016/j.cell.2018.03.014] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 11/25/2017] [Accepted: 03/06/2018] [Indexed: 12/25/2022]
Abstract
The construction of a predictive model of an entire eukaryotic cell that describes its dynamic structure from atomic to cellular scales is a grand challenge at the intersection of biology, chemistry, physics, and computer science. Having such a model will open new dimensions in biological research and accelerate healthcare advancements. Developing the necessary experimental and modeling methods presents abundant opportunities for a community effort to realize this goal. Here, we present a vision for creation of a spatiotemporal multi-scale model of the pancreatic β-cell, a relevant target for understanding and modulating the pathogenesis of diabetes.
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Affiliation(s)
- Jitin Singla
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Kyle M McClary
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Kate L White
- Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Frank Alber
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA.
| | - Andrej Sali
- California Institute for Quantitative Biosciences, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Raymond C Stevens
- Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA.
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36
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Widmer LA, Stelling J. Bridging intracellular scales by mechanistic computational models. Curr Opin Biotechnol 2018; 52:17-24. [PMID: 29486391 DOI: 10.1016/j.copbio.2018.02.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Accepted: 02/11/2018] [Indexed: 12/31/2022]
Abstract
The impact of intracellular spatial organization beyond classical compartments on processes such as cell signaling is increasingly recognized. A quantitative, mechanistic understanding of cellular systems therefore needs to account for different scales in at least three coordinates: time, molecular abundances, and space. Mechanistic mathematical models may span all these scales, but corresponding multi-scale models need to resolve mechanistic details on small scales while maintaining computational tractability for larger ones. This typically results in models that combine different levels of description: from a microscopic representation of chemical reactions up to continuum dynamics in space and time. We highlight recent progress in bridging these model classes and outline current challenges in multi-scale models such as active transport and dynamic geometries.
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Affiliation(s)
- Lukas Andreas Widmer
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics, ETH Zürich, Basel, Switzerland; Systems Biology PhD Program, Life Science Zurich Graduate School, Zurich, Switzerland
| | - Jörg Stelling
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics, ETH Zürich, Basel, Switzerland.
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37
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Raschka S, Wolf AJ, Bemister-Buffington J, Kuhn LA. Protein–ligand interfaces are polarized: discovery of a strong trend for intermolecular hydrogen bonds to favor donors on the protein side with implications for predicting and designing ligand complexes. J Comput Aided Mol Des 2018; 32:511-528. [DOI: 10.1007/s10822-018-0105-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Accepted: 02/05/2018] [Indexed: 10/18/2022]
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38
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Abstract
Mesoscale molecular modeling is providing a new window into the inner workings of living cells. Modeling of genomes, however, remains a technical challenge, due to their large size and complexity. We describe a lattice method for rapid generation of bacterial nucleoid models that integrates experimental data from a variety of biophysical techniques and provides a starting point for simulation and hypothesis generation. The current method builds models of a circular bacterial genome with supercoiled plectonemes, packed within the small space of the bacterial cell. Lattice models are generated for Mycoplasma genitalium and Escherichia coli nucleoids, and used to simulate interaction data. The method is rapid enough to allow generation of multiple models when analyzing structure/function relationships, and we demonstrate use of the lattice models in creation of an all-atom representation of an entire cell.
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Affiliation(s)
- David S Goodsell
- Department of Integrative Structural and Computational Biology , The Scripps Research Institute , 10550 North Torrey Pines Road , La Jolla , California , United States.,Center for Integrative Proteomics Research , Rutgers State University , 174 Frelinghuysen Road , Piscataway , New Jersey , United States
| | - Ludovic Autin
- Department of Integrative Structural and Computational Biology , The Scripps Research Institute , 10550 North Torrey Pines Road , La Jolla , California , United States
| | - Arthur J Olson
- Department of Integrative Structural and Computational Biology , The Scripps Research Institute , 10550 North Torrey Pines Road , La Jolla , California , United States
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39
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Onufriev AV, Izadi S. Water models for biomolecular simulations. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2017. [DOI: 10.1002/wcms.1347] [Citation(s) in RCA: 94] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Alexey V. Onufriev
- Department of Physics; Virginia Tech; Blacksburg VA USA
- Department of Computer Science; Virginia Tech; Blacksburg VA USA
- Center for Soft Matter and Biological Physics; Virginia Tech; Blacksburg VA USA
| | - Saeed Izadi
- Early Stage Pharmaceutical Development; Genentech Inc.; South San Francisco, CA USA
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40
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Hacker WC, Li S, Elcock AH. Features of genomic organization in a nucleotide-resolution molecular model of the Escherichia coli chromosome. Nucleic Acids Res 2017. [PMID: 28645155 PMCID: PMC5570083 DOI: 10.1093/nar/gkx541] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
We describe structural models of the Escherichia coli chromosome in which the positions of all 4.6 million nucleotides of each DNA strand are resolved. Models consistent with two basic chromosomal orientations, differing in their positioning of the origin of replication, have been constructed. In both types of model, the chromosome is partitioned into plectoneme-abundant and plectoneme-free regions, with plectoneme lengths and branching patterns matching experimental distributions, and with spatial distributions of highly-transcribed chromosomal regions matching recent experimental measurements of the distribution of RNA polymerases. Physical analysis of the models indicates that the effective persistence length of the DNA and relative contributions of twist and writhe to the chromosome's negative supercoiling are in good correspondence with experimental estimates. The models exhibit characteristics similar to those of ‘fractal globules,’ and even the most genomically-distant parts of the chromosome can be physically connected, through paths combining linear diffusion and inter-segmental transfer, by an average of only ∼10 000 bp. Finally, macrodomain structures and the spatial distributions of co-expressed genes are analyzed: the latter are shown to depend strongly on the overall orientation of the chromosome. We anticipate that the models will prove useful in exploring other static and dynamic features of the bacterial chromosome.
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Affiliation(s)
- William C Hacker
- Department of Biochemistry, University of Iowa, Iowa City, IA 52242, USA
| | - Shuxiang Li
- Department of Biochemistry, University of Iowa, Iowa City, IA 52242, USA
| | - Adrian H Elcock
- Department of Biochemistry, University of Iowa, Iowa City, IA 52242, USA
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41
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Schmalhorst PS, Deluweit F, Scherrers R, Heisenberg CP, Sikora M. Overcoming the Limitations of the MARTINI Force Field in Simulations of Polysaccharides. J Chem Theory Comput 2017; 13:5039-5053. [DOI: 10.1021/acs.jctc.7b00374] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
| | - Felix Deluweit
- Wyatt Technology Europe, Hochstraße
18, 56307 Dernbach, Germany
| | - Roger Scherrers
- Wyatt Technology Europe, Hochstraße
18, 56307 Dernbach, Germany
| | | | - Mateusz Sikora
- Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria
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42
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Nakano SI, Sugimoto N. Model studies of the effects of intracellular crowding on nucleic acid interactions. MOLECULAR BIOSYSTEMS 2017; 13:32-41. [PMID: 27819369 DOI: 10.1039/c6mb00654j] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Molecular interactions and reactions in living cells occur with high concentrations of background molecules and ions. Many research studies have shown that intracellular molecules have characteristics different from those obtained using simple aqueous solutions. To better understand the behavior of biomolecules in intracellular environments, biophysical experiments were conducted under cell-mimicking conditions in a test tube. It has been shown that the molecular environments at the physiological level of macromolecular crowding, spatial confinement, water activity and dielectric constant, have significant effects on the interactions of DNA and RNA for hybridization, higher-order folding, and catalytic activity. The experimental approaches using in vitro model systems are useful to reveal the origin of the environmental effects and to bridge the gap between the behaviors of nucleic acids in vitro and in vivo. This paper highlights the model experiments used to evaluate the influences of intracellular environment on nucleic acid interactions.
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Affiliation(s)
- Shu-Ichi Nakano
- Department of Nanobiochemistry, Faculty of Frontiers of Innovative Research in Science and Technology (FIRST), Konan University, 7-1-20, Minatojima-minamimachi, Chuo-ku, Kobe, 650-0047, Japan.
| | - Naoki Sugimoto
- Frontier Institute for Biomolecular Engineering Research (FIBER), Konan University, 7-1-20, Minatojima-minamimachi, Chuo-ku, Kobe, 650-0047, Japan. and Graduate School of Frontiers of Innovative Research in Science and Technology (FIRST), Konan University, 7-1-20, Minatojima-minamimachi, Chuo-ku, Kobe, 650-0047, Japan
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43
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Kobayashi C, Jung J, Matsunaga Y, Mori T, Ando T, Tamura K, Kamiya M, Sugita Y. GENESIS 1.1: A hybrid-parallel molecular dynamics simulator with enhanced sampling algorithms on multiple computational platforms. J Comput Chem 2017; 38:2193-2206. [PMID: 28718930 DOI: 10.1002/jcc.24874] [Citation(s) in RCA: 111] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Revised: 06/08/2017] [Accepted: 06/09/2017] [Indexed: 01/09/2023]
Abstract
GENeralized-Ensemble SImulation System (GENESIS) is a software package for molecular dynamics (MD) simulation of biological systems. It is designed to extend limitations in system size and accessible time scale by adopting highly parallelized schemes and enhanced conformational sampling algorithms. In this new version, GENESIS 1.1, new functions and advanced algorithms have been added. The all-atom and coarse-grained potential energy functions used in AMBER and GROMACS packages now become available in addition to CHARMM energy functions. The performance of MD simulations has been greatly improved by further optimization, multiple time-step integration, and hybrid (CPU + GPU) computing. The string method and replica-exchange umbrella sampling with flexible collective variable choice are used for finding the minimum free-energy pathway and obtaining free-energy profiles for conformational changes of a macromolecule. These new features increase the usefulness and power of GENESIS for modeling and simulation in biological research. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Chigusa Kobayashi
- Computational Biophysics Research Team, RIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima-minamachi, Chuo-ku, Kobe, 650-0047, Japan
| | - Jaewoon Jung
- Computational Biophysics Research Team, RIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima-minamachi, Chuo-ku, Kobe, 650-0047, Japan.,Theoretical Molecular Science Laboratory, RIKEN, 2-1, Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Yasuhiro Matsunaga
- Computational Biophysics Research Team, RIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima-minamachi, Chuo-ku, Kobe, 650-0047, Japan.,JST PRESTO, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan
| | - Takaharu Mori
- Theoretical Molecular Science Laboratory, RIKEN, 2-1, Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Tadashi Ando
- Laboratory for Biomolecular Function Simulation, RIKEN Quantitative Biology Center Computational Biology Research Core, 1-6-5 Minatojima-minamachi, Chuo-ku, Kobe, 650-0047, Japan.,Department of Applied Electronics, Faculty of Industrial Science and Technology, Tokyo University of Science, 6-3-1 Niijuku, Katsushika-ku, Tokyo, 125-8585, Japan.,Water Frontier Science and Technology Research Center, Research Institute for Science and Technology, Tokyo University of Science, 6-3-1 Niijuku, Katsushika-ku, Tokyo, 125-8585, Japan.,Research Division of Multiscale Interfacial Thermofluid Dynamics, Research Institute for Science and Technology, Tokyo University of Science, 6-3-1 Niijuku, Katsushika-ku, Tokyo, 125-8585, Japan
| | - Koichi Tamura
- Computational Biophysics Research Team, RIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima-minamachi, Chuo-ku, Kobe, 650-0047, Japan
| | - Motoshi Kamiya
- Computational Biophysics Research Team, RIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima-minamachi, Chuo-ku, Kobe, 650-0047, Japan
| | - Yuji Sugita
- Computational Biophysics Research Team, RIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima-minamachi, Chuo-ku, Kobe, 650-0047, Japan.,Theoretical Molecular Science Laboratory, RIKEN, 2-1, Hirosawa, Wako, Saitama, 351-0198, Japan.,Laboratory for Biomolecular Function Simulation, RIKEN Quantitative Biology Center Computational Biology Research Core, 1-6-5 Minatojima-minamachi, Chuo-ku, Kobe, 650-0047, Japan
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44
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Feig M, Yu I, Wang PH, Nawrocki G, Sugita Y. Crowding in Cellular Environments at an Atomistic Level from Computer Simulations. J Phys Chem B 2017; 121:8009-8025. [PMID: 28666087 PMCID: PMC5582368 DOI: 10.1021/acs.jpcb.7b03570] [Citation(s) in RCA: 115] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
![]()
The
effects of crowding in biological environments on biomolecular
structure, dynamics, and function remain not well understood. Computer
simulations of atomistic models of concentrated peptide and protein
systems at different levels of complexity are beginning to provide
new insights. Crowding, weak interactions with other macromolecules
and metabolites, and altered solvent properties within cellular environments
appear to remodel the energy landscape of peptides and proteins in
significant ways including the possibility of native state destabilization.
Crowding is also seen to affect dynamic properties, both conformational
dynamics and diffusional properties of macromolecules. Recent simulations
that address these questions are reviewed here and discussed in the
context of relevant experiments.
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Affiliation(s)
- Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University , East Lansing, Michigan, United States.,Quantitative Biology Center, RIKEN , Kobe, Japan
| | - Isseki Yu
- Theoretical Molecular Science Laboratory, RIKEN , Wako, Japan.,iTHES Research Group, RIKEN , Wako, Japan
| | - Po-Hung Wang
- Theoretical Molecular Science Laboratory, RIKEN , Wako, Japan
| | - Grzegorz Nawrocki
- Department of Biochemistry and Molecular Biology, Michigan State University , East Lansing, Michigan, United States
| | - Yuji Sugita
- Quantitative Biology Center, RIKEN , Kobe, Japan.,Theoretical Molecular Science Laboratory, RIKEN , Wako, Japan.,iTHES Research Group, RIKEN , Wako, Japan.,Advanced Institute for Computational Science, RIKEN , Kobe, Japan
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45
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Ando T, Yu I, Feig M, Sugita Y. Thermodynamics of Macromolecular Association in Heterogeneous Crowding Environments: Theoretical and Simulation Studies with a Simplified Model. J Phys Chem B 2016; 120:11856-11865. [PMID: 27797534 DOI: 10.1021/acs.jpcb.6b06243] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The cytoplasm of a cell is crowded with many different kinds of macromolecules. The macromolecular crowding affects the thermodynamics and kinetics of biological reactions in a living cell, such as protein folding, association, and diffusion. Theoretical and simulation studies using simplified models focus on the essential features of the crowding effects and provide a basis for analyzing experimental data. In most of the previous studies on the crowding effects, a uniform crowder size is assumed, which is in contrast to the inhomogeneous size distribution of macromolecules in a living cell. Here, we evaluate the free energy changes upon macromolecular association in a cell-like inhomogeneous crowding system via a theory of hard-sphere fluids and free energy calculations using Brownian dynamics trajectories. The inhomogeneous crowding model based on 41 different types of macromolecules represented by spheres with different radii mimics the physiological concentrations of macromolecules in the cytoplasm of Mycoplasma genitalium. The free energy changes of macromolecular association evaluated by the theory and simulations were in good agreement with each other. The crowder size distribution affects both specific and nonspecific molecular associations, suggesting that not only the volume fraction but also the size distribution of macromolecules are important factors for evaluating in vivo crowding effects. This study relates in vitro experiments on macromolecular crowding to in vivo crowding effects by using the theory of hard-sphere fluids with crowder-size heterogeneity.
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Affiliation(s)
- Tadashi Ando
- RIKEN Quantitative Biology Center (QBiC), Integrated Innovation Building 7F, 6-7-1 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Isseki Yu
- RIKEN Theoretical Molecular Science Laboratory and iTHES, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
| | - Michael Feig
- RIKEN Quantitative Biology Center (QBiC), Integrated Innovation Building 7F, 6-7-1 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.,Department of Biochemistry & Molecular Biology, Michigan State University , East Lansing, Michigan 48824, United States
| | - Yuji Sugita
- RIKEN Quantitative Biology Center (QBiC), Integrated Innovation Building 7F, 6-7-1 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.,RIKEN Theoretical Molecular Science Laboratory and iTHES, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan.,RIKEN Advanced Institute for Computational Science (AICS), 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
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46
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Tegnér J, Zenil H, Kiani NA, Ball G, Gomez-Cabrero D. A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2016; 374:rsta.2016.0144. [PMID: 27698038 PMCID: PMC5052728 DOI: 10.1098/rsta.2016.0144] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/15/2016] [Indexed: 05/06/2023]
Abstract
Systems in nature capable of collective behaviour are nonlinear, operating across several scales. Yet our ability to account for their collective dynamics differs in physics, chemistry and biology. Here, we briefly review the similarities and differences between mathematical modelling of adaptive living systems versus physico-chemical systems. We find that physics-based chemistry modelling and computational neuroscience have a shared interest in developing techniques for model reductions aiming at the identification of a reduced subsystem or slow manifold, capturing the effective dynamics. By contrast, as relations and kinetics between biological molecules are less characterized, current quantitative analysis under the umbrella of bioinformatics focuses on signal extraction, correlation, regression and machine-learning analysis. We argue that model reduction analysis and the ensuing identification of manifolds bridges physics and biology. Furthermore, modelling living systems presents deep challenges as how to reconcile rich molecular data with inherent modelling uncertainties (formalism, variables selection and model parameters). We anticipate a new generative data-driven modelling paradigm constrained by identified governing principles extracted from low-dimensional manifold analysis. The rise of a new generation of models will ultimately connect biology to quantitative mechanistic descriptions, thereby setting the stage for investigating the character of the model language and principles driving living systems.This article is part of the themed issue 'Multiscale modelling at the physics-chemistry-biology interface'.
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Affiliation(s)
- Jesper Tegnér
- Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden Center for Molecular Medicine, Karolinska Institutet, L8:05, 171 76 Stockholm, Sweden Department of Medicine, Unit of Clinical Epidemiology, Karolinska University Hospital L8, 17176 Stockholm, Sweden Science for Life Laboratory, Stockholm, Sweden
| | - Hector Zenil
- Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden Center for Molecular Medicine, Karolinska Institutet, L8:05, 171 76 Stockholm, Sweden Department of Medicine, Unit of Clinical Epidemiology, Karolinska University Hospital L8, 17176 Stockholm, Sweden Science for Life Laboratory, Stockholm, Sweden
| | - Narsis A Kiani
- Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden Center for Molecular Medicine, Karolinska Institutet, L8:05, 171 76 Stockholm, Sweden Department of Medicine, Unit of Clinical Epidemiology, Karolinska University Hospital L8, 17176 Stockholm, Sweden Science for Life Laboratory, Stockholm, Sweden
| | - Gordon Ball
- Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden Center for Molecular Medicine, Karolinska Institutet, L8:05, 171 76 Stockholm, Sweden Department of Medicine, Unit of Clinical Epidemiology, Karolinska University Hospital L8, 17176 Stockholm, Sweden Science for Life Laboratory, Stockholm, Sweden
| | - David Gomez-Cabrero
- Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden Center for Molecular Medicine, Karolinska Institutet, L8:05, 171 76 Stockholm, Sweden Department of Medicine, Unit of Clinical Epidemiology, Karolinska University Hospital L8, 17176 Stockholm, Sweden Science for Life Laboratory, Stockholm, Sweden Mucosal and Salivary Biology Division, King's College London Dental Institute, London SE1 9RT, UK
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47
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Yu I, Mori T, Ando T, Harada R, Jung J, Sugita Y, Feig M. Biomolecular interactions modulate macromolecular structure and dynamics in atomistic model of a bacterial cytoplasm. eLife 2016; 5. [PMID: 27801646 PMCID: PMC5089862 DOI: 10.7554/elife.19274] [Citation(s) in RCA: 203] [Impact Index Per Article: 25.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2016] [Accepted: 09/28/2016] [Indexed: 12/24/2022] Open
Abstract
Biological macromolecules function in highly crowded cellular environments. The structure and dynamics of proteins and nucleic acids are well characterized in vitro, but in vivo crowding effects remain unclear. Using molecular dynamics simulations of a comprehensive atomistic model cytoplasm we found that protein-protein interactions may destabilize native protein structures, whereas metabolite interactions may induce more compact states due to electrostatic screening. Protein-protein interactions also resulted in significant variations in reduced macromolecular diffusion under crowded conditions, while metabolites exhibited significant two-dimensional surface diffusion and altered protein-ligand binding that may reduce the effective concentration of metabolites and ligands in vivo. Metabolic enzymes showed weak non-specific association in cellular environments attributed to solvation and entropic effects. These effects are expected to have broad implications for the in vivo functioning of biomolecules. This work is a first step towards physically realistic in silico whole-cell models that connect molecular with cellular biology.
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Affiliation(s)
- Isseki Yu
- iTHES Research Group, RIKEN, Saitama, Japan.,Theoretical Molecular Science Laboratory, RIKEN, Saitama, Japan
| | - Takaharu Mori
- iTHES Research Group, RIKEN, Saitama, Japan.,Theoretical Molecular Science Laboratory, RIKEN, Saitama, Japan
| | - Tadashi Ando
- Laboratory for Biomolecular Function Simulation, RIKEN Quantitative Biology Center, Kobe, Japan
| | - Ryuhei Harada
- Computational Biophysics Research Team, RIKEN Advanced Institute for Computational Science, Kobe, Japan
| | - Jaewoon Jung
- Computational Biophysics Research Team, RIKEN Advanced Institute for Computational Science, Kobe, Japan
| | - Yuji Sugita
- iTHES Research Group, RIKEN, Saitama, Japan.,Theoretical Molecular Science Laboratory, RIKEN, Saitama, Japan.,Laboratory for Biomolecular Function Simulation, RIKEN Quantitative Biology Center, Kobe, Japan.,Computational Biophysics Research Team, RIKEN Advanced Institute for Computational Science, Kobe, Japan
| | - Michael Feig
- Laboratory for Biomolecular Function Simulation, RIKEN Quantitative Biology Center, Kobe, Japan.,Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, United States
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48
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Qin S, Zhou HX. Protein folding, binding, and droplet formation in cell-like conditions. Curr Opin Struct Biol 2016; 43:28-37. [PMID: 27771543 DOI: 10.1016/j.sbi.2016.10.006] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Accepted: 10/07/2016] [Indexed: 10/20/2022]
Abstract
The many bystander macromolecules in the crowded cellular environments present both steric repulsion and weak attraction to proteins undergoing folding or binding and hence impact the thermodynamic and kinetic properties of these processes. The weak but nonrandom binding with bystander macromolecules may facilitate subcellular localization and biological function. Weak binding also leads to the emergence of a protein-rich droplet phase, which has been implicated in regulating a variety of cellular functions. All these important problems can now be addressed by realistic modeling of intermolecular interactions. Configurational sampling of concentrated protein solutions is an ongoing challenge.
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Affiliation(s)
- Sanbo Qin
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306, USA
| | - Huan-Xiang Zhou
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306, USA.
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49
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Jung J, Sugita Y. Multiple program/multiple data molecular dynamics method with multiple time step integrator for large biological systems. J Comput Chem 2016; 38:1410-1418. [PMID: 27709646 DOI: 10.1002/jcc.24511] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Revised: 09/19/2016] [Accepted: 09/20/2016] [Indexed: 12/17/2022]
Abstract
Parallelization of molecular dynamics (MD) simulation is essential for investigating conformational dynamics of large biological systems, such as ribosomes, viruses, and multiple proteins in cellular environments. To improve efficiency in the parallel computation, we have to reduce the amount of data transfer between processors by introducing domain decomposition schemes. Also, it is important to optimize the computational balance between real-space non-bonded interactions and reciprocal-space interactions for long-range electrostatic interactions. Here, we introduce a novel parallelization scheme for large-scale MD simulations on massively parallel supercomputers consisting of only CPUs. We make use of a multiple program/multiple data (MPMD) approach for separating the real-space and reciprocal-space computations on different processors. We also utilize the r-RESPA multiple time step integrator on the framework of the MPMD approach in an efficient way: when the reciprocal-space computations are skipped in r-RESPA, processors assigned for them are utilized for half of the real-space computations. The new scheme allows us to use twice as many as processors that are available in the conventional single program approach. The best performances of all-atom MD simulations for 1 million (STMV), 8.5 million (8_STMV), and 28.8 million (27_STMV) atom systems on K computer are 65, 36, and 24 ns/day, respectively. The MPMD scheme can accelerate 23.4, 10.2, and 9.2 ns/day from the maximum performance of single-program approach for STMV, 8_STMV, and 27_STMV systems, respectively, which correspond to 57%, 39%, and 60% speed up. This suggests significant speedups by increasing the number of processors without losing parallel computational efficiency. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Jaewoon Jung
- Computational Biophysics Research Team, RIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo Kobe, 640-0047, Japan.,RIKEN Theoretical Molecular Science Laboratory, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan
| | - Yuji Sugita
- Computational Biophysics Research Team, RIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo Kobe, 640-0047, Japan.,RIKEN Theoretical Molecular Science Laboratory, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan.,Laboratory for Biomolecular Function Simulation, RIKEN Quantitative Biology Center (QBiC), IIB 7F, 6-7-1 Minatojimaminami-machi, Chuo-ku, Kobe, Hyogo, 650-0047, Japan.,RIKEN iTHES, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan
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50
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Roy SS, Kapoor M. In silico identification and computational analysis of the nucleotide binding site in the C-terminal domain of Hsp90. J Mol Graph Model 2016; 70:253-274. [PMID: 27771574 DOI: 10.1016/j.jmgm.2016.10.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 10/02/2016] [Indexed: 12/28/2022]
Abstract
Hsp90 contains two distinct Nucleotide Binding Sites (NBS), in its N-terminal domain (NTD) and C-terminal domain (CTD), respectively. The NTD site belongs to the GHKL super-family of ATPases and has been the subject of extensive characterization. However, a structure of the nucleotide-bound form of CTD is still unavailable. In this study molecular modeling was employed to incorporate experimental data using partial constructs of the CTD, from work published by many research groups, onto existing structural models of its apo- form. Our attempts to locate potential nucleotide ligand-binding sites or cavities yielded one major candidate-a structurally unconventional site-exhibiting the requisite shape and volume for accommodation of tri-phosphate nucleotides. Its structure was refined by molecular dynamics (MD)-based techniques. We reproducibly docked the Mg2+ complexed form of ATP, GTP, CTP, TTP and UTP to this putative NBS. These docking simulations and calculated ligand-binding scores are in general agreement with published data about experimentally measured binding to the CTD. The overall pattern of interactions between residues lining the site and docked nucleotides is conserved and broadly similar to that of other nucleotide-binding sites. Our docking simulations suggest that nucleotide binding stabilizes the only structurally labile region, thereby providing a rationale for the increased resistance to thermal denaturation and proteolysis. The docked nucleotides do not intrude onto the surface of residues involved in dimerization or chaperoning. Our molecular modeling permitted recognition of larger structural changes in the nucleotide-bound CTD dimer, including stabilization of helix-2 in both chains and intra- and inter- chain interactions between three residues (I613, Q617, R620).
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Affiliation(s)
- Samir S Roy
- Department of Biological Sciences, The University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
| | - Manju Kapoor
- Department of Biological Sciences, The University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada.
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