1
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Jung J, Yagi K, Tan C, Oshima H, Mori T, Yu I, Matsunaga Y, Kobayashi C, Ito S, Ugarte La Torre D, Sugita Y. GENESIS 2.1: High-Performance Molecular Dynamics Software for Enhanced Sampling and Free-Energy Calculations for Atomistic, Coarse-Grained, and Quantum Mechanics/Molecular Mechanics Models. J Phys Chem B 2024; 128:6028-6048. [PMID: 38876465 PMCID: PMC11215777 DOI: 10.1021/acs.jpcb.4c02096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 05/15/2024] [Accepted: 05/21/2024] [Indexed: 06/16/2024]
Abstract
GENeralized-Ensemble SImulation System (GENESIS) is a molecular dynamics (MD) software developed to simulate the conformational dynamics of a single biomolecule, as well as molecular interactions in large biomolecular assemblies and between multiple biomolecules in cellular environments. To achieve the latter purpose, the earlier versions of GENESIS emphasized high performance in atomistic MD simulations on massively parallel supercomputers, with or without graphics processing units (GPUs). Here, we implemented multiscale MD simulations that include atomistic, coarse-grained, and hybrid quantum mechanics/molecular mechanics (QM/MM) calculations. They demonstrate high performance and are integrated with enhanced conformational sampling algorithms and free-energy calculations without using external programs except for the QM programs. In this article, we review new functions, molecular models, and other essential features in GENESIS version 2.1 and discuss ongoing developments for future releases.
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Affiliation(s)
- Jaewoon Jung
- Computational
Biophysics Research Team, RIKEN Center for
Computational Science, Kobe, Hyogo 650-0047, Japan
- Theoretical
Molecular Science Laboratory, RIKEN Cluster
for Pioneering Research, Wako, Saitama 351-0198, Japan
| | - Kiyoshi Yagi
- Theoretical
Molecular Science Laboratory, RIKEN Cluster
for Pioneering Research, Wako, Saitama 351-0198, Japan
| | - Cheng Tan
- Computational
Biophysics Research Team, RIKEN Center for
Computational Science, Kobe, Hyogo 650-0047, Japan
| | - Hiraku Oshima
- Laboratory
for Biomolecular Function Simulation, RIKEN
Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047, Japan
- Graduate
School of Life Science, University of Hyogo, Harima Science Park City, Hyogo 678-1297, Japan
| | - Takaharu Mori
- Theoretical
Molecular Science Laboratory, RIKEN Cluster
for Pioneering Research, Wako, Saitama 351-0198, Japan
- Department
of Chemistry, Tokyo University of Science, Shinjuku-ku, Tokyo 162-8601, Japan
| | - Isseki Yu
- Theoretical
Molecular Science Laboratory, RIKEN Cluster
for Pioneering Research, Wako, Saitama 351-0198, Japan
- Department
of Bioinformatics, Maebashi Institute of
Technology, Maebashi, Gunma 371-0816, Japan
| | - Yasuhiro Matsunaga
- Computational
Biophysics Research Team, RIKEN Center for
Computational Science, Kobe, Hyogo 650-0047, Japan
- Graduate
School of Science and Engineering, Saitama
University, Saitama 338-8570, Japan
| | - Chigusa Kobayashi
- Computational
Biophysics Research Team, RIKEN Center for
Computational Science, Kobe, Hyogo 650-0047, Japan
| | - Shingo Ito
- Theoretical
Molecular Science Laboratory, RIKEN Cluster
for Pioneering Research, Wako, Saitama 351-0198, Japan
| | - Diego Ugarte La Torre
- Computational
Biophysics Research Team, RIKEN Center for
Computational Science, Kobe, Hyogo 650-0047, Japan
| | - Yuji Sugita
- Computational
Biophysics Research Team, RIKEN Center for
Computational Science, Kobe, Hyogo 650-0047, Japan
- Theoretical
Molecular Science Laboratory, RIKEN Cluster
for Pioneering Research, Wako, Saitama 351-0198, Japan
- Laboratory
for Biomolecular Function Simulation, RIKEN
Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047, Japan
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2
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Wu C, Duan Y, Yu L, Hu Y, Zhao C, Ji C, Guo X, Zhang S, Dai X, Ma P, Wang Q, Ling S, Yang X, Dai Q. In-situ observation of silk nanofibril assembly via graphene plasmonic infrared sensor. Nat Commun 2024; 15:4643. [PMID: 38821959 PMCID: PMC11143229 DOI: 10.1038/s41467-024-49076-5] [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: 10/13/2023] [Accepted: 05/20/2024] [Indexed: 06/02/2024] Open
Abstract
Silk nanofibrils (SNFs), the fundamental building blocks of silk fibers, endow them with exceptional properties. However, the intricate mechanism governing SNF assembly, a process involving both protein conformational transitions and protein molecule conjunctions, remains elusive. This lack of understanding has hindered the development of artificial silk spinning techniques. In this study, we address this challenge by employing a graphene plasmonic infrared sensor in conjunction with multi-scale molecular dynamics (MD). This unique approach allows us to probe the secondary structure of nanoscale assembly intermediates (0.8-6.2 nm) and their morphological evolution. It also provides insights into the dynamics of silk fibroin (SF) over extended molecular timeframes. Our novel findings reveal that amorphous SFs undergo a conformational transition towards β-sheet-rich oligomers on graphene. These oligomers then connect to evolve into SNFs. These insights provide a comprehensive picture of SNF assembly, paving the way for advancements in biomimetic silk spinning.
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Affiliation(s)
- Chenchen Wu
- CAS Key Laboratory of Nanophotonic Materials and Devices, National Center for Nanoscience and Technology, Beijing, 100190, China
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, National Center for Nanoscience and Technology, Beijing, 100190, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yu Duan
- CAS Key Laboratory of Nanophotonic Materials and Devices, National Center for Nanoscience and Technology, Beijing, 100190, China
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, National Center for Nanoscience and Technology, Beijing, 100190, China
- Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou, 450001, China
| | - Lintao Yu
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, 201210, China
- Shanghai Clinical Research and Trial Center, Shanghai, 201210, China
| | - Yao Hu
- Department of Physics, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Chenxi Zhao
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, 201210, China
- Shanghai Clinical Research and Trial Center, Shanghai, 201210, China
| | - Chunwang Ji
- CAS Key Laboratory of Nanophotonic Materials and Devices, National Center for Nanoscience and Technology, Beijing, 100190, China
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, National Center for Nanoscience and Technology, Beijing, 100190, China
| | - Xiangdong Guo
- CAS Key Laboratory of Nanophotonic Materials and Devices, National Center for Nanoscience and Technology, Beijing, 100190, China
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, National Center for Nanoscience and Technology, Beijing, 100190, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
- School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Shu Zhang
- CAS Key Laboratory of Nanophotonic Materials and Devices, National Center for Nanoscience and Technology, Beijing, 100190, China
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, National Center for Nanoscience and Technology, Beijing, 100190, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaokang Dai
- CAS Key Laboratory of Nanophotonic Materials and Devices, National Center for Nanoscience and Technology, Beijing, 100190, China
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, National Center for Nanoscience and Technology, Beijing, 100190, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Puyi Ma
- CAS Key Laboratory of Nanophotonic Materials and Devices, National Center for Nanoscience and Technology, Beijing, 100190, China
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, National Center for Nanoscience and Technology, Beijing, 100190, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qian Wang
- Department of Physics, University of Science and Technology of China, Hefei, Anhui, 230026, China.
| | - Shengjie Ling
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
- Shanghai Clinical Research and Trial Center, Shanghai, 201210, China.
| | - Xiaoxia Yang
- CAS Key Laboratory of Nanophotonic Materials and Devices, National Center for Nanoscience and Technology, Beijing, 100190, China.
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, National Center for Nanoscience and Technology, Beijing, 100190, China.
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Qing Dai
- CAS Key Laboratory of Nanophotonic Materials and Devices, National Center for Nanoscience and Technology, Beijing, 100190, China.
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, National Center for Nanoscience and Technology, Beijing, 100190, China.
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China.
- School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
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3
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Jaafari H, Bueno C, Schafer NP, Martin J, Morcos F, Wolynes PG. The physical and evolutionary energy landscapes of devolved protein sequences corresponding to pseudogenes. Proc Natl Acad Sci U S A 2024; 121:e2322428121. [PMID: 38739795 PMCID: PMC11127006 DOI: 10.1073/pnas.2322428121] [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: 12/20/2023] [Accepted: 03/26/2024] [Indexed: 05/16/2024] Open
Abstract
Protein evolution is guided by structural, functional, and dynamical constraints ensuring organismal viability. Pseudogenes are genomic sequences identified in many eukaryotes that lack translational activity due to sequence degradation and thus over time have undergone "devolution." Previously pseudogenized genes sometimes regain their protein-coding function, suggesting they may still encode robust folding energy landscapes despite multiple mutations. We study both the physical folding landscapes of protein sequences corresponding to human pseudogenes using the Associative Memory, Water Mediated, Structure and Energy Model, and the evolutionary energy landscapes obtained using direct coupling analysis (DCA) on their parent protein families. We found that generally mutations that have occurred in pseudogene sequences have disrupted their native global network of stabilizing residue interactions, making it harder for them to fold if they were translated. In some cases, however, energetic frustration has apparently decreased when the functional constraints were removed. We analyzed this unexpected situation for Cyclophilin A, Profilin-1, and Small Ubiquitin-like Modifier 2 Protein. Our analysis reveals that when such mutations in the pseudogene ultimately stabilize folding, at the same time, they likely alter the pseudogenes' former biological activity, as estimated by DCA. We localize most of these stabilizing mutations generally to normally frustrated regions required for binding to other partners.
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Affiliation(s)
- Hana Jaafari
- Center for Theoretical Biophysics, Rice University, Houston, TX77005
- Applied Physics Graduate Program, Smalley-Curl Institute, Rice University, Houston, TX77005
- Department of Chemistry, Rice University, Houston, TX77005
| | - Carlos Bueno
- Center for Theoretical Biophysics, Rice University, Houston, TX77005
| | | | - Jonathan Martin
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX75080
| | - Faruck Morcos
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX75080
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX75080
- Center for Systems Biology, University of Texas at Dallas, Richardson, TX75080
| | - Peter G. Wolynes
- Center for Theoretical Biophysics, Rice University, Houston, TX77005
- Department of Chemistry, Rice University, Houston, TX77005
- Department of Physics and Astronomy, Rice University, Houston, TX77005
- Department of Biochemistry and Cell Biology, Rice University, Houston, TX77005
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4
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Jung J, Tan C, Sugita Y. GENESIS CGDYN: large-scale coarse-grained MD simulation with dynamic load balancing for heterogeneous biomolecular systems. Nat Commun 2024; 15:3370. [PMID: 38643169 PMCID: PMC11032353 DOI: 10.1038/s41467-024-47654-1] [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: 09/07/2023] [Accepted: 04/08/2024] [Indexed: 04/22/2024] Open
Abstract
Residue-level coarse-grained (CG) molecular dynamics (MD) simulation is widely used to investigate slow biological processes that involve multiple proteins, nucleic acids, and their complexes. Biomolecules in a large simulation system are distributed non-uniformly, limiting computational efficiency with conventional methods. Here, we develop a hierarchical domain decomposition scheme with dynamic load balancing for heterogeneous biomolecular systems to keep computational efficiency even after drastic changes in particle distribution. These schemes are applied to the dynamics of intrinsically disordered protein (IDP) droplets. During the fusion of two droplets, we find that the changes in droplet shape correlate with the mixing of IDP chains. Additionally, we simulate large systems with multiple IDP droplets, achieving simulation sizes comparable to those observed in microscopy. In our MD simulations, we directly observe Ostwald ripening, a phenomenon where small droplets dissolve and their molecules redeposit into larger droplets. These methods have been implemented in CGDYN of the GENESIS software, offering a tool for investigating mesoscopic biological processes using the residue-level CG models.
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Affiliation(s)
- Jaewoon Jung
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe, Hyogo, 650-0047, Japan
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama, 351-0198, Japan
| | - Cheng Tan
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe, Hyogo, 650-0047, Japan
| | - Yuji Sugita
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe, Hyogo, 650-0047, Japan.
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama, 351-0198, Japan.
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo, 650-0047, Japan.
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5
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Jones MS, Shmilovich K, Ferguson AL. DiAMoNDBack: Diffusion-Denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Traces. J Chem Theory Comput 2023; 19:7908-7923. [PMID: 37906711 DOI: 10.1021/acs.jctc.3c00840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Coarse-grained molecular models of proteins permit access to length and time scales unattainable by all-atom models and the simulation of processes that occur on long time scales, such as aggregation and folding. The reduced resolution realizes computational accelerations, but an atomistic representation can be vital for a complete understanding of mechanistic details. Backmapping is the process of restoring all-atom resolution to coarse-grained molecular models. In this work, we report DiAMoNDBack (Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping) as an autoregressive denoising diffusion probability model to restore all-atom details to coarse-grained protein representations retaining only Cα coordinates. The autoregressive generation process proceeds from the protein N-terminus to C-terminus in a residue-by-residue fashion conditioned on the Cα trace and previously backmapped backbone and side-chain atoms within the local neighborhood. The local and autoregressive nature of our model makes it transferable between proteins. The stochastic nature of the denoising diffusion process means that the model generates a realistic ensemble of backbone and side-chain all-atom configurations consistent with the coarse-grained Cα trace. We train DiAMoNDBack over 65k+ structures from the Protein Data Bank (PDB) and validate it in applications to a hold-out PDB test set, intrinsically disordered protein structures from the Protein Ensemble Database (PED), molecular dynamics simulations of fast-folding mini-proteins from DE Shaw Research, and coarse-grained simulation data. We achieve state-of-the-art reconstruction performance in terms of correct bond formation, avoidance of side-chain clashes, and the diversity of the generated side-chain configurational states. We make the DiAMoNDBack model publicly available as a free and open-source Python package.
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Affiliation(s)
- Michael S Jones
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Kirill Shmilovich
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
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6
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Borges-Araújo L, Patmanidis I, Singh AP, Santos LHS, Sieradzan AK, Vanni S, Czaplewski C, Pantano S, Shinoda W, Monticelli L, Liwo A, Marrink SJ, Souza PCT. Pragmatic Coarse-Graining of Proteins: Models and Applications. J Chem Theory Comput 2023; 19:7112-7135. [PMID: 37788237 DOI: 10.1021/acs.jctc.3c00733] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
The molecular details involved in the folding, dynamics, organization, and interaction of proteins with other molecules are often difficult to assess by experimental techniques. Consequently, computational models play an ever-increasing role in the field. However, biological processes involving large-scale protein assemblies or long time scale dynamics are still computationally expensive to study in atomistic detail. For these applications, employing coarse-grained (CG) modeling approaches has become a key strategy. In this Review, we provide an overview of what we call pragmatic CG protein models, which are strategies combining, at least in part, a physics-based implementation and a top-down experimental approach to their parametrization. In particular, we focus on CG models in which most protein residues are represented by at least two beads, allowing these models to retain some degree of chemical specificity. A description of the main modern pragmatic protein CG models is provided, including a review of the most recent applications and an outlook on future perspectives in the field.
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Affiliation(s)
- Luís Borges-Araújo
- Molecular Microbiology and Structural Biochemistry (MMSB, UMR 5086), CNRS, University of Lyon, 7 Passage du Vercors, 69007 Lyon, France
| | - Ilias Patmanidis
- Department of Chemistry, Aarhus University, Langelandsgade 140, 8000 Aarhus C, Denmark
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands
| | - Akhil P Singh
- Department of Biology, University of Fribourg, Chemin du Musée 10, Fribourg CH-1700, Switzerland
| | - Lucianna H S Santos
- Biomolecular Simulations Group, Institut Pasteur de Montevideo, Montevideo 11400, Uruguay
| | - Adam K Sieradzan
- Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Stefano Vanni
- Department of Biology, University of Fribourg, Chemin du Musée 10, Fribourg CH-1700, Switzerland
- Institut de Pharmacologie Moléculaire et Cellulaire, Université Côte d'Azur, Inserm, CNRS, 06560 Valbonne, France
| | - Cezary Czaplewski
- Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Sergio Pantano
- Biomolecular Simulations Group, Institut Pasteur de Montevideo, Montevideo 11400, Uruguay
| | - Wataru Shinoda
- Research Institute for Interdisciplinary Science, Okayama University, 3-1-1 Tsushima-naka, Kita, Okayama 700-8530, Japan
| | - Luca Monticelli
- Molecular Microbiology and Structural Biochemistry (MMSB, UMR 5086), CNRS, University of Lyon, 7 Passage du Vercors, 69007 Lyon, France
| | - Adam Liwo
- Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Siewert J Marrink
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands
| | - Paulo C T Souza
- Molecular Microbiology and Structural Biochemistry (MMSB, UMR 5086), CNRS, University of Lyon, 7 Passage du Vercors, 69007 Lyon, France
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7
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Liu S, Wang C, Latham AP, Ding X, Zhang B. OpenABC enables flexible, simplified, and efficient GPU accelerated simulations of biomolecular condensates. PLoS Comput Biol 2023; 19:e1011442. [PMID: 37695778 PMCID: PMC10513381 DOI: 10.1371/journal.pcbi.1011442] [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] [Received: 04/21/2023] [Revised: 09/21/2023] [Accepted: 08/19/2023] [Indexed: 09/13/2023] Open
Abstract
Biomolecular condensates are important structures in various cellular processes but are challenging to study using traditional experimental techniques. In silico simulations with residue-level coarse-grained models strike a balance between computational efficiency and chemical accuracy. They could offer valuable insights by connecting the emergent properties of these complex systems with molecular sequences. However, existing coarse-grained models often lack easy-to-follow tutorials and are implemented in software that is not optimal for condensate simulations. To address these issues, we introduce OpenABC, a software package that greatly simplifies the setup and execution of coarse-grained condensate simulations with multiple force fields using Python scripting. OpenABC seamlessly integrates with the OpenMM molecular dynamics engine, enabling efficient simulations with performance on a single GPU that rivals the speed achieved by hundreds of CPUs. We also provide tools that convert coarse-grained configurations to all-atom structures for atomistic simulations. We anticipate that OpenABC will significantly facilitate the adoption of in silico simulations by a broader community to investigate the structural and dynamical properties of condensates.
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Affiliation(s)
- Shuming Liu
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Cong Wang
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Andrew P. Latham
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, California, United States of America
| | - Xinqiang Ding
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Bin Zhang
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
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8
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Wellawatte GP, Hocky GM, White AD. Neural potentials of proteins extrapolate beyond training data. J Chem Phys 2023; 159:085103. [PMID: 37642255 PMCID: PMC10474891 DOI: 10.1063/5.0147240] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 07/31/2023] [Indexed: 08/31/2023] Open
Abstract
We evaluate neural network (NN) coarse-grained (CG) force fields compared to traditional CG molecular mechanics force fields. We conclude that NN force fields are able to extrapolate and sample from unseen regions of the free energy surface when trained with limited data. Our results come from 88 NN force fields trained on different combinations of clustered free energy surfaces from four protein mapped trajectories. We used a statistical measure named total variation similarity to assess the agreement between reference free energy surfaces from mapped atomistic simulations and CG simulations from trained NN force fields. Our conclusions support the hypothesis that NN CG force fields trained with samples from one region of the proteins' free energy surface can, indeed, extrapolate to unseen regions. Additionally, the force matching error was found to only be weakly correlated with a force field's ability to reconstruct the correct free energy surface.
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Affiliation(s)
- Geemi P. Wellawatte
- Department of Chemistry, University of Rochester, Rochester, New York 14627, USA
| | - Glen M. Hocky
- Department of Chemistry, Simons Center for Computational Physical Chemistry, New York University, New York, New York 10003, USA
| | - Andrew D. White
- Department of Chemical Engineering, University of Rochester, Rochester, New York 14627, USA
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9
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Sedinkin SL, Burns D, Shukla D, Potoyan DA, Venditti V. Solution Structure Ensembles of the Open and Closed Forms of the ∼130 kDa Enzyme I via AlphaFold Modeling, Coarse Grained Simulations, and NMR. J Am Chem Soc 2023; 145:13347-13356. [PMID: 37278728 PMCID: PMC10772991 DOI: 10.1021/jacs.3c03425] [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] [Indexed: 06/07/2023]
Abstract
Large-scale interdomain rearrangements are essential to protein function, governing the activity of large enzymes and molecular machineries. Yet, obtaining an atomic-resolution understanding of how the relative domain positioning is affected by external stimuli is a hard task in modern structural biology. Here, we show that combining structural modeling by AlphaFold2 with coarse-grained molecular dynamics simulations and NMR residual dipolar coupling data is sufficient to characterize the spatial domain organization of bacterial enzyme I (EI), a ∼130 kDa multidomain oligomeric protein that undergoes large-scale conformational changes during its catalytic cycle. In particular, we solve conformational ensembles for EI at two different experimental temperatures and demonstrate that a lower temperature favors sampling of the catalytically competent closed state of the enzyme. These results suggest a role for conformational entropy in the activation of EI and demonstrate the ability of our protocol to detect and characterize the effect of external stimuli (such as mutations, ligand binding, and post-translational modifications) on the interdomain organization of multidomain proteins. We expect the ensemble refinement protocol described here to be easily transferrable to the investigation of the structure and dynamics of other uncharted multidomain systems and have assembled a Google Colab page (https://potoyangroup.github.io/Seq2Ensemble/) to facilitate implementation of the presented methodology elsewhere.
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Affiliation(s)
| | - Daniel Burns
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa 50011, USA
| | - Divyanshu Shukla
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, USA
| | - Davit A. Potoyan
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, USA
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa 50011, USA
| | - Vincenzo Venditti
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, USA
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa 50011, USA
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10
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Liu S, Wang C, Latham A, Ding X, Zhang B. OpenABC Enables Flexible, Simplified, and Efficient GPU Accelerated Simulations of Biomolecular Condensates. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.19.537533. [PMID: 37131742 PMCID: PMC10153273 DOI: 10.1101/2023.04.19.537533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Biomolecular condensates are important structures in various cellular processes but are challenging to study using traditional experimental techniques. In silico simulations with residue-level coarse-grained models strike a balance between computational efficiency and chemical accuracy. They could offer valuable insights by connecting the emergent properties of these complex systems with molecular sequences. However, existing coarse-grained models often lack easy-to-follow tutorials and are implemented in software that is not optimal for condensate simulations. To address these issues, we introduce OpenABC, a software package that greatly simplifies the setup and execution of coarse-grained condensate simulations with multiple force fields using Python scripting. OpenABC seamlessly integrates with the OpenMM molecular dynamics engine, enabling efficient simulations with performances on a single GPU that rival the speed achieved by hundreds of CPUs. We also provide tools that convert coarse-grained configurations to all-atom structures for atomistic simulations. We anticipate that Open-ABC will significantly facilitate the adoption of in silico simulations by a broader community to investigate the structural and dynamical properties of condensates. Open-ABC is available at https://github.com/ZhangGroup-MITChemistry/OpenABC.
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Affiliation(s)
- Shuming Liu
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Cong Wang
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Andrew Latham
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Xinqiang Ding
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Bin Zhang
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
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11
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Sinha S, Pindi C, Ahsan M, Arantes PR, Palermo G. Machines on Genes through the Computational Microscope. J Chem Theory Comput 2023; 19:1945-1964. [PMID: 36947696 PMCID: PMC10104023 DOI: 10.1021/acs.jctc.2c01313] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
Macromolecular machines acting on genes are at the core of life's fundamental processes, including DNA replication and repair, gene transcription and regulation, chromatin packaging, RNA splicing, and genome editing. Here, we report the increasing role of computational biophysics in characterizing the mechanisms of "machines on genes", focusing on innovative applications of computational methods and their integration with structural and biophysical experiments. We showcase how state-of-the-art computational methods, including classical and ab initio molecular dynamics to enhanced sampling techniques, and coarse-grained approaches are used for understanding and exploring gene machines for real-world applications. As this review unfolds, advanced computational methods describe the biophysical function that is unseen through experimental techniques, accomplishing the power of the "computational microscope", an expression coined by Klaus Schulten to highlight the extraordinary capability of computer simulations. Pushing the frontiers of computational biophysics toward a pragmatic representation of large multimegadalton biomolecular complexes is instrumental in bridging the gap between experimentally obtained macroscopic observables and the molecular principles playing at the microscopic level. This understanding will help harness molecular machines for medical, pharmaceutical, and biotechnological purposes.
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12
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Sieradzan AK, Sans-Duñó J, Lubecka EA, Czaplewski C, Lipska AG, Leszczyński H, Ocetkiewicz KM, Proficz J, Czarnul P, Krawczyk H, Liwo A. Optimization of parallel implementation of UNRES package for coarse-grained simulations to treat large proteins. J Comput Chem 2023; 44:602-625. [PMID: 36378078 DOI: 10.1002/jcc.27026] [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: 06/14/2022] [Revised: 09/19/2022] [Accepted: 10/17/2022] [Indexed: 11/16/2022]
Abstract
We report major algorithmic improvements of the UNRES package for physics-based coarse-grained simulations of proteins. These include (i) introduction of interaction lists to optimize computations, (ii) transforming the inertia matrix to a pentadiagonal form to reduce computing and memory requirements, (iii) removing explicit angles and dihedral angles from energy expressions and recoding the most time-consuming energy/force terms to minimize the number of operations and to improve numerical stability, (iv) using OpenMP to parallelize those sections of the code for which distributed-memory parallelization involves unfavorable computing/communication time ratio, and (v) careful memory management to minimize simultaneous access of distant memory sections. The new code enables us to run molecular dynamics simulations of protein systems with size exceeding 100,000 amino-acid residues, reaching over 1 ns/day (1 μs/day in all-atom timescale) with 24 cores for proteins of this size. Parallel performance of the code and comparison of its performance with that of AMBER, GROMACS and MARTINI 3 is presented.
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Affiliation(s)
- Adam K Sieradzan
- Faculty of Chemistry, University of Gdańsk, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland.,Centre of Informatics Tri-city Academic Supercomputer and Network (CI TASK), Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Jordi Sans-Duñó
- Department of Chemistry, University of Lleida, Lleida, Spain
| | - Emilia A Lubecka
- Faculty of Electronics, Telecommunication and Informatics, Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Cezary Czaplewski
- Faculty of Chemistry, University of Gdańsk, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland.,Centre of Informatics Tri-city Academic Supercomputer and Network (CI TASK), Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Agnieszka G Lipska
- Faculty of Chemistry, University of Gdańsk, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland.,Centre of Informatics Tri-city Academic Supercomputer and Network (CI TASK), Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Henryk Leszczyński
- Faculty of Mathematics, Physics and Informatics, University of Gdańsk, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Krzysztof M Ocetkiewicz
- Centre of Informatics Tri-city Academic Supercomputer and Network (CI TASK), Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Jerzy Proficz
- Centre of Informatics Tri-city Academic Supercomputer and Network (CI TASK), Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Paweł Czarnul
- Faculty of Electronics, Telecommunication and Informatics, Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Henryk Krawczyk
- Centre of Informatics Tri-city Academic Supercomputer and Network (CI TASK), Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland.,Faculty of Electronics, Telecommunication and Informatics, Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Adam Liwo
- Faculty of Chemistry, University of Gdańsk, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland.,Centre of Informatics Tri-city Academic Supercomputer and Network (CI TASK), Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
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13
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Ślusarz R, Lubecka EA, Czaplewski C, Liwo A. Improvements and new functionalities of UNRES server for coarse-grained modeling of protein structure, dynamics, and interactions. Front Mol Biosci 2022; 9:1071428. [PMID: 36589235 PMCID: PMC9794589 DOI: 10.3389/fmolb.2022.1071428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 11/29/2022] [Indexed: 12/15/2022] Open
Abstract
In this paper we report the improvements and extensions of the UNRES server (https://unres-server.chem.ug.edu.pl) for physics-based simulations with the coarse-grained UNRES model of polypeptide chains. The improvements include the replacement of the old code with the recently optimized one and adding the recent scale-consistent variant of the UNRES force field, which performs better in the modeling of proteins with the β and the α+β structures. The scope of applications of the package was extended to data-assisted simulations with restraints from nuclear magnetic resonance (NMR) and chemical crosslink mass-spectroscopy (XL-MS) measurements. NMR restraints can be input in the NMR Exchange Format (NEF), which has become a standard. Ambiguous NMR restraints are handled without expert intervention owing to a specially designed penalty function. The server can be used to run smaller jobs directly or to prepare input data to run larger production jobs by using standalone installations of UNRES.
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Affiliation(s)
- Rafał Ślusarz
- Faculty of Chemistry, University of Gdańsk, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Emilia A. Lubecka
- Faculty of Electronics, Telecommunication and Informatics, Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Cezary Czaplewski
- Faculty of Chemistry, University of Gdańsk, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Adam Liwo
- Faculty of Chemistry, University of Gdańsk, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland,*Correspondence: Adam Liwo,
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14
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Brandani GB, Gopi S, Yamauchi M, Takada S. Molecular dynamics simulations for the study of chromatin biology. Curr Opin Struct Biol 2022; 77:102485. [PMID: 36274422 DOI: 10.1016/j.sbi.2022.102485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 09/05/2022] [Accepted: 09/18/2022] [Indexed: 12/14/2022]
Abstract
The organization of Eukaryotic DNA into chromatin has profound implications for the processing of genetic information. In the past years, molecular dynamics (MD) simulations proved to be a powerful tool to investigate the mechanistic basis of chromatin biology. We review recent all-atom and coarse-grained MD studies revealing how the structure and dynamics of chromatin underlie its biological functions. We describe the latest method developments; the structural fluctuations of nucleosomes and the various factors affecting them; the organization of chromatin fibers, with particular emphasis on its liquid-like character; the interactions and dynamics of transcription factors on chromatin; and how chromatin organization is modulated by molecular motors acting on DNA.
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Affiliation(s)
- Giovanni B Brandani
- Department of Biophysics, Graduate School of Science, Kyoto University, Japan.
| | - Soundhararajan Gopi
- Department of Biophysics, Graduate School of Science, Kyoto University, Japan
| | - Masataka Yamauchi
- Department of Biophysics, Graduate School of Science, Kyoto University, Japan
| | - Shoji Takada
- Department of Biophysics, Graduate School of Science, Kyoto University, Japan
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15
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Murata Y, Niina T, Takada S. The stoichiometric interaction model for mesoscopic MD simulations of liquid-liquid phase separation. Biophys J 2022; 121:4382-4393. [PMID: 36199253 PMCID: PMC9703007 DOI: 10.1016/j.bpj.2022.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/28/2022] [Accepted: 09/30/2022] [Indexed: 12/14/2022] Open
Abstract
Liquid-liquid phase separation (LLPS) has received considerable attention in recent years for explaining the formation of cellular biomolecular condensates. The fluidity and the complexity of their components make molecular simulation approaches indispensable for gaining structural insights. Domain-resolution mesoscopic model simulations have been explored for cases in which condensates are formed by multivalent proteins with tandem domains. One problem with this approach is that interdomain pairwise interactions cannot regulate the valency of the binding domains. To overcome this problem, we propose a new potential, the stoichiometric interaction (SI) potential. First, we verified that the SI potential maintained the valency of the interacting domains for the test systems. We then examined a well-studied LLPS model system containing tandem repeats of SH3 domains and proline-rich motifs. We found that the SI potential alone cannot reproduce the phase diagram of LLPS quantitatively. We had to combine the SI and a pairwise interaction; the former and the latter represent the specific and nonspecific interactions, respectively. Biomolecular condensates with the mixed SI and pairwise interaction exhibited fluidity, whereas those with the pairwise interaction alone showed no detectable diffusion. We also compared the phase diagrams of the systems containing different numbers of tandem domains with those obtained from the experiments and found quantitative agreement in all but one case.
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Affiliation(s)
- Yutaka Murata
- Department of Biophysics, Graduate School of Science, Kyoto University, Kyoto, Japan
| | - Toru Niina
- Department of Biophysics, Graduate School of Science, Kyoto University, Kyoto, Japan
| | - Shoji Takada
- Department of Biophysics, Graduate School of Science, Kyoto University, Kyoto, Japan.
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16
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A structural dynamics model for how CPEB3 binding to SUMO2 can regulate translational control in dendritic spines. PLoS Comput Biol 2022; 18:e1010657. [DOI: 10.1371/journal.pcbi.1010657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 11/18/2022] [Accepted: 10/14/2022] [Indexed: 11/09/2022] Open
Abstract
A prion-like RNA-binding protein, CPEB3, can regulate local translation in dendritic spines. CPEB3 monomers repress translation, whereas CPEB3 aggregates activate translation of its target mRNAs. However, the CPEB3 aggregates, as long-lasting prions, may raise the problem of unregulated translational activation. Here, we propose a computational model of the complex structure between CPEB3 RNA-binding domain (CPEB3-RBD) and small ubiquitin-like modifier protein 2 (SUMO2). Free energy calculations suggest that the allosteric effect of CPEB3-RBD/SUMO2 interaction can amplify the RNA-binding affinity of CPEB3. Combining with previous experimental observations on the SUMOylation mode of CPEB3, this model suggests an equilibrium shift of mRNA from binding to deSUMOylated CPEB3 aggregates to binding to SUMOylated CPEB3 monomers in basal synapses. This work shows how a burst of local translation in synapses can be silenced following a stimulation pulse, and explores the CPEB3/SUMO2 interplay underlying the structural change of synapses and the formation of long-term memories.
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17
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Sánchez IE, Galpern EA, Garibaldi MM, Ferreiro DU. Molecular Information Theory Meets Protein Folding. J Phys Chem B 2022; 126:8655-8668. [PMID: 36282961 DOI: 10.1021/acs.jpcb.2c04532] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
We propose an application of molecular information theory to analyze the folding of single domain proteins. We analyze results from various areas of protein science, such as sequence-based potentials, reduced amino acid alphabets, backbone configurational entropy, secondary structure content, residue burial layers, and mutational studies of protein stability changes. We found that the average information contained in the sequences of evolved proteins is very close to the average information needed to specify a fold ∼2.2 ± 0.3 bits/(site·operation). The effective alphabet size in evolved proteins equals the effective number of conformations of a residue in the compact unfolded state at around 5. We calculated an energy-to-information conversion efficiency upon folding of around 50%, lower than the theoretical limit of 70%, but much higher than human-built macroscopic machines. We propose a simple mapping between molecular information theory and energy landscape theory and explore the connections between sequence evolution, configurational entropy, and the energetics of protein folding.
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Affiliation(s)
- Ignacio E Sánchez
- Facultad de Ciencias Exactas y Naturales, Laboratorio de Fisiología de Proteínas, Consejo Nacional de Investigaciones Científicas y Técnicas, Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Universidad de Buenos Aires, Buenos AiresCP1428, Argentina
| | - Ezequiel A Galpern
- Facultad de Ciencias Exactas y Naturales, Laboratorio de Fisiología de Proteínas, Consejo Nacional de Investigaciones Científicas y Técnicas, Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Universidad de Buenos Aires, Buenos AiresCP1428, Argentina
| | - Martín M Garibaldi
- Facultad de Ciencias Exactas y Naturales, Laboratorio de Fisiología de Proteínas, Consejo Nacional de Investigaciones Científicas y Técnicas, Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Universidad de Buenos Aires, Buenos AiresCP1428, Argentina
| | - Diego U Ferreiro
- Facultad de Ciencias Exactas y Naturales, Laboratorio de Fisiología de Proteínas, Consejo Nacional de Investigaciones Científicas y Técnicas, Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Universidad de Buenos Aires, Buenos AiresCP1428, Argentina
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18
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Narayanan RP, Procyk J, Nandi P, Prasad A, Xu Y, Poppleton E, Williams D, Zhang F, Yan H, Chiu PL, Stephanopoulos N, Šulc P. Coarse-Grained Simulations for the Characterization and Optimization of Hybrid Protein-DNA Nanostructures. ACS NANO 2022; 16:14086-14096. [PMID: 35980981 PMCID: PMC9590280 DOI: 10.1021/acsnano.2c04013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
We present here the combination of experimental and computational modeling tools for the design and characterization of protein-DNA hybrid nanostructures. Our work incorporates several features in the design of these nanostructures: (1) modeling of the protein-DNA linker identity and length; (2) optimizing the design of protein-DNA cages to account for mechanical stresses; (3) probing the incorporation efficiency of protein-DNA conjugates into DNA nanostructures. The modeling tools were experimentally validated using structural characterization methods like cryo-TEM and AFM. Our method can be used for fitting low-resolution electron density maps when structural insights cannot be deciphered from experiments, as well as enable in-silico validation of nanostructured systems before their experimental realization. These tools will facilitate the design of complex hybrid protein-DNA nanostructures that seamlessly integrate the two different biomolecules.
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Affiliation(s)
- Raghu Pradeep Narayanan
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
- Center for molecular design and biomimetics, The Biodesign Institute, Arizona State University, Tempe, Arizona 85287, United States
| | - Jonah Procyk
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
- Center for molecular design and biomimetics, The Biodesign Institute, Arizona State University, Tempe, Arizona 85287, United States
| | - Purbasha Nandi
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
- Center for Applied Structural Discovery, The Biodesign Institute, Arizona State University, Tempe, Arizona 85287, United States
| | - Abhay Prasad
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
- Center for molecular design and biomimetics, The Biodesign Institute, Arizona State University, Tempe, Arizona 85287, United States
| | - Yang Xu
- Center for molecular design and biomimetics, The Biodesign Institute, Arizona State University, Tempe, Arizona 85287, United States
| | - Erik Poppleton
- Center for molecular design and biomimetics, The Biodesign Institute, Arizona State University, Tempe, Arizona 85287, United States
| | - Dewight Williams
- Eyring Materials Center, Office of Knowledge Enterprise Development, Arizona State University, Tempe, Arizona 85287, United States
| | - Fei Zhang
- Department of Chemistry, Rutgers University, Newark, New Jersey 07102, United States
| | - Hao Yan
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
- Center for molecular design and biomimetics, The Biodesign Institute, Arizona State University, Tempe, Arizona 85287, United States
| | - Po-Lin Chiu
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
- Center for Applied Structural Discovery, The Biodesign Institute, Arizona State University, Tempe, Arizona 85287, United States
| | - Nicholas Stephanopoulos
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
- Center for molecular design and biomimetics, The Biodesign Institute, Arizona State University, Tempe, Arizona 85287, United States
| | - Petr Šulc
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
- Center for molecular design and biomimetics, The Biodesign Institute, Arizona State University, Tempe, Arizona 85287, United States
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19
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Jin S, Bueno C, Lu W, Wang Q, Chen M, Chen X, Wolynes PG, Gao Y. Computationally exploring the mechanism of bacteriophage T7 gp4 helicase translocating along ssDNA. Proc Natl Acad Sci U S A 2022; 119:e2202239119. [PMID: 35914145 PMCID: PMC9371691 DOI: 10.1073/pnas.2202239119] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 07/05/2022] [Indexed: 12/12/2022] Open
Abstract
Bacteriophage T7 gp4 helicase has served as a model system for understanding mechanisms of hexameric replicative helicase translocation. The mechanistic basis of how nucleoside 5'-triphosphate hydrolysis and translocation of gp4 helicase are coupled is not fully resolved. Here, we used a thermodynamically benchmarked coarse-grained protein force field, Associative memory, Water mediated, Structure and Energy Model (AWSEM), with the single-stranded DNA (ssDNA) force field 3SPN.2C to investigate gp4 translocation. We found that the adenosine 5'-triphosphate (ATP) at the subunit interface stabilizes the subunit-subunit interaction and inhibits subunit translocation. Hydrolysis of ATP to adenosine 5'-diphosphate enables the translocation of one subunit, and new ATP binding at the new subunit interface finalizes the subunit translocation. The LoopD2 and the N-terminal primase domain provide transient protein-protein and protein-DNA interactions that facilitate the large-scale subunit movement. The simulations of gp4 helicase both validate our coarse-grained protein-ssDNA force field and elucidate the molecular basis of replicative helicase translocation.
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Affiliation(s)
- Shikai Jin
- Department of Biosciences, Rice University, Houston, TX 77005
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005
| | - Carlos Bueno
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005
| | - Wei Lu
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005
- Department of Physics, Rice University, Houston, TX 77005
| | - Qian Wang
- Department of Physics, University of Science and Technology of China, Hefei 230026, China
| | - Mingchen Chen
- Department of Research and Development, neoX Biotech, Beijing 100206, China
| | - Xun Chen
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005
- Department of Chemistry, Rice University, Houston, TX 77005
| | - Peter G Wolynes
- Department of Biosciences, Rice University, Houston, TX 77005
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005
- Department of Physics, Rice University, Houston, TX 77005
- Department of Chemistry, Rice University, Houston, TX 77005
| | - Yang Gao
- Department of Biosciences, Rice University, Houston, TX 77005
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20
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Tan C, Jung J, Kobayashi C, Torre DUL, Takada S, Sugita Y. Implementation of residue-level coarse-grained models in GENESIS for large-scale molecular dynamics simulations. PLoS Comput Biol 2022; 18:e1009578. [PMID: 35381009 PMCID: PMC9012402 DOI: 10.1371/journal.pcbi.1009578] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 04/15/2022] [Accepted: 03/26/2022] [Indexed: 12/28/2022] Open
Abstract
Residue-level coarse-grained (CG) models have become one of the most popular tools in biomolecular simulations in the trade-off between modeling accuracy and computational efficiency. To investigate large-scale biological phenomena in molecular dynamics (MD) simulations with CG models, unified treatments of proteins and nucleic acids, as well as efficient parallel computations, are indispensable. In the GENESIS MD software, we implement several residue-level CG models, covering structure-based and context-based potentials for both well-folded biomolecules and intrinsically disordered regions. An amino acid residue in protein is represented as a single CG particle centered at the Cα atom position, while a nucleotide in RNA or DNA is modeled with three beads. Then, a single CG particle represents around ten heavy atoms in both proteins and nucleic acids. The input data in CG MD simulations are treated as GROMACS-style input files generated from a newly developed toolbox, GENESIS-CG-tool. To optimize the performance in CG MD simulations, we utilize multiple neighbor lists, each of which is attached to a different nonbonded interaction potential in the cell-linked list method. We found that random number generations for Gaussian distributions in the Langevin thermostat are one of the bottlenecks in CG MD simulations. Therefore, we parallelize the computations with message-passing-interface (MPI) to improve the performance on PC clusters or supercomputers. We simulate Herpes simplex virus (HSV) type 2 B-capsid and chromatin models containing more than 1,000 nucleosomes in GENESIS as examples of large-scale biomolecular simulations with residue-level CG models. This framework extends accessible spatial and temporal scales by multi-scale simulations to study biologically relevant phenomena, such as genome-scale chromatin folding or phase-separated membrane-less condensations.
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Affiliation(s)
- Cheng Tan
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe, Hyogo, Japan
| | - Jaewoon Jung
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe, Hyogo, Japan
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama, Japan
| | - Chigusa Kobayashi
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe, Hyogo, Japan
| | - Diego Ugarte La Torre
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe, Hyogo, Japan
| | - Shoji Takada
- Department of Biophysics, Graduate School of Science, Kyoto University, Kyoto, Japan
| | - Yuji Sugita
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe, Hyogo, Japan
- 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
- * E-mail:
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21
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Exploring the folding energy landscapes of heme proteins using a hybrid AWSEM-heme model. J Biol Phys 2022; 48:37-53. [PMID: 35000062 PMCID: PMC8866609 DOI: 10.1007/s10867-021-09596-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 11/03/2021] [Indexed: 10/29/2022] Open
Abstract
Heme is an active center in many proteins. Here we explore computationally the role of heme in protein folding and protein structure. We model heme proteins using a hybrid model employing the AWSEM Hamiltonian, a coarse-grained forcefield for the protein chain along with AMBER, an all-atom forcefield for the heme. We carefully designed transferable force fields that model the interactions between the protein and the heme. The types of protein-ligand interactions in the hybrid model include thioester covalent bonds, coordinated covalent bonds, hydrogen bonds, and electrostatics. We explore the influence of different types of hemes (heme b and heme c) on folding and structure prediction. Including both types of heme improves the quality of protein structure predictions. The free energy landscape shows that both types of heme can act as nucleation sites for protein folding and stabilize the protein folded state. In binding the heme, coordinated covalent bonds and thioester covalent bonds for heme c drive the heme toward the native pocket. The electrostatics also facilitates the search for the binding site.
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22
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Chen W, Lu W, Wolynes PG, Komives E. Single-molecule conformational dynamics of a transcription factor reveals a continuum of binding modes controlling association and dissociation. Nucleic Acids Res 2021; 49:11211-11223. [PMID: 34614173 PMCID: PMC8565325 DOI: 10.1093/nar/gkab874] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 09/10/2021] [Accepted: 09/22/2021] [Indexed: 12/24/2022] Open
Abstract
Binding and unbinding of transcription factors to DNA are kinetically controlled to regulate the transcriptional outcome. Control of the release of the transcription factor NF-κB from DNA is achieved through accelerated dissociation by the inhibitor protein IκBα. Using single-molecule FRET, we observed a continuum of conformations of NF-κB in free and DNA-bound states interconverting on the subseconds to minutes timescale, comparable to in vivo binding on the seconds timescale, suggesting that structural dynamics directly control binding kinetics. Much of the DNA-bound NF-κB is partially bound, allowing IκBα invasion to facilitate DNA dissociation. IκBα induces a locked conformation where the DNA-binding domains of NF-κB are too far apart to bind DNA, whereas a loss-of-function IκBα mutant retains the NF-κB conformational ensemble. Overall, our results suggest a novel mechanism with a continuum of binding modes for controlling association and dissociation of transcription factors.
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Affiliation(s)
- Wei Chen
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Wei Lu
- Center for Theoretical Biological Physics, Departments of Chemistry, Physics, and Biosciences, Rice University, Houston, Texas 77005, USA
| | - Peter G Wolynes
- Center for Theoretical Biological Physics, Departments of Chemistry, Physics, and Biosciences, Rice University, Houston, Texas 77005, USA
| | - Elizabeth A Komives
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
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Jones M, Ashwood B, Tokmakoff A, Ferguson AL. Determining Sequence-Dependent DNA Oligonucleotide Hybridization and Dehybridization Mechanisms Using Coarse-Grained Molecular Simulation, Markov State Models, and Infrared Spectroscopy. J Am Chem Soc 2021; 143:17395-17411. [PMID: 34644072 PMCID: PMC8554761 DOI: 10.1021/jacs.1c05219] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Indexed: 11/29/2022]
Abstract
A robust understanding of the sequence-dependent thermodynamics of DNA hybridization has enabled rapid advances in DNA nanotechnology. A fundamental understanding of the sequence-dependent kinetics and mechanisms of hybridization and dehybridization remains comparatively underdeveloped. In this work, we establish new understanding of the sequence-dependent hybridization/dehybridization kinetics and mechanism within a family of self-complementary pairs of 10-mer DNA oligomers by integrating coarse-grained molecular simulation, machine learning of the slow dynamical modes, data-driven inference of long-time kinetic models, and experimental temperature-jump infrared spectroscopy. For a repetitive ATATATATAT sequence, we resolve a rugged dynamical landscape comprising multiple metastable states, numerous competing hybridization/dehybridization pathways, and a spectrum of dynamical relaxations. Introduction of a G:C pair at the terminus (GATATATATC) or center (ATATGCATAT) of the sequence reduces the ruggedness of the dynamics landscape by eliminating a number of metastable states and reducing the number of competing dynamical pathways. Only by introducing a G:C pair midway between the terminus and the center to maximally disrupt the repetitive nature of the sequence (ATGATATCAT) do we recover a canonical "all-or-nothing" two-state model of hybridization/dehybridization with no intermediate metastable states. Our results establish new understanding of the dynamical richness of sequence-dependent kinetics and mechanisms of DNA hybridization/dehybridization by furnishing quantitative and predictive kinetic models of the dynamical transition network between metastable states, present a molecular basis with which to understand experimental temperature jump data, and furnish foundational design rules by which to rationally engineer the kinetics and pathways of DNA association and dissociation for DNA nanotechnology applications.
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Affiliation(s)
- Michael
S. Jones
- Pritzker
School of Molecular Engineering, The University
of Chicago, 5640 South Ellis Avenue, Chicago, Illinois 60637, United
States
| | - Brennan Ashwood
- Department
of Chemistry, Institute for Biophysical Dynamics, and James Franck
Institute, The University of Chicago, 929 East 57th Street, Chicago, Illinois 60637, United States
| | - Andrei Tokmakoff
- Department
of Chemistry, Institute for Biophysical Dynamics, and James Franck
Institute, The University of Chicago, 929 East 57th Street, Chicago, Illinois 60637, United States
| | - Andrew L. Ferguson
- Pritzker
School of Molecular Engineering, The University
of Chicago, 5640 South Ellis Avenue, Chicago, Illinois 60637, United
States
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Ugarte La Torre D, Takada S. Modeling lipid-protein interactions for coarse-grained lipid and Cα protein models. J Chem Phys 2021; 155:155101. [PMID: 34686048 DOI: 10.1063/5.0057278] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Biological membranes that play major roles in diverse functions are composed of numerous lipids and proteins, making them an important target for coarse-grained (CG) molecular dynamics (MD) simulations. Recently, we have developed the CG implicit solvent lipid force field (iSoLF) that has a resolution compatible with the widely used Cα protein representation [D. Ugarte La Torre and S. Takada, J. Chem. Phys. 153, 205101 (2020)]. In this study, we extended it and developed a lipid-protein interaction model that allows the combination of the iSoLF and the Cα protein force field, AICG2+. The hydrophobic-hydrophilic interaction is modeled as a modified Lennard-Jones potential in which parameters were tuned partly to reproduce the experimental transfer free energy and partly based on the free energy profile normal to the membrane surface from previous all-atom MD simulations. Then, the obtained lipid-protein interaction is tested for the configuration and placement of transmembrane proteins, water-soluble proteins, and peripheral proteins, showing good agreement with prior knowledge. The interaction is generally applicable and is implemented in the publicly available software, CafeMol.
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Affiliation(s)
- Diego Ugarte La Torre
- Department of Biophysics, Graduate School of Science, Kyoto University, Kyoto, Japan
| | - Shoji Takada
- Department of Biophysics, Graduate School of Science, Kyoto University, Kyoto, Japan
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25
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Ma YW, Lin TY, Tsai MY. Fibril Surface-Dependent Amyloid Precursors Revealed by Coarse-Grained Molecular Dynamics Simulation. Front Mol Biosci 2021; 8:719320. [PMID: 34422910 PMCID: PMC8378332 DOI: 10.3389/fmolb.2021.719320] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 07/26/2021] [Indexed: 01/05/2023] Open
Abstract
Amyloid peptides are known to self-assemble into larger aggregates that are linked to the pathogenesis of many neurodegenerative disorders. In contrast to primary nucleation, recent experimental and theoretical studies have shown that many toxic oligomeric species are generated through secondary processes on a pre-existing fibrillar surface. Nucleation, for example, can also occur along the surface of a pre-existing fibril—secondary nucleation—as opposed to the primary one. However, explicit pathways are still not clear. In this study, we use molecular dynamics simulation to explore the free energy landscape of a free Abeta monomer binding to an existing fibrillar surface. We specifically look into several potential Abeta structural precursors that might precede some secondary events, including elongation and secondary nucleation. We find that the overall process of surface-dependent events can be described at least by the following three stages: 1. Free diffusion 2. Downhill guiding 3. Dock and lock. And we show that the outcome of adding a new monomer onto a pre-existing fibril is pathway-dependent, which leads to different secondary processes. To understand structural details, we have identified several monomeric amyloid precursors over the fibrillar surfaces and characterize their heterogeneity using a probability contact map analysis. Using the frustration analysis (a bioinformatics tool), we show that surface heterogeneity correlates with the energy frustration of specific local residues that form binding sites on the fibrillar structure. We further investigate the helical twisting of protofilaments of different sizes and observe a length dependence on the filament twisting. This work presents a comprehensive survey over the properties of fibril growth using a combination of several openMM-based platforms, including the GPU-enabled openAWSEM package for coarse-grained modeling, MDTraj for trajectory analysis, and pyEMMA for free energy calculation. This combined approach makes long-timescale simulation for aggregation systems as well as all-in-one analysis feasible. We show that this protocol allows us to explore fibril stability, surface binding affinity/heterogeneity, as well as fibrillar twisting. All these properties are important for understanding the molecular mechanism of surface-catalyzed secondary processes of fibril growth.
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Affiliation(s)
- Yuan-Wei Ma
- Department of Chemistry, Tamkang University, New Taipei City, Taiwan
| | - Tong-You Lin
- Department of Chemistry, Tamkang University, New Taipei City, Taiwan
| | - Min-Yeh Tsai
- Department of Chemistry, Tamkang University, New Taipei City, Taiwan
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26
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DNA Nanodevices as Mechanical Probes of Protein Structure and Function. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11062802] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
DNA nanotechnology has reported a wide range of structurally tunable scaffolds with precise control over their size, shape and mechanical properties. One promising application of these nanodevices is as probes for protein function or determination of protein structure. In this perspective we cover several recent examples in this field, including determining the effect of ligand spacing and multivalency on cell activation, applying forces at the nanoscale, and helping to solve protein structure by cryo-EM. We also highlight some future directions in the chemistry necessary for integrating proteins with DNA nanoscaffolds, as well as opportunities for computational modeling of hybrid protein-DNA nanomaterials.
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