1
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Heo L, Feig M. One bead per residue can describe all-atom protein structures. Structure 2024; 32:97-111.e6. [PMID: 38000367 PMCID: PMC10872525 DOI: 10.1016/j.str.2023.10.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/16/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023]
Abstract
Atomistic resolution is the standard for high-resolution biomolecular structures, but experimental structural data are often at lower resolution. Coarse-grained models are also used extensively in computational studies to reach biologically relevant spatial and temporal scales. This study explores the use of advanced machine learning networks for reconstructing atomistic models from reduced representations. The main finding is that a single bead per amino acid residue allows construction of accurate and stereochemically realistic all-atom structures with minimal loss of information. This suggests that lower resolution representations of proteins may be sufficient for many applications when combined with a machine learning framework that encodes knowledge from known structures. Practical applications include the rapid addition of atomistic detail to low-resolution structures from experiment or computational coarse-grained models. The application of rapid, deterministic all-atom reconstruction within multi-scale frameworks is further demonstrated with a rapid protocol for the generation of accurate models from cryo-EM densities close to experimental structures.
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Affiliation(s)
- Lim Heo
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.
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2
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Airas J, Ding X, Zhang B. Transferable Implicit Solvation via Contrastive Learning of Graph Neural Networks. ACS CENTRAL SCIENCE 2023; 9:2286-2297. [PMID: 38161379 PMCID: PMC10755853 DOI: 10.1021/acscentsci.3c01160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/26/2023] [Accepted: 10/31/2023] [Indexed: 01/03/2024]
Abstract
Implicit solvent models are essential for molecular dynamics simulations of biomolecules, striking a balance between computational efficiency and biological realism. Efforts are underway to develop accurate and transferable implicit solvent models and coarse-grained (CG) force fields in general, guided by a bottom-up approach that matches the CG energy function with the potential of mean force (PMF) defined by the finer system. However, practical challenges arise due to the lack of analytical expressions for the PMF and algorithmic limitations in parameterizing CG force fields. To address these challenges, a machine learning-based approach is proposed, utilizing graph neural networks (GNNs) to represent the solvation free energy and potential contrasting for parameter optimization. We demonstrate the effectiveness of the approach by deriving a transferable GNN implicit solvent model using 600,000 atomistic configurations of six proteins obtained from explicit solvent simulations. The GNN model provides solvation free energy estimations much more accurately than state-of-the-art implicit solvent models, reproducing configurational distributions of explicit solvent simulations. We also demonstrate the reasonable transferability of the GNN model outside of the training data. Our study offers valuable insights for deriving systematically improvable implicit solvent models and CG force fields from a bottom-up perspective.
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Affiliation(s)
- Justin Airas
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139-4307, United
States
| | - Xinqiang Ding
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139-4307, United
States
| | - Bin Zhang
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139-4307, United
States
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3
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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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4
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Airas J, Ding X, Zhang B. Transferable Coarse Graining via Contrastive Learning of Graph Neural Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.08.556923. [PMID: 37745447 PMCID: PMC10515757 DOI: 10.1101/2023.09.08.556923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Coarse-grained (CG) force fields are essential for molecular dynamics simulations of biomolecules, striking a balance between computational efficiency and biological realism. These simulations employ simplified models grouping atoms into interaction sites, enabling the study of complex biomolecular systems over biologically relevant timescales. Efforts are underway to develop accurate and transferable CG force fields, guided by a bottom-up approach that matches the CG energy function with the potential of mean force (PMF) defined by the finer system. However, practical challenges arise due to many-body effects, lack of analytical expressions for the PMF, and limitations in parameterizing CG force fields. To address these challenges, a machine learning-based approach is proposed, utilizing graph neural networks (GNNs) to represent CG force fields and potential contrasting for parameterization from atomistic simulation data. We demonstrate the effectiveness of the approach by deriving a transferable GNN implicit solvent model using 600,000 atomistic configurations of six proteins obtained from explicit solvent simulations. The GNN model provides solvation free energy estimations much more accurately than state-of-the-art implicit solvent models, reproducing configurational distributions of explicit solvent simulations. We also demonstrate the reasonable transferability of the GNN model outside the training data. Our study offers valuable insights for building accurate coarse-grained models bottom-up.
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Affiliation(s)
- Justin Airas
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, 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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5
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Fagerberg E, Skepö M. Comparative Performance of Computer Simulation Models of Intrinsically Disordered Proteins at Different Levels of Coarse-Graining. J Chem Inf Model 2023. [PMID: 37339604 DOI: 10.1021/acs.jcim.3c00113] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2023]
Abstract
Coarse-graining is commonly used to decrease the computational cost of simulations. However, coarse-grained models are also considered to have lower transferability, with lower accuracy for systems outside the original scope of parametrization. Here, we benchmark a bead-necklace model and a modified Martini 2 model, both coarse-grained models, for a set of intrinsically disordered proteins, with the different models having different degrees of coarse-graining. The SOP-IDP model has earlier been used for this set of proteins; thus, those results are included in this study to compare how models with different levels of coarse-graining compare. The sometimes naive expectation of the least coarse-grained model performing best does not hold true for the experimental pool of proteins used here. Instead, it showed the least good agreement, indicating that one should not necessarily trust the otherwise intuitive notion of a more advanced model inherently being better in model choice.
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Affiliation(s)
- Eric Fagerberg
- Theoretical Chemistry, Lund University, POB 124, SE-221 00 Lund, Sweden
| | - Marie Skepö
- Theoretical Chemistry, Lund University, POB 124, SE-221 00 Lund, Sweden
- LINXS - Institute of Advanced Neutron and X-ray Science, Scheelevägen 19, SE-223 70 Lund, Sweden
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6
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Valdes-Garcia G, Heo L, Lapidus LJ, Feig M. Modeling Concentration-dependent Phase Separation Processes Involving Peptides and RNA via Residue-Based Coarse-Graining. J Chem Theory Comput 2023; 19:10.1021/acs.jctc.2c00856. [PMID: 36607820 PMCID: PMC10323037 DOI: 10.1021/acs.jctc.2c00856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Biomolecular condensation, especially liquid-liquid phase separation, is an important physical process with relevance for a number of different aspects of biological functions. Key questions of what drives such condensation, especially in terms of molecular composition, can be addressed via computer simulations, but the development of computationally efficient yet physically realistic models has been challenging. Here, the coarse-grained model COCOMO is introduced that balances the polymer behavior of peptides and RNA chains with their propensity to phase separate as a function of composition and concentration. COCOMO is a residue-based model that combines bonded terms with short- and long-range terms, including a Debye-Hückel solvation term. The model is highly predictive of experimental data on phase-separating model systems. It is also computationally efficient and can reach the spatial and temporal scales on which biomolecular condensation is observed with moderate computational resources.
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Affiliation(s)
- Gilberto Valdes-Garcia
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Lim Heo
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Lisa J. Lapidus
- Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824, USA
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
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7
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Abstract
Glycoscience assembles all the scientific disciplines involved in studying various molecules and macromolecules containing carbohydrates and complex glycans. Such an ensemble involves one of the most extensive sets of molecules in quantity and occurrence since they occur in all microorganisms and higher organisms. Once the compositions and sequences of these molecules are established, the determination of their three-dimensional structural and dynamical features is a step toward understanding the molecular basis underlying their properties and functions. The range of the relevant computational methods capable of addressing such issues is anchored by the specificity of stereoelectronic effects from quantum chemistry to mesoscale modeling throughout molecular dynamics and mechanics and coarse-grained and docking calculations. The Review leads the reader through the detailed presentations of the applications of computational modeling. The illustrations cover carbohydrate-carbohydrate interactions, glycolipids, and N- and O-linked glycans, emphasizing their role in SARS-CoV-2. The presentation continues with the structure of polysaccharides in solution and solid-state and lipopolysaccharides in membranes. The full range of protein-carbohydrate interactions is presented, as exemplified by carbohydrate-active enzymes, transporters, lectins, antibodies, and glycosaminoglycan binding proteins. A final section features a list of 150 tools and databases to help address the many issues of structural glycobioinformatics.
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Affiliation(s)
- Serge Perez
- Centre de Recherche sur les Macromolecules Vegetales, University of Grenoble-Alpes, Centre National de la Recherche Scientifique, Grenoble F-38041, France
| | - Olga Makshakova
- FRC Kazan Scientific Center of Russian Academy of Sciences, Kazan Institute of Biochemistry and Biophysics, Kazan 420111, Russia
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8
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González-Fernández C, Bringas E, Oostenbrink C, Ortiz I. In silico investigation and surmounting of Lipopolysaccharide barrier in Gram-Negative Bacteria: How far has molecular dynamics Come? Comput Struct Biotechnol J 2022; 20:5886-5901. [PMID: 36382192 PMCID: PMC9636410 DOI: 10.1016/j.csbj.2022.10.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/24/2022] [Accepted: 10/24/2022] [Indexed: 11/29/2022] Open
Abstract
Lipopolysaccharide (LPS), a main component of the outer membrane of Gram-negative bacteria, has crucial implications on both antibiotic resistance and the overstimulation of the host innate immune system. Fighting against these global concerns calls for the molecular understanding of the barrier function and immunostimulatory ability of LPS. Molecular dynamics (MD) simulations have become an invaluable tool for uncovering important findings in LPS research. While the reach of MD simulations for investigating the immunostimulatory ability of LPS has been already outlined, little attention has been paid to the role of MD simulations for exploring its barrier function and synthesis. Herein, we give an overview about the impact of MD simulations on gaining insight into the shield role and synthesis pathway of LPS, which have attracted considerable attention to discover molecules able to surmount antibiotic resistance, either circumventing LPS defenses or disrupting its synthesis. We specifically focus on the enhanced sampling and free energy calculation methods that have been combined with MD simulations to address such research. We also highlight the use of special-purpose MD supercomputers, the importance of appropriate LPS and ions parameterization to obtain reliable results, and the complementary views that MD and wet-lab experiments provide. Thereby, this work, which covers the last five years of research, apart from outlining the phenomena and strategies that are being explored, evidences the valuable insights that are gained by MD, which may be useful to advance antibiotic design, and what the prospects of this in silico method could be in LPS research.
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Affiliation(s)
- Cristina González-Fernández
- Department of Chemical and Biomolecular Engineering, ETSIIT, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain
| | - Eugenio Bringas
- Department of Chemical and Biomolecular Engineering, ETSIIT, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain
| | - Chris Oostenbrink
- Institute for Molecular Modeling and Simulation, BOKU – University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Inmaculada Ortiz
- Department of Chemical and Biomolecular Engineering, ETSIIT, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain
- Corresponding author.
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9
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González-Fernández C, Basauri A, Fallanza M, Bringas E, Oostenbrink C, Ortiz I. Fighting Against Bacterial Lipopolysaccharide-Caused Infections through Molecular Dynamics Simulations: A Review. J Chem Inf Model 2021; 61:4839-4851. [PMID: 34559524 PMCID: PMC8549069 DOI: 10.1021/acs.jcim.1c00613] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
![]()
Lipopolysaccharide
(LPS) is the primary component of the outer
leaflet of Gram-negative bacterial outer membranes. LPS elicits an
overwhelming immune response during infection, which can lead to life-threatening
sepsis or septic shock for which no suitable treatment is available
so far. As a result of the worldwide expanding multidrug-resistant
bacteria, the occurrence and frequency of sepsis are expected to increase;
thus, there is an urge to develop novel strategies for treating bacterial
infections. In this regard, gaining an in-depth understanding about
the ability of LPS to both stimulate the host immune system and interact
with several molecules is crucial for fighting against LPS-caused
infections and allowing for the rational design of novel antisepsis
drugs, vaccines and LPS sequestration and detection methods. Molecular
dynamics (MD) simulations, which are understood as being a computational
microscope, have proven to be of significant value to understand LPS-related
phenomena, driving and optimizing experimental research studies. In
this work, a comprehensive review on the methods that can be combined
with MD simulations, recently applied in LPS research, is provided.
We focus especially on both enhanced sampling methods, which enable
the exploration of more complex systems and access to larger time
scales, and free energy calculation approaches. Thereby, apart from
outlining several strategies for surmounting LPS-caused infections,
this work reports the current state-of-the-art of the methods applied
with MD simulations for moving a step forward in the development of
such strategies.
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Affiliation(s)
- Cristina González-Fernández
- Department of Chemical and Biomolecular Engineering, ETSIIT, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain
| | - Arantza Basauri
- Department of Chemical and Biomolecular Engineering, ETSIIT, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain
| | - Marcos Fallanza
- Department of Chemical and Biomolecular Engineering, ETSIIT, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain
| | - Eugenio Bringas
- Department of Chemical and Biomolecular Engineering, ETSIIT, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain
| | - Chris Oostenbrink
- Institute for Molecular Modeling and Simulation, BOKU - University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Inmaculada Ortiz
- Department of Chemical and Biomolecular Engineering, ETSIIT, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain
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10
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Kubincová A, Riniker S, Hünenberger PH. Solvent-scaling as an alternative to coarse-graining in adaptive-resolution simulations: The adaptive solvent-scaling (AdSoS) scheme. J Chem Phys 2021; 155:094107. [PMID: 34496576 DOI: 10.1063/5.0057384] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
A new approach termed Adaptive Solvent-Scaling (AdSoS) is introduced for performing simulations of a solute embedded in a fine-grained (FG) solvent region itself surrounded by a coarse-grained (CG) solvent region, with a continuous FG ↔ CG switching of the solvent resolution across a buffer layer. Instead of relying on a distinct CG solvent model, the AdSoS scheme is based on CG models defined by a dimensional scaling of the FG solvent by a factor s, accompanied by an s-dependent modulation of the atomic masses and interaction parameters. The latter changes are designed to achieve an isomorphism between the dynamics of the FG and CG models, and to preserve the dispersive and dielectric solvation properties of the solvent with respect to a solute at FG resolution. This scaling approach offers a number of advantages compared to traditional coarse-graining: (i) the CG parameters are immediately related to those of the FG model (no need to parameterize a distinct CG model); (ii) nearly ideal mixing is expected for CG variants with similar s-values (ideal mixing holding in the limit of identical s-values); (iii) the solvent relaxation timescales should be preserved (no dynamical acceleration typical for coarse-graining); (iv) the graining level NG (number of FG molecules represented by one CG molecule) can be chosen arbitrarily (in particular, NG = s3 is not necessarily an integer); and (v) in an adaptive-resolution scheme, this level can be varied continuously as a function of the position (without requiring a bundling mechanism), and this variation occurs at a constant number of particles per molecule (no occurrence of fractional degrees of freedom in the buffer layer). By construction, the AdSoS scheme minimizes the thermodynamic mismatch between the different regions of the adaptive-resolution system, leading to a nearly homogeneous scaled solvent density s3ρ. Residual density artifacts in and at the surface of the boundary layer can easily be corrected by means of a grid-based biasing potential constructed in a preliminary pure-solvent simulation. This article introduces the AdSoS scheme and provides an initial application to pure atomic liquids (no solute) with Lennard-Jones plus Coulomb interactions in a slab geometry.
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Affiliation(s)
- Alžbeta Kubincová
- Laboratory of Physical Chemistry, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir Prelog-Weg 2, CH-8093 Zürich, Switzerland
| | - Sereina Riniker
- Laboratory of Physical Chemistry, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir Prelog-Weg 2, CH-8093 Zürich, Switzerland
| | - Philippe H Hünenberger
- Laboratory of Physical Chemistry, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir Prelog-Weg 2, CH-8093 Zürich, Switzerland
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11
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Franco-Ulloa S, Guarnieri D, Riccardi L, Pompa PP, De Vivo M. Association Mechanism of Peptide-Coated Metal Nanoparticles with Model Membranes: A Coarse-Grained Study. J Chem Theory Comput 2021; 17:4512-4523. [PMID: 34077229 PMCID: PMC8280734 DOI: 10.1021/acs.jctc.1c00127] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Indexed: 11/28/2022]
Abstract
Functionalized metal nanoparticles (NPs) hold great promise as innovative tools in nanomedicine. However, one of the main challenges is how to optimize their association with the cell membrane, which is critical for their effective delivery. Recent findings show high cellular uptake rates for NPs coated with the polycationic cell-penetrating peptide gH625-644 (gH), although the underlying internalization mechanism is poorly understood. Here, we use extended coarse-grained simulations and free energy calculations to study systems that simultaneously include metal NPs, peptides, lipids, and sterols. In particular, we investigate the first encounter between multicomponent model membranes and 2.5 nm metal NPs coated with gH (gHNPs), based on the evidence from scanning transmission electron microscopy. By comparing multiple membrane and (membranotropic) NP models, we found that gHNP internalization occurs by forming an intermediate state characterized by specific stabilizing interactions formed by peptide-coated nanoparticles with multicomponent model membranes. This association mechanism is mainly characterized by interactions of gH with the extracellular solvent and the polar membrane surface. At the same time, the NP core interacts with the transmembrane (cholesterol-rich) fatty phase.
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Affiliation(s)
- Sebastian Franco-Ulloa
- Molecular
Modeling and Drug Discovery Lab, Istituto
Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Daniela Guarnieri
- Dipartimento
di Chimica e Biologia “A. Zambelli”, Università degli Studi di Salerno, Via Giovanni Paolo II, 132, Fisciano, l-84084 Salerno, Italy
| | - Laura Riccardi
- Molecular
Modeling and Drug Discovery Lab, Istituto
Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Pier Paolo Pompa
- Nanobiointeractions
& Nanodiagnostics, Istituto Italiano
di Tecnologia, Via Morego
30, 16163 Genova, Italy
| | - Marco De Vivo
- Molecular
Modeling and Drug Discovery Lab, Istituto
Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
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12
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Latham AP, Zhang B. Consistent Force Field Captures Homologue-Resolved HP1 Phase Separation. J Chem Theory Comput 2021; 17:3134-3144. [PMID: 33826337 PMCID: PMC8119372 DOI: 10.1021/acs.jctc.0c01220] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Many proteins have been shown to function via liquid-liquid phase separation. Computational modeling could offer much needed structural details of protein condensates and reveal the set of molecular interactions that dictate their stability. However, the presence of both ordered and disordered domains in these proteins places a high demand on the model accuracy. Here, we present an algorithm to derive a coarse-grained force field, MOFF, which can model both ordered and disordered proteins with consistent accuracy. It combines maximum entropy biasing, least-squares fitting, and basic principles of energy landscape theory to ensure that MOFF recreates experimental radii of gyration while predicting the folded structures for globular proteins with lower energy. The theta temperature determined from MOFF separates ordered and disordered proteins at 300 K and exhibits a strikingly linear relationship with amino acid sequence composition. We further applied MOFF to study the phase behavior of HP1, an essential protein for post-translational modification and spatial organization of chromatin. The force field successfully resolved the structural difference of two HP1 homologues despite their high sequence similarity. We carried out large-scale simulations with hundreds of proteins to determine the critical temperature of phase separation and uncover multivalent interactions that stabilize higher-order assemblies. In all, our work makes significant methodological strides to connect theories of ordered and disordered proteins and provides a powerful tool for studying liquid-liquid phase separation with near-atomistic details.
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Affiliation(s)
- Andrew P Latham
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Bin Zhang
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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13
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Wang B, Su Z, Wu Y. Characterizing the function of domain linkers in regulating the dynamics of multi-domain fusion proteins by microsecond molecular dynamics simulations and artificial intelligence. Proteins 2021; 89:884-895. [PMID: 33620752 DOI: 10.1002/prot.26066] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 01/20/2021] [Accepted: 02/20/2021] [Indexed: 11/12/2022]
Abstract
Multi-domain proteins are not only formed through natural evolution but can also be generated by recombinant DNA technology. Because many fusion proteins can enhance the selectivity of cell targeting, these artificially produced molecules, called multi-specific biologics, are promising drug candidates, especially for immunotherapy. Moreover, the rational design of domain linkers in fusion proteins is becoming an essential step toward a quantitative understanding of the dynamics in these biopharmaceutics. We developed a computational framework to characterize the impacts of peptide linkers on the dynamics of multi-specific biologics. Specifically, we first constructed a benchmark containing six types of linkers that represent various lengths and degrees of flexibility and used them to connect two natural proteins as a test system. We then projected the microsecond dynamics of these proteins generated from Anton onto a coarse-grained conformational space. We further analyzed the similarity of dynamics among different proteins in this low-dimensional space by a neural-network-based classification model. Finally, we applied hierarchical clustering to place linkers into different subgroups based on the classification results. The clustering results suggest that the length of linkers, which is used to spatially separate different functional modules, plays the most important role in regulating the dynamics of this fusion protein. Given the same number of amino acids, linker flexibility functions as a regulator of protein dynamics. In summary, we illustrated that a new computational strategy can be used to study the dynamics of multi-domain fusion proteins by a combination of long timescale molecular dynamics simulation, coarse-grained feature extraction, and artificial intelligence.
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Affiliation(s)
- Bo Wang
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Zhaoqian Su
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, USA
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14
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Liwo A, Czaplewski C, Sieradzan AK, Lubecka EA, Lipska AG, Golon Ł, Karczyńska A, Krupa P, Mozolewska MA, Makowski M, Ganzynkowicz R, Giełdoń A, Maciejczyk M. Scale-consistent approach to the derivation of coarse-grained force fields for simulating structure, dynamics, and thermodynamics of biopolymers. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2020; 170:73-122. [PMID: 32145953 DOI: 10.1016/bs.pmbts.2019.12.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
In this chapter the scale-consistent approach to the derivation of coarse-grained force fields developed in our laboratory is presented, in which the effective energy function originates from the potential of mean force of the system under consideration and embeds atomistically detailed interactions in the resulting energy terms through use of Kubo's cluster-cumulant expansion, appropriate selection of the major degrees of freedom to be averaged out in the derivation of analytical approximations to the energy terms, and appropriate expression of the interaction energies at the all-atom level in these degrees of freedom. Our approach enables the developers to find correct functional forms of the effective coarse-grained energy terms, without having to import them from all-atom force fields or deriving them on a heuristic basis. In particular, the energy terms derived in such a way exhibit correct dependence on coarse-grained geometry, in particular on site orientation. Moreover, analytical formulas for the multibody (correlation) terms, which appear to be crucial for coarse-grained modeling of many of the regular structures such as, e.g., protein α-helices and β-sheets, can be derived in a systematic way. Implementation of the developed theory to the UNIfied COarse-gRaiNed (UNICORN) model of biological macromolecules, which consists of the UNRES (for proteins), NARES-2P (for nucleic acids), and SUGRES-1P (for polysaccharides) components, and is being developed in our laboratory is described. Successful applications of UNICORN to the prediction of protein structure, simulating the folding and stability of proteins and nucleic acids, and solving biological problems are discussed.
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Affiliation(s)
- Adam Liwo
- Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland; School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Republic of Korea.
| | | | - Adam K Sieradzan
- Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland; School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Republic of Korea
| | - Emilia A Lubecka
- Institute of Informatics, Faculty of Mathematics, Physics, and Informatics, University of Gdańsk, Gdańsk, Poland
| | | | - Łukasz Golon
- Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | | | - Paweł Krupa
- Institute of Physics, Polish Academy of Sciences, Warsaw, Poland
| | | | | | | | - Artur Giełdoń
- Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | - Maciej Maciejczyk
- Department of Physics and Biophysics, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland
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15
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Liwo A, Czaplewski C. Extension of the force-matching method to coarse-grained models with axially symmetric sites to produce transferable force fields: Application to the UNRES model of proteins. J Chem Phys 2020; 152:054902. [DOI: 10.1063/1.5138991] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Affiliation(s)
- Adam Liwo
- Faculty of Chemistry, University of Gdańsk, ul. Wita-Stwosza 63, 80-308 Gdańsk, Poland
- School of Computational Sciences, Korea Institute for Advanced Study, 87 Hoegiro, Dongdaemun-gu, 130-722 Seoul, South Korea
| | - Cezary Czaplewski
- Faculty of Chemistry, University of Gdańsk, ul. Wita-Stwosza 63, 80-308 Gdańsk, Poland
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16
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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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17
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Abstract
Brownian dynamics (BD) is a technique for carrying out computer simulations of physical systems that are driven by thermal fluctuations. Biological systems at the macromolecular and cellular level, while falling in the gap between well-established atomic-level models and continuum models, are especially suitable for such simulations. We present a brief history, examples of important biological processes that are driven by thermal motion, and those that have been profitably studied by BD. We also present some of the challenges facing developers of algorithms and software, especially in the attempt to simulate larger systems more accurately and for longer times.
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Affiliation(s)
- Gary A Huber
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093-0340, USA.,Department of Pharmocology, University of California San Diego, La Jolla, CA 92093-0636, USA
| | - J Andrew McCammon
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093-0340, USA.,Department of Pharmocology, University of California San Diego, La Jolla, CA 92093-0636, USA
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18
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Zhang Y, Xia K, Cao Z, Gräter F, Xia F. A new method for the construction of coarse-grained models of large biomolecules from low-resolution cryo-electron microscopy data. Phys Chem Chem Phys 2019; 21:9720-9727. [PMID: 31025999 DOI: 10.1039/c9cp01370a] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
The rapid development of cryo-electron microscopy (cryo-EM) has led to the generation of significant low-resolution electron density data of biomolecules. However, the atomistic details of huge biomolecules usually cannot be obtained because it is very difficult to construct all-atom models for MD simulations. Thus, it is still a challenge to make use of the rich low-resolution cryo-EM data for computer simulation and functional study. In this study, we proposed a new method called Convolutional and K-means Coarse-Graining (CK-CG) for the efficient coarse-graining of large biological systems. Using the CK-CG method, we could directly map the cryo-EM data into coarse-grained (CG) beads. Furthermore, the CG beads were parameterized with an empirical harmonic potential to construct a new CG model. We subjected the CK-CG models of the fibrillar protein assemblies F-actin and collagen to external forces in pulling dynamic simulations to assess their mechanical response. The agreement between the estimated tensile stiffness between CG models and experiments demonstrates the validity of the CK-CG method. Thus, our method provides a practical strategy for the direct construction of a structural model from low-resolution data for biological function studies.
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Affiliation(s)
- Yuwei Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China.
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19
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Petersen NP, Ort T, Torda AE. Improving the Numerical Stability of the NAST Force Field for RNA Simulations. J Chem Theory Comput 2019; 15:3402-3409. [DOI: 10.1021/acs.jctc.9b00089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Nils P. Petersen
- Centre for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Thomas Ort
- Laboratory Automation and Biomanufacturing Engineering, Fraunhofer Institute for Manufacturing Engineering and Automation IPA, Nobelstrasse 12, 70569 Stuttgart, Germany
| | - Andrew E. Torda
- Centre for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
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20
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Ciemny MP, Badaczewska-Dawid AE, Pikuzinska M, Kolinski A, Kmiecik S. Modeling of Disordered Protein Structures Using Monte Carlo Simulations and Knowledge-Based Statistical Force Fields. Int J Mol Sci 2019; 20:E606. [PMID: 30708941 PMCID: PMC6386871 DOI: 10.3390/ijms20030606] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 01/23/2019] [Accepted: 01/29/2019] [Indexed: 12/20/2022] Open
Abstract
The description of protein disordered states is important for understanding protein folding mechanisms and their functions. In this short review, we briefly describe a simulation approach to modeling protein interactions, which involve disordered peptide partners or intrinsically disordered protein regions, and unfolded states of globular proteins. It is based on the CABS coarse-grained protein model that uses a Monte Carlo (MC) sampling scheme and a knowledge-based statistical force field. We review several case studies showing that description of protein disordered states resulting from CABS simulations is consistent with experimental data. The case studies comprise investigations of protein⁻peptide binding and protein folding processes. The CABS model has been recently made available as the simulation engine of multiscale modeling tools enabling studies of protein⁻peptide docking and protein flexibility. Those tools offer customization of the modeling process, driving the conformational search using distance restraints, reconstruction of selected models to all-atom resolution, and simulation of large protein systems in a reasonable computational time. Therefore, CABS can be combined in integrative modeling pipelines incorporating experimental data and other modeling tools of various resolution.
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Affiliation(s)
- Maciej Pawel Ciemny
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland.
- Faculty of Physics, University of Warsaw, Pasteura 5, 02-093 Warsaw, Poland.
| | | | - Monika Pikuzinska
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland.
| | - Andrzej Kolinski
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland.
| | - Sebastian Kmiecik
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland.
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21
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Jain A, Globisch C, Verma S, Peter C. Coarse-Grained Simulations of Peptide Nanoparticle Formation: Role of Local Structure and Nonbonded Interactions. J Chem Theory Comput 2019; 15:1453-1462. [DOI: 10.1021/acs.jctc.8b01138] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Alok Jain
- Department of Biotechnology, National Institute of Pharmaceutical Education and Research, Ahmedabad, Gujarat 380054, India
- Department of Chemistry, University of Konstanz, Konstanz 78464, Germany
| | - Christoph Globisch
- Department of Chemistry, University of Konstanz, Konstanz 78464, Germany
| | - Sandeep Verma
- Department of Chemistry and Center for Nanoscience, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh 208016, India
| | - Christine Peter
- Department of Chemistry, University of Konstanz, Konstanz 78464, Germany
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22
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Lebold KM, Noid WG. Systematic study of temperature and density variations in effective potentials for coarse-grained models of molecular liquids. J Chem Phys 2019; 150:014104. [DOI: 10.1063/1.5050509] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Affiliation(s)
- Kathryn M. Lebold
- Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, USA
| | - W. G. Noid
- Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, USA
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23
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Sorzano COS, Jiménez A, Mota J, Vilas JL, Maluenda D, Martínez M, Ramírez-Aportela E, Majtner T, Segura J, Sánchez-García R, Rancel Y, del Caño L, Conesa P, Melero R, Jonic S, Vargas J, Cazals F, Freyberg Z, Krieger J, Bahar I, Marabini R, Carazo JM. Survey of the analysis of continuous conformational variability of biological macromolecules by electron microscopy. Acta Crystallogr F Struct Biol Commun 2019; 75:19-32. [PMID: 30605122 PMCID: PMC6317454 DOI: 10.1107/s2053230x18015108] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 10/26/2018] [Indexed: 11/10/2022] Open
Abstract
Single-particle analysis by electron microscopy is a well established technique for analyzing the three-dimensional structures of biological macromolecules. Besides its ability to produce high-resolution structures, it also provides insights into the dynamic behavior of the structures by elucidating their conformational variability. Here, the different image-processing methods currently available to study continuous conformational changes are reviewed.
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Affiliation(s)
| | - A. Jiménez
- National Center of Biotechnology (CSIC), Spain
| | - J. Mota
- National Center of Biotechnology (CSIC), Spain
| | - J. L. Vilas
- National Center of Biotechnology (CSIC), Spain
| | - D. Maluenda
- National Center of Biotechnology (CSIC), Spain
| | - M. Martínez
- National Center of Biotechnology (CSIC), Spain
| | | | - T. Majtner
- National Center of Biotechnology (CSIC), Spain
| | - J. Segura
- National Center of Biotechnology (CSIC), Spain
| | | | - Y. Rancel
- National Center of Biotechnology (CSIC), Spain
| | - L. del Caño
- National Center of Biotechnology (CSIC), Spain
| | - P. Conesa
- National Center of Biotechnology (CSIC), Spain
| | - R. Melero
- National Center of Biotechnology (CSIC), Spain
| | - S. Jonic
- Sorbonne Université, UMR CNRS 7590, Muséum National d’Histoire Naturelle, IRD, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, Paris, France
| | | | - F. Cazals
- Inria Sophia Antipolis – Méditerranée, France
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24
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Coarse-grained dynamics of supramolecules: Conformational changes in outer shells of Dengue viruses. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2018; 143:20-37. [PMID: 30273615 DOI: 10.1016/j.pbiomolbio.2018.09.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Revised: 09/22/2018] [Accepted: 09/24/2018] [Indexed: 01/12/2023]
Abstract
While structural data on viruses are more and more common, information on their dynamics is much harder to obtain as those viruses form very large molecular complexes. In this paper, we propose a new method for computing the coarse-grained normal modes of such supra-molecules, NormalGo. A new formalism is developed to represent the Hessian of a quadratic potential using tensor products. This formalism is applied to the Tirion elastic potential, as well as to a Gō like potential. When combined with a fast method for computing a select set of eigenpairs of the Hessian, this new formalism enables the computation of thousands of normal modes of a full viral shell with more than one hundred thousand atoms in less than 2 h on a standard desktop computer. We then compare the two coarse-grained potentials. We show that, despite significant differences in their formulations, the Tirion and the Gō like potentials capture very similar dynamics characteristics of the molecule under study. However, we find that the Gō like potential should be preferred as it leads to less local deformations in the structure of the molecule during normal mode dynamics. Finally, we use NormalGo to characterize the structural transitions that occur when FAB fragments bind to the icosahedral outer shell of serotype 3 of the Dengue virus. We have identified residues at the surface of the outer shell that are important for the transition between the FAB-free and FAB-bound conformations, and therefore potentially useful for the design of antibodies to Dengue viruses.
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25
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Dawid AE, Gront D, Kolinski A. SURPASS Low-Resolution Coarse-Grained Protein Modeling. J Chem Theory Comput 2017; 13:5766-5779. [PMID: 28992694 DOI: 10.1021/acs.jctc.7b00642] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Coarse-grained modeling of biomolecules has a very important role in molecular biology. In this work we present a novel SURPASS (Single United Residue per Pre-Averaged Secondary Structure fragment) model of proteins that can be an interesting alternative for existing coarse-grained models. The design of the model is unique and strongly supported by the statistical analysis of structural regularities characteristic for protein systems. Coarse-graining of protein chain structures assumes a single center of interactions per residue and accounts for preaveraged effects of four adjacent residue fragments. Knowledge-based statistical potentials encode complex interaction patterns of these fragments. Using the Replica Exchange Monte Carlo sampling scheme and a generic version of the SURPASS force field we performed test simulations of a representative set of single-domain globular proteins. The method samples a significant part of conformational space and reproduces protein structures, including native-like, with surprisingly good accuracy. Future extension of the SURPASS model on large biomacromolecular systems is briefly discussed.
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Affiliation(s)
- Aleksandra E Dawid
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw , Pasteura 1, 02-093 Warsaw, Poland
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw , Pasteura 1, 02-093 Warsaw, Poland
| | - Andrzej Kolinski
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw , Pasteura 1, 02-093 Warsaw, Poland
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26
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Kar P, Feig M. Hybrid All-Atom/Coarse-Grained Simulations of Proteins by Direct Coupling of CHARMM and PRIMO Force Fields. J Chem Theory Comput 2017; 13:5753-5765. [PMID: 28992696 DOI: 10.1021/acs.jctc.7b00840] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Hybrid all-atom/coarse-grained (AA/CG) simulations of proteins offer a computationally efficient compromise where atomistic details are only applied to biologically relevant regions while benefiting from the speedup of treating the remaining parts of a given system at the CG level. The recently developed CG model, PRIMO, allows a direct coupling with an atomistic force field with no additional modifications or coupling terms and the ability to carry out dynamic simulations without any restraints on secondary or tertiary structures. A hybrid AA/CG scheme based on combining all-atom CHARMM and coarse-grained PRIMO representations was validated via molecular dynamics and replica exchange simulations of soluble and membrane proteins. The AA/CG scheme was also tested in the calculation of the free energy profile for the transition from the closed to the open state of adenylate kinase via umbrella sampling molecular dynamics method. The overall finding is that the AA/CG scheme generates dynamics and energetics that are qualitatively and quantitatively comparable to AA simulations while offering the computational advantages of coarse-graining. This model opens the door to challenging applications where high accuracy is required only in parts of large biomolecular complexes.
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Affiliation(s)
- Parimal Kar
- Department of Biochemistry and Molecular Biology, Michigan State University , East Lansing, Michigan 48824, United States
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University , East Lansing, Michigan 48824, United States
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27
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Ramezanghorbani F, Dalgicdir C, Sayar M. A multi-state coarse grained modeling approach for an intrinsically disordered peptide. J Chem Phys 2017; 147:094103. [DOI: 10.1063/1.5001087] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
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28
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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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29
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Culka M, Gisdon FJ, Ullmann GM. Computational Biochemistry-Enzyme Mechanisms Explored. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2017; 109:77-112. [PMID: 28683923 DOI: 10.1016/bs.apcsb.2017.04.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Understanding enzyme mechanisms is a major task to achieve in order to comprehend how living cells work. Recent advances in biomolecular research provide huge amount of data on enzyme kinetics and structure. The analysis of diverse experimental results and their combination into an overall picture is, however, often challenging. Microscopic details of the enzymatic processes are often anticipated based on several hints from macroscopic experimental data. Computational biochemistry aims at creation of a computational model of an enzyme in order to explain microscopic details of the catalytic process and reproduce or predict macroscopic experimental findings. Results of such computations are in part complementary to experimental data and provide an explanation of a biochemical process at the microscopic level. In order to evaluate the mechanism of an enzyme, a structural model is constructed which can be analyzed by several theoretical approaches. Several simulation methods can and should be combined to get a reliable picture of the process of interest. Furthermore, abstract models of biological systems can be constructed combining computational and experimental data. In this review, we discuss structural computational models of enzymatic systems. We first discuss various models to simulate enzyme catalysis. Furthermore, we review various approaches how to characterize the enzyme mechanism both qualitatively and quantitatively using different modeling approaches.
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Affiliation(s)
- Martin Culka
- Computational Biochemistry, University of Bayreuth, Bayreuth, Germany
| | - Florian J Gisdon
- Computational Biochemistry, University of Bayreuth, Bayreuth, Germany
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30
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Feig M. Computational protein structure refinement: Almost there, yet still so far to go. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2017; 7:e1307. [PMID: 30613211 PMCID: PMC6319934 DOI: 10.1002/wcms.1307] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Protein structures are essential in modern biology yet experimental methods are far from being able to catch up with the rapid increase in available genomic data. Computational protein structure prediction methods aim to fill the gap while the role of protein structure refinement is to take approximate initial template-based models and bring them closer to the true native structure. Current methods for computational structure refinement rely on molecular dynamics simulations, related sampling methods, or iterative structure optimization protocols. The best methods are able to achieve moderate degrees of refinement but consistent refinement that can reach near-experimental accuracy remains elusive. Key issues revolve around the accuracy of the energy function, the inability to reliably rank multiple models, and the use of restraints that keep sampling close to the native state but also limit the degree of possible refinement. A different aspect is the question of what exactly the target of high-resolution refinement should be as experimental structures are affected by experimental conditions and different biological questions require varying levels of accuracy. While improvement of the global protein structure is a difficult problem, high-resolution refinement methods that improves local structural quality such as favorable stereochemistry and the avoidance of atomic clashes are much more successful.
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Affiliation(s)
- Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, 603 Wilson Rd., Room 218 BCH, East Lansing, MI, USA, ; 517-432-7439
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31
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Kmiecik S, Kolinski A. One-Dimensional Structural Properties of Proteins in the Coarse-Grained CABS Model. Methods Mol Biol 2017; 1484:83-113. [PMID: 27787822 DOI: 10.1007/978-1-4939-6406-2_8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Despite the significant increase in computational power, molecular modeling of protein structure using classical all-atom approaches remains inefficient, at least for most of the protein targets in the focus of biomedical research. Perhaps the most successful strategy to overcome the inefficiency problem is multiscale modeling to merge all-atom and coarse-grained models. This chapter describes a well-established CABS coarse-grained protein model. The CABS (C-Alpha, C-Beta, and Side chains) model assumes a 2-4 united-atom representation of amino acids, knowledge-based force field (derived from the statistical regularities seen in known protein sequences and structures) and efficient Monte Carlo sampling schemes (MC dynamics, MC replica-exchange, and combinations). A particular emphasis is given to the unique design of the CABS force-field, which is largely defined using one-dimensional structural properties of proteins, including protein secondary structure. This chapter also presents CABS-based modeling methods, including multiscale tools for de novo structure prediction, modeling of protein dynamics and prediction of protein-peptide complexes. CABS-based tools are freely available at http://biocomp.chem.uw.edu.pl/tools.
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Affiliation(s)
- Sebastian Kmiecik
- Faculty of Chemistry, University of Warsaw, Pasteura 1, Warszawa, 02-093, Poland
| | - Andrzej Kolinski
- Faculty of Chemistry, University of Warsaw, Pasteura 1, Warszawa, 02-093, Poland.
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32
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Kmiecik S, Gront D, Kolinski M, Wieteska L, Dawid AE, Kolinski A. Coarse-Grained Protein Models and Their Applications. Chem Rev 2016; 116:7898-936. [DOI: 10.1021/acs.chemrev.6b00163] [Citation(s) in RCA: 555] [Impact Index Per Article: 69.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Sebastian Kmiecik
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Dominik Gront
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Michal Kolinski
- Bioinformatics
Laboratory, Mossakowski Medical Research Center of the Polish Academy of Sciences, Pawinskiego 5, 02-106 Warsaw, Poland
| | - Lukasz Wieteska
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
- Department
of Medical Biochemistry, Medical University of Lodz, Mazowiecka 6/8, 92-215 Lodz, Poland
| | | | - Andrzej Kolinski
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
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Shao Q, Hall CK. A Discontinuous Potential Model for Protein-Protein Interactions. FOUNDATIONS OF MOLECULAR MODELING AND SIMULATION : SELECT PAPERS FROM FOMMS 2015. INTERNATIONAL CONFERENCE ON FOUNDATIONS OF MOLECULAR MODELING AND SIMULATION (6TH : 2015 : MOUNT HOOD, OR.) 2016; 2016:1-20. [PMID: 28580454 PMCID: PMC5453719 DOI: 10.1007/978-981-10-1128-3_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Protein-protein interactions play an important role in many biologic and industrial processes. In this work, we develop a two-bead-per-residue model that enables us to account for protein-protein interactions in a multi-protein system using discontinuous molecular dynamics simulations. This model deploys discontinuous potentials to describe the non-bonded interactions and virtual bonds to keep proteins in their native state. The geometric and energetic parameters are derived from the potentials of mean force between sidechain-sidechain, sidechain-backbone, and backbone-backbone pairs. The energetic parameters are scaled with the aim of matching the second virial coefficient of lysozyme reported in experiment. We also investigate the performance of several bond-building strategies.
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Affiliation(s)
- Qing Shao
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh 27695, USA
| | - Carol K Hall
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh 27695, USA
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Trovato F, O'Brien EP. Insights into Cotranslational Nascent Protein Behavior from Computer Simulations. Annu Rev Biophys 2016; 45:345-69. [PMID: 27297399 DOI: 10.1146/annurev-biophys-070915-094153] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Regulation of protein stability and function in vivo begins during protein synthesis, when the ribosome translates a messenger RNA into a nascent polypeptide. Cotranslational processes involving a nascent protein include folding, binding to other macromolecules, enzymatic modification, and secretion through membranes. Experiments have shown that the rate at which the ribosome adds amino acids to the elongating nascent chain influences the efficiency of these processes, with alterations to these rates possibly contributing to diseases, including some types of cancer. In this review, we discuss recent insights into cotranslational processes gained from molecular simulations, how different computational approaches have been combined to understand cotranslational processes at multiple scales, and the new scenarios illuminated by these simulations. We conclude by suggesting interesting questions that computational approaches in this research area can address over the next few years.
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Affiliation(s)
- Fabio Trovato
- Department of Chemistry, Pennsylvania State University, University Park, Pennsylvania 16802;
| | - Edward P O'Brien
- Department of Chemistry, Pennsylvania State University, University Park, Pennsylvania 16802;
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Enhanced Sampling of Coarse-Grained Transmembrane-Peptide Structure Formation from Hydrogen-Bond Replica Exchange. J Membr Biol 2014; 248:395-405. [DOI: 10.1007/s00232-014-9738-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2014] [Accepted: 09/27/2014] [Indexed: 12/14/2022]
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