1
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Iyer SS, Srivastava A. Membrane lateral organization from potential energy disconnectivity graph. Biophys Chem 2024; 313:107284. [PMID: 39002248 DOI: 10.1016/j.bpc.2024.107284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 06/17/2024] [Accepted: 06/19/2024] [Indexed: 07/15/2024]
Abstract
Understanding the thermodynamic and kinetic properties of biomolecules requires elucidation of their complex energy landscape. A disconnectivity graph analysis of the energy landscape provides a framework for mapping the multi-dimensional landscape onto a two-dimensional representation while preserving the key features of the energy landscape. Several studies show that the structure or shape of the disconnectity graph is directly associated with the function of protein and nucleic acid molecules. In this review, we discuss how disconnectivity analysis of the potential energy surface can be extended to lipid molecules to glean important information about membrane organization. The shape of the disconnectivity graphs can be used to predict the lateral organization of multi-component lipid bilayer. We hope that this review encourages the use of disconnectivity graphs routinely by membrane biophysicists to predict the lateral organization of lipids.
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Affiliation(s)
| | - Anand Srivastava
- Molecular Biophysics Unit, Indian Institute of Science Bangalore, C. V. Raman Road, Bangalore, Karnataka 560012, India.
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2
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Stulajter MM, Rappoport D. Reaction Networks Resemble Low-Dimensional Regular Lattices. J Chem Theory Comput 2024. [PMID: 39236261 DOI: 10.1021/acs.jctc.4c00810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2024]
Abstract
The computational exploration, manipulation, and design of complex chemical reactions face fundamental challenges related to the high-dimensional nature of potential energy surfaces (PESs) that govern reactivity. Accurately modeling complex reactions is crucial for understanding the chemical processes involved in, for example, organocatalysis, autocatalytic cycles, and one-pot molecular assembly. Our prior research demonstrated that discretizing PESs using heuristics based on bond breaking and bond formation produces a reaction network representation with a low-dimensional structure (metric space). We now find that these stoichiometry-preserving reaction networks possess additional, though approximate, structure and resemble low-dimensional regular lattices with a small amount of random edge rewiring. The heuristics-based discretization thus generates a nonlinear dimensionality reduction by a factor of 10 with an a posteriori error measure (probability of random rewiring). The structure becomes evident through a comparative analysis of CHNO reaction networks of varying stoichiometries against a panel of size-matched generative network models, taking into account their local, metric, and global properties. The generative models include random networks (Erdős-Rényi and bipartite random networks), regular lattices (periodic and nonperiodic), and network models with a tunable level of "randomness" (Watts-Strogatz graphs and regular lattices with random rewiring). The CHNO networks are simultaneously closely matched in all these properties by 3-4-dimensional regular lattices with 10% or less of edges randomly rewired. The effective dimensionality reduction is found to be independent of the system size, stoichiometry, and ruleset, suggesting that search and sampling algorithms for PESs of complex chemical reactions can be effectively leveraged.
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Affiliation(s)
- Miko M Stulajter
- Department of Chemistry, University of California Irvine, Irvine, California 92697, United States
- Computational Science Research Center, San Diego State University, San Diego, California 92182, United States
| | - Dmitrij Rappoport
- Department of Chemistry, University of California Irvine, Irvine, California 92697, United States
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3
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Gantzer P, Staub R, Harabuchi Y, Maeda S, Varnek A. Chemography-guided analysis of a reaction path network for ethylene hydrogenation with a model Wilkinson's catalyst. Mol Inform 2024:e202400063. [PMID: 39121023 DOI: 10.1002/minf.202400063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 07/11/2024] [Accepted: 07/19/2024] [Indexed: 08/11/2024]
Abstract
Visualization and analysis of large chemical reaction networks become rather challenging when conventional graph-based approaches are used. As an alternative, we propose to use the chemical cartography ("chemography") approach, describing the data distribution on a 2-dimensional map. Here, the Generative Topographic Mapping (GTM) algorithm - an advanced chemography approach - has been applied to visualize the reaction path network of a simplified Wilkinson's catalyst-catalyzed hydrogenation containing some 105 structures generated with the help of the Artificial Force Induced Reaction (AFIR) method using either Density Functional Theory or Neural Network Potential (NNP) for potential energy surface calculations. Using new atoms permutation invariant 3D descriptors for structure encoding, we've demonstrated that GTM possesses the abilities to cluster structures that share the same 2D representation, to visualize potential energy surface, to provide an insight on the reaction path exploration as a function of time and to compare reaction path networks obtained with different methods of energy assessment.
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Affiliation(s)
- Philippe Gantzer
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, Hokkaido, 001-0021, Japan
| | - Ruben Staub
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, Hokkaido, 001-0021, Japan
| | - Yu Harabuchi
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, Hokkaido, 001-0021, Japan
| | - Satoshi Maeda
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, Hokkaido, 001-0021, Japan
| | - Alexandre Varnek
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, Hokkaido, 001-0021, Japan
- Laboratory of Chemoinformatics, UMR 7140, CNRS, University of Strasbourg, Strasbourg, 67081, France
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4
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Poudel H, Wales DJ, Leitner DM. Vibrational Energy Landscapes and Energy Flow in GPCRs. J Phys Chem B 2024; 128:7568-7576. [PMID: 39058920 DOI: 10.1021/acs.jpcb.4c04513] [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: 07/28/2024]
Abstract
We construct and analyze disconnectivity graphs to provide the first graphical representation of the vibrational energy landscape of a protein, in this study β2AR, a G-protein coupled receptor (GPCR), in active and inactive states. The graphs, which indicate the relative free energy of each residue and the minimum free energy barriers for energy transfer between them, reveal important composition, structural and dynamic properties that mediate the flow of energy. Prolines and glycines, which contribute to GPCR plasticity and function, are identified as bottlenecks to energy transport along the backbone from which alternative pathways for energy transport via nearby noncovalent contacts emerge, seen also in the analysis of first passage time (FPT) distributions presented here. Striking differences between the disconnectivity graphs and FPT distributions for the inactive and active states of β2AR are found where structural and dynamic changes occur upon activation, contributing to allosteric regulation.
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Affiliation(s)
- Humanath Poudel
- Department of Chemistry, University of Nevada, Reno, Nevada 89557, United States
| | - David J Wales
- Yusuf Hamied Department of Chemistry, Cambridge University, Cambridge CB2 1EW, U.K
| | - David M Leitner
- Department of Chemistry, University of Nevada, Reno, Nevada 89557, United States
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5
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Nicy, Morgan JWR, Wales DJ. Energy landscapes for clusters of hexapeptides. J Chem Phys 2024; 161:054112. [PMID: 39092941 DOI: 10.1063/5.0220652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Accepted: 07/17/2024] [Indexed: 08/04/2024] Open
Abstract
We present the results for energy landscapes of hexapeptides obtained using interfaces to the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) program. We have used basin-hopping global optimization and discrete path sampling to explore the landscapes of hexapeptide monomers, dimers, and oligomers containing 10, 100, and 200 monomers modeled using a residue-level coarse-grained potential, Mpipi, implemented in LAMMPS. We find that the dimers of peptides containing amino acid residues that are better at promoting phase separation, such as tyrosine and arginine, have melting peaks at higher temperature in their heat capacity compared to phenylalanine and lysine, respectively. This observation correlates with previous work on the same uncapped hexapeptide monomers modeled using atomistic potential. For oligomers, we compare the variation in monomer conformations with radial distance and observe trends for selected angles calculated for each monomer. The LAMMPS interfaces to the GMIN and OPTIM programs for landscape exploration offer new opportunities to investigate larger systems and provide access to the coarse-grained potentials implemented within LAMMPS.
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Affiliation(s)
- Nicy
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - John W R Morgan
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - David J Wales
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
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6
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Schön JC. Energy landscapes-Past, present, and future: A perspective. J Chem Phys 2024; 161:050901. [PMID: 39101536 DOI: 10.1063/5.0212867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Accepted: 06/17/2024] [Indexed: 08/06/2024] Open
Abstract
Energy landscapes and the closely related cost function landscapes have been recognized in science, mathematics, and various other fields such as economics as being highly useful paradigms and tools for the description and analysis of the properties of many systems, ranging from glasses, proteins, and abstract global optimization problems to business models. A multitude of algorithms for the exploration and exploitation of such landscapes have been developed over the past five decades in the various fields of applications, where many re-inventions but also much cross-fertilization have occurred. Twenty-five years ago, trying to increase the fruitful interactions between workers in different fields led to the creation of workshops and small conferences dedicated to the study of energy landscapes in general instead of only focusing on specific applications. In this perspective, I will present some history of the development of energy landscape studies and try to provide an outlook on in what directions the field might evolve in the future and what larger challenges are going to lie ahead, both from a conceptual and a practical point of view, with the main focus on applications of energy landscapes in chemistry and physics.
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Affiliation(s)
- J C Schön
- Max-Planck-Institute for Solid State Research, Heisenbergstr. 1, D-70569 Stuttgart, Germany
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7
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Frank JT, Unke OT, Müller KR, Chmiela S. A Euclidean transformer for fast and stable machine learned force fields. Nat Commun 2024; 15:6539. [PMID: 39107296 PMCID: PMC11303804 DOI: 10.1038/s41467-024-50620-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 07/10/2024] [Indexed: 08/10/2024] Open
Abstract
Recent years have seen vast progress in the development of machine learned force fields (MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the reliability of MLFFs in molecular dynamics (MD) simulations is facing growing scrutiny due to concerns about instability over extended simulation timescales. Our findings suggest a potential connection between robustness to cumulative inaccuracies and the use of equivariant representations in MLFFs, but the computational cost associated with these representations can limit this advantage in practice. To address this, we propose a transformer architecture called SO3KRATES that combines sparse equivariant representations (Euclidean variables) with a self-attention mechanism that separates invariant and equivariant information, eliminating the need for expensive tensor products. SO3KRATES achieves a unique combination of accuracy, stability, and speed that enables insightful analysis of quantum properties of matter on extended time and system size scales. To showcase this capability, we generate stable MD trajectories for flexible peptides and supra-molecular structures with hundreds of atoms. Furthermore, we investigate the PES topology for medium-sized chainlike molecules (e.g., small peptides) by exploring thousands of minima. Remarkably, SO3KRATES demonstrates the ability to strike a balance between the conflicting demands of stability and the emergence of new minimum-energy conformations beyond the training data, which is crucial for realistic exploration tasks in the field of biochemistry.
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Affiliation(s)
- J Thorben Frank
- Machine Learning Group, TU Berlin, Berlin, Germany
- BIFOLD, Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
| | | | - Klaus-Robert Müller
- Machine Learning Group, TU Berlin, Berlin, Germany.
- BIFOLD, Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.
- Google DeepMind, Berlin, Germany.
- Department of Artificial Intelligence, Korea University, Seoul, Korea.
- Max Planck Institut für Informatik, Saarbrücken, Germany.
| | - Stefan Chmiela
- Machine Learning Group, TU Berlin, Berlin, Germany.
- BIFOLD, Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.
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8
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Cerasoli FT, Donadio D. Effective optimization of atomic decoration in giant and superstructurally ordered crystals with machine learning. J Chem Phys 2024; 161:044101. [PMID: 39037130 DOI: 10.1063/5.0213132] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 07/05/2024] [Indexed: 07/23/2024] Open
Abstract
Crystals with complicated geometry are often observed with mixed chemical occupancy among Wyckoff sites, presenting a unique challenge for accurate atomic modeling. Similar systems possessing exact occupancy on all the sites can exhibit superstructural ordering, dramatically inflating the unit cell size. In this work, a crystal graph convolutional neural network (CGCNN) is used to predict optimal atomic decorations on fixed crystalline geometries. This is achieved with a site permutation search (SPS) optimization algorithm based on Monte Carlo moves combined with simulated annealing and basin-hopping techniques. Our approach relies on the evidence that, for a given chemical composition, a CGCNN estimates the correct energetic ordering of different atomic decorations, as predicted by electronic structure calculations. This provides a suitable energy landscape that can be optimized according to site occupation, allowing the prediction of chemical decoration in crystals exhibiting mixed or disordered occupancy, or superstructural ordering. Verification of the procedure is carried out on several known compounds, including the superstructurally ordered clathrate compound Rb8Ga27Sb16 and vacancy-ordered perovskite Cs2SnI6, neither of which was previously seen during the neural network training. In addition, the critical temperature of an order-disorder phase transition in solid solution CuZn is probed with our SPS routines by sampling site configuration trajectories in the canonical ensemble. This strategy provides an accurate method for determining favorable decoration in complex crystals and analyzing site occupation at unprecedented speed and scale.
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Affiliation(s)
- Frank T Cerasoli
- Department of Chemistry, University of California, Davis, California 95616, USA
| | - Davide Donadio
- Department of Chemistry, University of California, Davis, California 95616, USA
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9
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Po HF, Yeung CH. Complete realization of energy landscapes and non-equilibrium trapping dynamics in small spin glass and optimization problems. Sci Rep 2024; 14:15675. [PMID: 38977813 PMCID: PMC11231248 DOI: 10.1038/s41598-024-65493-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 06/20/2024] [Indexed: 07/10/2024] Open
Abstract
Energy landscapes are high-dimensional surfaces underlie all physical systems, which determine crucially the energetic and behavioral dependence of the systems on variable configurations, but are difficult to be analyzed due to their high-dimensional nature. Here we introduce an approach to reveal for the complete energy landscapes of spin glasses and Boolean satisfiability problems with a small system size, and unravels their non-equilibrium dynamics at an arbitrary temperature for an arbitrarily long time. Remarkably, our results show that it can be less likely for the system to attain ground states when temperature decreases, due to trapping in individual local minima, which ceases at a different time, leading to multiple abrupt jumps in the ground-state probability. For large systems, we introduce a variant approach to extract partially the energy landscapes and observe both semi-analytically and in simulations similar phenomena. This work introduces new methodology to unravel the energy landscapes and non-equilibrium dynamics of glassy systems, and provides us with a clear, complete and new physical picture on their long-time behaviors inaccessible by existing approaches.
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Grants
- GRF 18304316 Research Grants Council of the Hong Kong Special Administrative Region, China
- GRF 18301217 Research Grants Council of the Hong Kong Special Administrative Region, China
- GRF 18301119 Research Grants Council of the Hong Kong Special Administrative Region, China
- GRF 18300623 Research Grants Council of the Hong Kong Special Administrative Region, China
- FLASS/DRF 04418 Dean's Research Fund of the Faculty of Liberal Arts and Social Sciences, The Education University of Hong Kong, Hong Kong Special Administrative Region, China
- FLASS/ROP 04396 Dean's Research Fund of the Faculty of Liberal Arts and Social Sciences, The Education University of Hong Kong, Hong Kong Special Administrative Region, China
- FLASS/DRF 04624 Dean's Research Fund of the Faculty of Liberal Arts and Social Sciences, The Education University of Hong Kong, Hong Kong Special Administrative Region, China
- RG67 2018-2019R R4015 Research Development Office Internal Research Grant, The Education University of Hong Kong, Hong Kong Special Administrative Region, China
- No. RG31 2020-2021R R4152 Research Development Office Internal Research Grant, The Education University of Hong Kong, Hong Kong Special Administrative Region, China
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Affiliation(s)
- Ho Fai Po
- Department of Science and Environmental Studies, The Education University of Hong Kong, 10 Lo Ping Road, Hong Kong, China
- Department of Mathematics, Aston University, Birmingham, B4 7ET, UK
| | - Chi Ho Yeung
- Department of Science and Environmental Studies, The Education University of Hong Kong, 10 Lo Ping Road, Hong Kong, China.
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10
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Keith A, Brichtová EP, Barber JG, Wales DJ, Jackson SE, Röder K. Energy Landscapes and Structural Ensembles of Glucagon-like Peptide-1 Monomers. J Phys Chem B 2024; 128:5601-5611. [PMID: 38831581 PMCID: PMC11182347 DOI: 10.1021/acs.jpcb.4c01794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/16/2024] [Accepted: 05/20/2024] [Indexed: 06/05/2024]
Abstract
While GLP-1 and its analogues are important pharmaceutical agents in the treatment of type 2 diabetes and obesity, their susceptibility to aggregate into amyloid fibrils poses a significant safety issue. Many factors may contribute to the aggregation propensity, including pH. While it is known that the monomeric structure of GLP-1 has a strong impact on primary nucleation, probing its diverse structural ensemble is challenging. Here, we investigated the monomer structural ensembles at pH 3, 4, and 7.5 using state-of-the-art computational methods in combination with experimental data. We found significant stabilization of β-strand structures and destabilization of helical structures at lower pH, correlating with observed aggregation lag times, which are lower under these conditions. We further identified helical defects at pH 4, which led to the fastest observed aggregation, in agreement with our far-UV circular dichroism data. The detailed atomistic structures that result from the computational studies help to rationalize the experimental results on the aggregation propensity of GLP-1. This work provides a new insight into the pH-dependence of monomeric structural ensembles of GLP-1 and connects them to experimental observations.
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Affiliation(s)
- Alasdair
D. Keith
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
- Now:
Department of Biochemistry, School of Medicine, Emory University, 1510 Clifton Rd NE, Atlanta, Georgia 30322, United States
| | - Eva Přáda Brichtová
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
- Now:
Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Gumpendorferstr. 1A, Vienna 1060, Austria
| | - Jack G. Barber
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - David J. Wales
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - Sophie E. Jackson
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - Konstantin Röder
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
- Now:
Randall Centre for Cell & Molecular Biophysics, King’s College London, Great Maze Pond, London SE1 1UL, U.K.
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11
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Röder K, Pasquali S. Assessing RNA atomistic force fields via energy landscape explorations in implicit solvent. Biophys Rev 2024; 16:285-295. [PMID: 39099837 PMCID: PMC11297004 DOI: 10.1007/s12551-024-01202-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 05/29/2024] [Indexed: 08/06/2024] Open
Abstract
Predicting the structure and dynamics of RNA molecules still proves challenging because of the relative scarcity of experimental RNA structures on which to train models and the very sensitive nature of RNA towards its environment. In the last decade, several atomistic force fields specifically designed for RNA have been proposed and are commonly used for simulations. However, it is not necessarily clear which force field is the most suitable for a given RNA molecule. In this contribution, we propose the use of the computational energy landscape framework to explore the energy landscape of RNA systems as it can bring complementary information to the more standard approaches of enhanced sampling simulations based on molecular dynamics. We apply the EL framework to the study of a small RNA pseudoknot, the Aquifex aeolicus tmRNA pseudoknot PK1, and we compare the results of five different RNA force fields currently available in the AMBER simulation software, in implicit solvent. With this computational approach, we can not only compare the predicted 'native' states for the different force fields, but the method enables us to study metastable states as well. As a result, our comparison not only looks at structural features of low energy folded structures, but provides insight into folding pathways and higher energy excited states, opening to the possibility of assessing the validity of force fields also based on kinetics and experiments providing information on metastable and unfolded states. Supplementary Information The online version contains supplementary material available at 10.1007/s12551-024-01202-9.
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Affiliation(s)
- Konstantin Röder
- Randall Centre for Cell & Molecular Biophysics, King’s College London, London, SE1 1UL UK
| | - Samuela Pasquali
- Laboratoire Biologie Functionnelle Et Adaptative, CNRS UMR 8251, Inserm ERL U1133, Université Paris Cité , 35 Rue Hélène Brion, Paris, France
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12
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Uesugi F, Wen Y, Hashimoto A, Ishii M. Prediction of nanocomposite properties and process optimization using persistent homology and machine learning. Micron 2024; 183:103664. [PMID: 38820861 DOI: 10.1016/j.micron.2024.103664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 05/18/2024] [Accepted: 05/22/2024] [Indexed: 06/02/2024]
Abstract
Physical property prediction and synthesis process optimization are key targets in material informatics. In this study, we propose a machine learning approach that utilizes ridge regression to predict the oxygen permeability at fuel cell electrode surfaces and determine the optimal process temperature. These predictions are based on a persistence diagram derived from tomographic images captured using transmission electron microscopy (TEM). Through machine learning analysis of the complex structures present in the Pt/CeO2 nanocomposites, we discovered that l2 regularization considering diverse structural elements is more appropriate than l1 regularization (sparse modeling). Notably, our model successfully captured the activation energy of oxygen permeability, a phenomenon that could not be solely explained by the geometric feature of the Betti numbers, as demonstrated in a previous study. The correspondence between the ridge regression coefficient and persistence diagram revealed the formation process of the local and three-dimensional structures of CeO2 and their contributions to pre-exponential factor and activation energies. This analysis facilitated the determination of the annealing temperature required to achieve the optimal structure and accurately predict the physical properties.
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Affiliation(s)
- Fumihiko Uesugi
- National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan.
| | - Yu Wen
- National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan; University of Tsukuba, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan
| | - Ayako Hashimoto
- National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan; University of Tsukuba, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan
| | - Masashi Ishii
- National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
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13
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Nagahata Y, Kobayashi M, Toda M, Maeda S, Taketsugu T, Komatsuzaki T. An encompassed representation of timescale hierarchies in first-order reaction network. Proc Natl Acad Sci U S A 2024; 121:e2317781121. [PMID: 38758700 PMCID: PMC11126998 DOI: 10.1073/pnas.2317781121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 04/16/2024] [Indexed: 05/19/2024] Open
Abstract
Complex networks are pervasive in various fields such as chemistry, biology, and sociology. In chemistry, first-order reaction networks are represented by a set of first-order differential equations, which can be constructed from the underlying energy landscape. However, as the number of nodes increases, it becomes more challenging to understand complex kinetics across different timescales. Hence, how to construct an interpretable, coarse-graining scheme that preserves the underlying timescales of overall reactions is of crucial importance. Here, we develop a scheme to capture the underlying hierarchical subsets of nodes, and a series of coarse-grained (reduced-dimensional) rate equations between the subsets as a function of time resolution from the original reaction network. Each of the coarse-grained representations guarantees to preserve the underlying slow characteristic timescales in the original network. The crux is the construction of a lumping scheme incorporating a similarity measure in deciphering the underlying timescale hierarchy, which does not rely on the assumption of equilibrium. As an illustrative example, we apply the scheme to four-state Markovian models and Claisen rearrangement of allyl vinyl ether (AVE), and demonstrate that the reduced-dimensional representation accurately reproduces not only the slowest but also the faster timescales of overall reactions although other reduction schemes based on equilibrium assumption well reproduce the slowest timescale but fail to reproduce the second-to-fourth slowest timescales with the same accuracy. Our scheme can be applied not only to the reaction networks but also to networks in other fields, which helps us encompass their hierarchical structures of the complex kinetics over timescales.
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Affiliation(s)
- Yutaka Nagahata
- The Institute for Chemical Reaction Design and Discovery, Hokkaido University, Sapporo001-0021, Japan
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University, Sapporo001-0020, Japan
| | - Masato Kobayashi
- The Institute for Chemical Reaction Design and Discovery, Hokkaido University, Sapporo001-0021, Japan
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University, Sapporo001-0020, Japan
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo060-0810, Japan
| | - Mikito Toda
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University, Sapporo001-0020, Japan
- Faculty Division of Natural Sciences, Nara Women’s University, Nara630-8506, Japan
- Graduate School of Information Science, University of Hyogo, Kobe650-0047, Japan
| | - Satoshi Maeda
- The Institute for Chemical Reaction Design and Discovery, Hokkaido University, Sapporo001-0021, Japan
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University, Sapporo001-0020, Japan
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo060-0810, Japan
| | - Tetsuya Taketsugu
- The Institute for Chemical Reaction Design and Discovery, Hokkaido University, Sapporo001-0021, Japan
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University, Sapporo001-0020, Japan
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo060-0810, Japan
| | - Tamiki Komatsuzaki
- The Institute for Chemical Reaction Design and Discovery, Hokkaido University, Sapporo001-0021, Japan
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University, Sapporo001-0020, Japan
- Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita565-0871, Japan
- The Institute of Scientific and Industrial Research, Osaka University, Ibaraki567-0047, Japan
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14
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Xing L, Guo Z, Long Z. Energy landscape analysis of brain network dynamics in Alzheimer's disease. Front Aging Neurosci 2024; 16:1375091. [PMID: 38813531 PMCID: PMC11133694 DOI: 10.3389/fnagi.2024.1375091] [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: 01/23/2024] [Accepted: 04/30/2024] [Indexed: 05/31/2024] Open
Abstract
Background Alzheimer's disease (AD) is a common neurodegenerative dementia, characterized by abnormal dynamic functional connectivity (DFC). Traditional DFC analysis, assuming linear brain dynamics, may neglect the complexity of the brain's nonlinear interactions. Energy landscape analysis offers a holistic, nonlinear perspective to investigate brain network attractor dynamics, which was applied to resting-state fMRI data for AD in this study. Methods This study utilized resting-state fMRI data from 60 individuals, comparing 30 Alzheimer's patients with 30 controls, from the Alzheimer's Disease Neuroimaging Initiative. Energy landscape analysis was applied to the data to characterize the aberrant brain network dynamics of AD patients. Results The AD group stayed in the co-activation state for less time than the healthy control (HC) group, and a positive correlation was identified between the transition frequency of the co-activation state and behavior performance. Furthermore, the AD group showed a higher occurrence frequency and transition frequency of the cognitive control state and sensory integration state than the HC group. The transition between the two states was positively correlated with behavior performance. Conclusion The results suggest that the co-activation state could be important to cognitive processing and that the AD group possibly raised cognitive ability by increasing the occurrence and transition between the impaired cognitive control and sensory integration states.
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Affiliation(s)
- Le Xing
- The State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Zhitao Guo
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Zhiying Long
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
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15
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Schwerdtfeger P, Wales DJ. 100 Years of the Lennard-Jones Potential. J Chem Theory Comput 2024; 20:3379-3405. [PMID: 38669689 DOI: 10.1021/acs.jctc.4c00135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024]
Abstract
It is now 100 years since Lennard-Jones published his first paper introducing the now famous potential that bears his name. It is therefore timely to reflect on the many achievements, as well as the limitations, of this potential in the theory of atomic and molecular interactions, where applications range from descriptions of intermolecular forces to molecules, clusters, and condensed matter.
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Affiliation(s)
- Peter Schwerdtfeger
- Centre for Theoretical Chemistry and Physics, The New Zealand Institute for Advanced Study, Massey University Auckland, Private Bag 102904, Auckland 0745, New Zealand
| | - David J Wales
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
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16
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Yamamoto K, Sakaguchi M, Onishi A, Yokoyama S, Matsui Y, Yamamoto W, Onizawa H, Fujii T, Murata K, Tanaka M, Hashimoto M, Matsuda S, Morinobu A. Energy landscape analysis and time-series clustering analysis of patient state multistability related to rheumatoid arthritis drug treatment: The KURAMA cohort study. PLoS One 2024; 19:e0302308. [PMID: 38709812 PMCID: PMC11073743 DOI: 10.1371/journal.pone.0302308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 04/02/2024] [Indexed: 05/08/2024] Open
Abstract
Rheumatoid arthritis causes joint inflammation due to immune abnormalities, resulting in joint pain and swelling. In recent years, there have been considerable advancements in the treatment of this disease. However, only approximately 60% of patients achieve remission. Patients with multifactorial diseases shift between states from day to day. Patients may remain in a good or poor state with few or no transitions, or they may switch between states frequently. The visualization of time-dependent state transitions, based on the evaluation axis of stable/unstable states, may provide useful information for achieving rheumatoid arthritis treatment goals. Energy landscape analysis can be used to quantitatively determine the stability/instability of each state in terms of energy. Time-series clustering is another method used to classify transitions into different groups to identify potential patterns within a time-series dataset. The objective of this study was to utilize energy landscape analysis and time-series clustering to evaluate multidimensional time-series data in terms of multistability. We profiled each patient's state transitions during treatment using energy landscape analysis and time-series clustering. Energy landscape analysis divided state transitions into two patterns: "good stability leading to remission" and "poor stability leading to treatment dead-end." The number of patients whose disease status improved increased markedly until approximately 6 months after treatment initiation and then plateaued after 1 year. Time-series clustering grouped patients into three clusters: "toward good stability," "toward poor stability," and "unstable." Patients in the "unstable" cluster are considered to have clinical courses that are difficult to predict; therefore, these patients should be treated with more care. Early disease detection and treatment initiation are important. The evaluation of state multistability enables us to understand a patient's current state in the context of overall state transitions related to rheumatoid arthritis drug treatment and to predict future state transitions.
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Affiliation(s)
- Keiichi Yamamoto
- Division of Data Science, Center for Industrial Research and Innovation, Translational Research Institute for Medical Innovation, Osaka Dental University, Hirakata City, Osaka, Japan
| | - Masahiko Sakaguchi
- Department of Engineering Informatics, Faculty of Information and Communication Engineering, Osaka Electro-Communication University, Neyagawa City, Osaka, Japan
| | - Akira Onishi
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Sakyo, Kyoto, Japan
| | | | | | - Wataru Yamamoto
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Sakyo, Kyoto, Japan
- Department of Health Information Management, Kurashiki Sweet Hospital, Nakasho, Kurashiki, Kurashiki City, Okayama Prefecture, Japan
| | - Hideo Onizawa
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Sakyo, Kyoto, Japan
| | - Takayuki Fujii
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Sakyo, Kyoto, Japan
| | - Koichi Murata
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Sakyo, Kyoto, Japan
| | - Masao Tanaka
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Sakyo, Kyoto, Japan
| | - Motomu Hashimoto
- Department of Clinical Immunology, Osaka Metropolitan University Graduate School of Medicine, Osaka City, Japan
| | - Shuichi Matsuda
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Sakyo, Kyoto, Japan
| | - Akio Morinobu
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Sakyo, Kyoto, Japan
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17
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Wesołowski P, Wales DJ, Pracht P. Multilevel Framework for Analysis of Protein Folding Involving Disulfide Bond Formation. J Phys Chem B 2024; 128:3145-3156. [PMID: 38512062 PMCID: PMC11000224 DOI: 10.1021/acs.jpcb.4c00104] [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: 01/05/2024] [Revised: 03/06/2024] [Accepted: 03/06/2024] [Indexed: 03/22/2024]
Abstract
In this study, a three-layered multicenter ONIOM approach is implemented to characterize the naive folding pathway of bovine pancreatic trypsin inhibitor (BPTI). Each layer represents a distinct level of theory, where the initial layer, encompassing the entire protein, is modeled by a general all-atom force-field GFN-FF. An intermediate electronic structure layer consisting of three multicenter fragments is introduced with the state-of-the-art semiempirical tight-binding method GFN2-xTB. Higher accuracy, specifically addressing the breaking and formation of the three disulfide bonds, is achieved at the innermost layer using the composite DFT method r2SCAN-3c. Our analysis sheds light on the structural stability of BPTI, particularly the significance of interlinking disulfide bonds. The accuracy and efficiency of the multicenter QM/SQM/MM approach are benchmarked using the oxidative formation of cystine. For the folding pathway of BPTI, relative stabilities are investigated through the calculation of free energy contributions for selected intermediates, focusing on the impact of the disulfide bond. Our results highlight the intricate trade-off between accuracy and computational cost, demonstrating that the multicenter ONIOM approach provides a well-balanced and comprehensive solution to describe electronic structure effects in biomolecular systems. We conclude that multiscale energy landscape exploration provides a robust methodology for the study of intriguing biological targets.
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Affiliation(s)
- Patryk
A. Wesołowski
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - David J. Wales
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - Philipp Pracht
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
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18
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Woods EJ, Wales DJ. Analysis and interpretation of first passage time distributions featuring rare events. Phys Chem Chem Phys 2024; 26:1640-1657. [PMID: 38059562 DOI: 10.1039/d3cp04199a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
In this contribution we consider theory and associated computational tools to treat the kinetics associated with competing pathways on multifunnel energy landscapes. Multifunnel landscapes are associated with molecular switches and multifunctional materials, and are expected to exhibit multiple relaxation time scales and associated thermodynamic signatures in the heat capacity. Our focus here is on the first passage time distribution, which is encoded in a kinetic transition network containing all the locally stable states and the pathways between them. This network can be renormalised to reduce the dimensionality, while exactly conserving the mean first passage time and approximately conserving the full distribution. The structure of the reduced network can be visualised using disconnectivity graphs. We show how features in the first passage time distribution can be associated with specific kinetic traps, and how the appearance of competing relaxation time scales depends on the starting conditions. The theory is tested for two model landscapes and applied to an atomic cluster and a disordered peptide. Our most important contribution is probably the reconstruction of the full distribution for long time scales, where numerical problems prevent direct calculations. Here we combine accurate treatment of the mean first passage time with the reliable part of the distribution corresponding to faster time scales. Hence we now have a fundamental understanding of both thermodynamic and kinetic signatures of multifunnel landscapes.
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Affiliation(s)
- Esmae J Woods
- Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge CB3 0HE, UK
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
| | - David J Wales
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
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19
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Keith AD, Sawyer EB, Choy DCY, Xie Y, Biggs GS, Klein OJ, Brear PD, Wales DJ, Barker PD. Combining experiment and energy landscapes to explore anaerobic heme breakdown in multifunctional hemoproteins. Phys Chem Chem Phys 2024; 26:695-712. [PMID: 38053511 DOI: 10.1039/d3cp03897a] [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: 12/07/2023]
Abstract
To survive, many pathogens extract heme from their host organism and break down the porphyrin scaffold to sequester the Fe2+ ion via a heme oxygenase. Recent studies have revealed that certain pathogens can anaerobically degrade heme. Our own research has shown that one such pathway proceeds via NADH-dependent heme degradation, which has been identified in a family of hemoproteins from a range of bacteria. HemS, from Yersinia enterocolitica, is the main focus of this work, along with HmuS (Yersinia pestis), ChuS (Escherichia coli) and ShuS (Shigella dysenteriae). We combine experiments, Energy Landscape Theory, and a bioinformatic investigation to place these homologues within a wider phylogenetic context. A subset of these hemoproteins are known to bind certain DNA promoter regions, suggesting not only that they can catalytically degrade heme, but that they are also involved in transcriptional modulation responding to heme flux. Many of the bacterial species responsible for these hemoproteins (including those that produce HemS, ChuS and ShuS) are known to specifically target oxygen-depleted regions of the gastrointestinal tract. A deeper understanding of anaerobic heme breakdown processes exploited by these pathogens could therefore prove useful in the development of future strategies for disease prevention.
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Affiliation(s)
- Alasdair D Keith
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
| | - Elizabeth B Sawyer
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
| | - Desmond C Y Choy
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
| | - Yuhang Xie
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
| | - George S Biggs
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
| | - Oskar James Klein
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
| | - Paul D Brear
- Department of Biochemistry, University of Cambridge, Sanger Building, Cambridge CB2 1GA, UK
| | - David J Wales
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
| | - Paul D Barker
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
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20
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Meadows J, Röder K. The Effect of Pulling and Twisting Forces on Chameleon Sequence Peptides. Chemphyschem 2023; 24:e202300351. [PMID: 37818741 DOI: 10.1002/cphc.202300351] [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: 05/16/2023] [Revised: 10/06/2023] [Accepted: 10/06/2023] [Indexed: 10/13/2023]
Abstract
Chameleon sequences are amino acid sequences found in several distinct configurations in experiment. They challenge our understanding of the link between sequence and structure, and provide insight into structural competition in proteins. Here, we study the energy landscapes for three such sequences, and interrogate how pulling and twisting forces impact the available structural ensembles. Chameleon sequences do not necessarily exhibit multiple structural ensembles on a multifunnel energy landscape when we consider them in isolation. The application of even small forces leads to drastic changes in the energy landscapes. For pulling forces, we observe transitions from helical to extended structures in a very small span of forces. For twisting forces, the picture is much more complex, and highly dependent on the magnitude and handedness of the applied force as well as the reference angle for the twist. Depending on these parameters, more complex and more simplistic energy landscapes are observed alongside more and less diverse structural ensembles. The impact of even small forces is significant, confirming their likely role in folding events. In addition, small forces exerted by the remaining scaffold of a protein may be sufficient to lead to the adoption of a specific structural ensemble by a chameleon sequence.
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Affiliation(s)
- James Meadows
- Department of Chemistry, Durham University, Stockton Road, Durham, DH1 3LE, UK
- Previous affiliation: Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Konstantin Röder
- Randall Centre for Cell & Molecular Biophysics, King's College London, Guy's Campus, Great Maze Pond, London, SE1 1UL, UK
- Previous affiliation: Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
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21
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Wesołowski PA, Sieradzan AK, Winnicki MJ, Morgan JWR, Wales DJ. Energy landscapes for proteins described by the UNRES coarse-grained potential. Biophys Chem 2023; 303:107107. [PMID: 37862761 DOI: 10.1016/j.bpc.2023.107107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/27/2023] [Accepted: 09/04/2023] [Indexed: 10/22/2023]
Abstract
The self-assembly of proteins is encoded in the underlying potential energy surface (PES), from which we can predict structure, dynamics, and thermodynamic properties. However, the corresponding analysis becomes increasingly challenging with larger protein sizes, due to the computational time required, which grows significantly with the number of atoms. Coarse-grained models offer an attractive approach to reduce the computational cost. In this Feature Article, we describe our implementation of the UNited RESidue (UNRES) coarse-grained potential in the Cambridge energy landscapes software. We have applied this framework to explore the energy landscapes of four proteins that exhibit native states involving different secondary structures. Here we have tested the ability of the UNRES potential to represent the global energy landscape of proteins containing up to 100 amino acid residues. The resulting potential energy landscapes exhibit good agreement with experiment, with low-lying minima close to the PDB geometries and to results obtained using the all-atom AMBER force field. The new program interfaces will allow us to investigate larger biomolecules in future work, using the UNRES potential in combination with all the methodology available in the computational energy landscapes framework.
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Affiliation(s)
- Patryk A Wesołowski
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
| | - Adam K Sieradzan
- Faculty of Chemistry, Gdansk University, Wita Stwosza 63, 80-308 Gdańsk, Poland
| | - Michał J Winnicki
- Faculty of Chemistry, Gdansk University, Wita Stwosza 63, 80-308 Gdańsk, Poland; Oklahoma Medical Research Foundation, 825 NE 13th St., Oklahoma City, OK 73104, USA; Intercollegiate Faculty of Biotechnology, University of Gdańsk and the Medical University of Gdańsk, Abrahama 58, 80-307 Gdańsk, Poland
| | - John W R Morgan
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK; Downing College, University of Cambridge, Regent St., Cambridge CB2 1DQ, UK
| | - David J Wales
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
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22
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Teramoto H, Saito T, Aoki M, Murayama B, Kobayashi M, Nakamura T, Taketsugu T. Reproducing the Reaction Route Map on the Shape Space from Its Quotient by the Complete Nuclear Permutation-Inversion Group. J Chem Theory Comput 2023; 19:5886-5896. [PMID: 37642714 DOI: 10.1021/acs.jctc.3c00500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
This study develops an algorithm to reproduce reaction route maps (RRMs) in the shape space from the outputs of potential search algorithms. To demonstrate the algorithm, global reaction route mapping is utilized as a potential search algorithm, but the proposed algorithm should work with other potential search algorithms in principle. The proposed algorithm does not require any encoding of the molecular configurations and is thus applicable to complicated realistic molecules for which efficient encoding is not readily available. We show that subgraphs of an RRM mapped to each other by the action of the symmetry group are isomorphic and also provide an algorithm to compute the set of feasible transformations in the sense of Longuet-Higgins. We demonstrate the proposed algorithm in toy models and in more realistic molecules. Finally, we remark on absolute rate theory from our perspective.
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Affiliation(s)
- Hiroshi Teramoto
- Faculty of Engineering Science, Kansai University, Suita 564-8680, Japan
| | - Takuya Saito
- Department of Mathematics, Faculty of Science, Hokkaido University, Sapporo 060-0810, Japan
- Department of Economics and Statistics, University of Turin, 10124 Turin, Italy
| | - Masamitsu Aoki
- Department of Mathematics, Faculty of Science, Hokkaido University, Sapporo 060-0810, Japan
| | - Burai Murayama
- Graduate School of Chemical Sciences and Engineering, Hokkaido University, Sapporo 060-0810, Japan
| | - Masato Kobayashi
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo 060-0810, Japan
- WPI-ICReDD, Hokkaido University, Sapporo 001-0021, Japan
| | - Takenobu Nakamura
- National Institute of Advanced Industrial Science and Technology, Tsukuba 305-8568, Japan
| | - Tetsuya Taketsugu
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo 060-0810, Japan
- WPI-ICReDD, Hokkaido University, Sapporo 001-0021, Japan
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23
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Nicy, Collepardo-Guevara R, Joseph JA, Wales DJ. Energy landscapes and heat capacity signatures for peptides correlate with phase separation propensity. QRB DISCOVERY 2023; 4:e7. [PMID: 37771761 PMCID: PMC10523320 DOI: 10.1017/qrd.2023.5] [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: 04/27/2023] [Revised: 07/09/2023] [Accepted: 07/17/2023] [Indexed: 09/30/2023] Open
Abstract
Phase separation plays an important role in the formation of membraneless compartments within the cell and intrinsically disordered proteins with low-complexity sequences can drive this compartmentalisation. Various intermolecular forces, such as aromatic-aromatic and cation-aromatic interactions, promote phase separation. However, little is known about how the ability of proteins to phase separate under physiological conditions is encoded in their energy landscapes and this is the focus of the present investigation. Our results provide a first glimpse into how the energy landscapes of minimal peptides that contain - and cation- interactions differ from the peptides that lack amino acids with such interactions. The peaks in the heat capacity () as a function of temperature report on alternative low-lying conformations that differ significantly in terms of their enthalpic and entropic contributions. The analysis and subsequent quantification of frustration of the energy landscape suggest that the interactions that promote phase separation lead to features (peaks or inflection points) at low temperatures in . More features may occur for peptides containing residues with better phase separation propensity and the energy landscape is more frustrated for such peptides. Overall, this work links the features in the underlying single-molecule potential energy landscapes to their collective phase separation behaviour and identifies quantities ( and frustration metric) that can be utilised in soft material design.
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Affiliation(s)
- Nicy
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Rosana Collepardo-Guevara
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
- Department of Physics, University of Cambridge, Cambridge, UK
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Jerelle A. Joseph
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, USA
| | - David J. Wales
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
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24
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Anderson MC, Woods EJ, Fay TP, Wales DJ, Limmer DT. On the Mechanism of Polaritonic Rate Suppression from Quantum Transition Paths. J Phys Chem Lett 2023:6888-6894. [PMID: 37494137 DOI: 10.1021/acs.jpclett.3c01188] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
Polariton chemistry holds promise for facilitating mode-selective chemical reactions, but the underlying mechanism behind the rate modifications observed under strong vibrational coupling is not well understood. Using the recently developed quantum transition path theory, we have uncovered a mechanism of resonant suppression of a thermal reaction rate in a simple model polaritonic system consisting of a reactive mode in a bath confined to a lossless microcavity with a single photon mode. We observed the formation of a polariton during rate-limiting transitions on reactive pathways and identified the concomitant rate suppression as being due to hybridization between the reactive mode and the cavity mode, which inhibits bath-mediated tunneling. The transition probabilities that define the quantum master equation can be directly translated into a visualization of the corresponding polariton energy landscape. This landscape exhibits a double funnel structure with a large barrier between the initial and final states.
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Affiliation(s)
- Michelle C Anderson
- Department of Chemistry, University of California, Berkeley 94720, United States
| | - Esmae J Woods
- Department of Physics, University of Cambridge, Cambridge CB3 0HE, U.K
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K
| | - Thomas P Fay
- Department of Chemistry, University of California, Berkeley 94720, United States
| | - David J Wales
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K
| | - David T Limmer
- Department of Chemistry, University of California, Berkeley 94720, United States
- Kavli Energy NanoSciences Institute, University of California, Berkeley 94720, United States
- Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley 94720, United States
- Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley 94720, United States
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25
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Woods EJ, Kannan D, Sharpe DJ, Swinburne TD, Wales DJ. Analysing ill-conditioned Markov chains. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2023; 381:20220245. [PMID: 37211032 DOI: 10.1098/rsta.2022.0245] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 11/15/2022] [Indexed: 05/23/2023]
Abstract
Discrete state Markov chains in discrete or continuous time are widely used to model phenomena in the social, physical and life sciences. In many cases, the model can feature a large state space, with extreme differences between the fastest and slowest transition timescales. Analysis of such ill-conditioned models is often intractable with finite precision linear algebra techniques. In this contribution, we propose a solution to this problem, namely partial graph transformation, to iteratively eliminate and renormalize states, producing a low-rank Markov chain from an ill-conditioned initial model. We show that the error induced by this procedure can be minimized by retaining both the renormalized nodes that represent metastable superbasins, and those through which reactive pathways concentrate, i.e. the dividing surface in the discrete state space. This procedure typically returns a much lower rank model, where trajectories can be efficiently generated with kinetic path sampling. We apply this approach to an ill-conditioned Markov chain for a model multi-community system, measuring the accuracy by direct comparison with trajectories and transition statistics. This article is part of a discussion meeting issue 'Supercomputing simulations of advanced materials'.
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Affiliation(s)
- Esmae J Woods
- Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge CB3 0HE, UK
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | - Deepti Kannan
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | - Daniel J Sharpe
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | - Thomas D Swinburne
- CNRS, CINaM UMR, Aix-Marseille Université, 7325, Campus de Luminy, 13288 Marseille, France
| | - David J Wales
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
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26
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Wales DJ. Energy Landscapes and Heat Capacity Signatures for Monomers and Dimers of Amyloid-Forming Hexapeptides. Int J Mol Sci 2023; 24:10613. [PMID: 37445791 DOI: 10.3390/ijms241310613] [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: 05/18/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023] Open
Abstract
Amyloid formation is a hallmark of various neurodegenerative disorders. In this contribution, energy landscapes are explored for various hexapeptides that are known to form amyloids. Heat capacity (CV) analysis at low temperature for these hexapeptides reveals that the low energy structures contributing to the first heat capacity feature above a threshold temperature exhibit a variety of backbone conformations for amyloid-forming monomers. The corresponding control sequences do not exhibit such structural polymorphism, as diagnosed via end-to-end distance and a dihedral angle defined for the monomer. A similar heat capacity analysis for dimer conformations obtained using basin-hopping global optimisation shows clear features in end-to-end distance versus dihedral correlation plots, where amyloid-forming sequences exhibit a preference for larger end-to-end distances and larger positive dihedrals. These results hold true for sequences taken from tau, amylin, insulin A chain, a de novo designed peptide, and various control sequences. While there is a little overall correlation between the aggregation propensity and the temperature at which the low-temperature CV feature occurs, further analysis suggests that the amyloid-forming sequences exhibit the key CV feature at a lower temperature compared to control sequences derived from the same protein.
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Affiliation(s)
- David J Wales
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
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27
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Takahashi S, Iuchi S, Hiraoka S, Sato H. Theoretical and computational methodologies for understanding coordination self-assembly complexes. Phys Chem Chem Phys 2023; 25:14659-14671. [PMID: 37051715 DOI: 10.1039/d3cp00082f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/14/2023]
Abstract
This perspective highlights three theoretical and computational methods to capture the coordination self-assembly processes at the molecular level: quantum chemical modeling, molecular dynamics, and reaction network analysis. These methods cover the different scales from the metal-ligand bond to a more global aspect, and approaches that are best suited to understand the coordination self-assembly from different perspectives are introduced. Theoretical and numerical researches based on these methods are not merely ways of interpreting the experimental studies but complementary to them.
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Affiliation(s)
- Satoshi Takahashi
- Department of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan.
| | - Satoru Iuchi
- Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
| | - Shuichi Hiraoka
- Department of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan.
| | - Hirofumi Sato
- Department of Molecular Engineering, Kyoto University, Nishikyo-ku, Kyoto 615-8510, Japan.
- Fukui Institute for Fundamental Chemistry, Kyoto University, Sakyo-ku, Kyoto 606-8103, Japan
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Fujita H, Ushio M, Suzuki K, Abe MS, Yamamichi M, Iwayama K, Canarini A, Hayashi I, Fukushima K, Fukuda S, Kiers ET, Toju H. Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics. MICROBIOME 2023; 11:63. [PMID: 36978146 PMCID: PMC10052866 DOI: 10.1186/s40168-023-01474-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as "dysbiosis" in human microbiomes. METHODS We integrated theoretical frameworks and empirical analyses with the aim of anticipating drastic shifts of microbial communities. We monitored 48 experimental microbiomes for 110 days and observed that various community-level events, including collapse and gradual compositional changes, occurred according to a defined set of environmental conditions. We analyzed the time-series data based on statistical physics and non-linear mechanics to describe the characteristics of the microbiome dynamics and to examine the predictability of major shifts in microbial community structure. RESULTS We confirmed that the abrupt community changes observed through the time-series could be described as shifts between "alternative stable states" or dynamics around complex attractors. Furthermore, collapses of microbiome structure were successfully anticipated by means of the diagnostic threshold defined with the "energy landscape" analysis of statistical physics or that of a stability index of nonlinear mechanics. CONCLUSIONS The results indicate that abrupt microbiome events in complex microbial communities can be forecasted by extending classic ecological concepts to the scale of species-rich microbial systems. Video Abstract.
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Affiliation(s)
- Hiroaki Fujita
- Center for Ecological Research, Kyoto University, Otsu, Shiga, 520-2133, Japan.
| | - Masayuki Ushio
- Center for Ecological Research, Kyoto University, Otsu, Shiga, 520-2133, Japan
- Department of Ocean Science, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Kenta Suzuki
- Integrated Bioresource Information Division, BioResource Research Center, RIKEN, Tsukuba, Ibaraki, 305-0074, Japan
| | - Masato S Abe
- Faculty of Culture and Information Science, Doshisha University, Kyotanabe, Kyoto, 610-0321, Japan
| | - Masato Yamamichi
- School of Biological Sciences, The University of Queensland, St. Lucia, Brisbane, QLD, 4072, Australia
- Department of International Health and Medical Anthropology, Institute of Tropical Medicine, Nagasaki University, Nagasaki, 852-8523, Japan
| | - Koji Iwayama
- Faculty of Data Science, Shiga University, Hikone, 522-8522, Japan
| | - Alberto Canarini
- Center for Ecological Research, Kyoto University, Otsu, Shiga, 520-2133, Japan
| | - Ibuki Hayashi
- Center for Ecological Research, Kyoto University, Otsu, Shiga, 520-2133, Japan
| | - Keitaro Fukushima
- Faculty of Food and Agricultural Sciences, Fukushima University, Kanayagawa 1, Fukushima, Fukushima, 960-1296, Japan
| | - Shinji Fukuda
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, 997-0052, Japan
- Gut Environmental Design Group, Kanagawa Institute of Industrial Science and Technology, Kawasaki, Kanagawa, 210-0821, Japan
- Transborder Medical Research Center, University of Tsukuba, Tsukuba, Ibaraki, 305-8575, Japan
| | - E Toby Kiers
- Department of Ecological Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Hirokazu Toju
- Center for Ecological Research, Kyoto University, Otsu, Shiga, 520-2133, Japan.
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Abstract
A significant challenge in the development of functional materials is understanding the growth and transformations of anisotropic colloidal metal nanocrystals. Theory and simulations can aid in the development and understanding of anisotropic nanocrystal syntheses. The focus of this review is on how results from first-principles calculations and classical techniques, such as Monte Carlo and molecular dynamics simulations, have been integrated into multiscale theoretical predictions useful in understanding shape-selective nanocrystal syntheses. Also, examples are discussed in which machine learning has been useful in this field. There are many areas at the frontier in condensed matter theory and simulation that are or could be beneficial in this area and these prospects for future progress are discussed.
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Affiliation(s)
- Kristen A Fichthorn
- Department of Chemical Engineering and Department of Physics The Pennsylvania State University University Park, Pennsylvania 16803 United States
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30
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Lanza G. Water model for hydrophobic cavities: structure and energy from quantum-chemical calculations. Phys Chem Chem Phys 2023; 25:6902-6913. [PMID: 36799662 DOI: 10.1039/d2cp05195h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
This ab initio study aims to design a series of large water clusters having a hollow clathrate-like cage able to host hydrophobic solutes of various sizes. Starting from the (H2O)n (n = 18, 20, 24 and 28) hollow cages, water layers have been added in a stepwise manner in order to model the configuration of water molecules beyond the primary shell. The large (H2O)100, (H2O)120 and (H2O)140 clusters complete the hydrogen bonding network of the cage with optimal and regular tiling of the do-, tetra-decahedron and hexa-decahedron, respectively. This study is corroborated by an investigation of dense water clusters up to the (H2O)123 one, being highly consistent with experimental data on ice concerning the electronic and zero-point energies for aggregate formation at 0 K and enthalpy and entropy at 273 K. The cavity creation profoundly alters the orientation of water molecules compared with those found in dense clusters. Nevertheless, such a large reorganization is necessary to maximize the water-water attraction by making it similar to the one found in dense clusters. The cage formation is an endothermic process; however, the computed values are large compared with previous reports for hydrocarbon aqueous solutions. Larger clusters are required for a more fruitful comparison.
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Affiliation(s)
- Giuseppe Lanza
- Dipartimento di Scienze del Farmaco e della Salute, Università di Catania, Viale A. Doria 6, Catania, 95125, Italy.
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31
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Choy B, Wales DJ. Molecular Energy Landscapes of Hardware-Efficient Ansätze in Quantum Computing. J Chem Theory Comput 2023; 19:1197-1206. [PMID: 36749922 PMCID: PMC9979602 DOI: 10.1021/acs.jctc.2c01057] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
Rapid advances in quantum computing have opened up new opportunities for solving the central electronic structure problem in computational chemistry. In the noisy intermediate-scale quantum (NISQ) era, where qubit coherence times are limited, it is essential to exploit quantum algorithms with sufficiently short quantum circuits to maximize qubit efficiency. The procedural construction of hardware-efficient ansätze provides one approach to design such circuits. However, refining the accuracy of the global minimum by increasing circuit depth may lead to a proliferation of local minima that hinders global optimization. To investigate this phenomenon, we explore the energy landscapes of hardware-efficient circuits to identify ground-state energies of the hydrogen, lithium hydride, and beryllium hydride molecules. We also propose a simple dimensionality reduction procedure that reduces quantum gate depth while retaining high accuracy for the global minimum, simplifying the energy landscape, and hence speeding up optimization from both software and hardware perspectives.
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Affiliation(s)
- Boy Choy
- School
of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Block N1.2, B3-13, 62 Nanyang Drive, Singapore 637459,
| | - David J. Wales
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield
Road, Cambridge CB2 1EW, United Kingdom
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32
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Qi G, Vrettas MD, Biancaniello C, Sanz-Hernandez M, Cafolla CT, Morgan JWR, Wang Y, De Simone A, Wales DJ. Enhancing Biomolecular Simulations with Hybrid Potentials Incorporating NMR Data. J Chem Theory Comput 2022; 18:7733-7750. [PMID: 36395419 DOI: 10.1021/acs.jctc.2c00657] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Some recent advances in biomolecular simulation and global optimization have used hybrid restraint potentials, where harmonic restraints that penalize conformations inconsistent with experimental data are combined with molecular mechanics force fields. These hybrid potentials can be used to improve the performance of molecular dynamics, structure prediction, energy landscape sampling, and other computational methods that rely on the accuracy of the underlying force field. Here, we develop a hybrid restraint potential based on NapShift, an artificial neural network trained to predict protein nuclear magnetic resonance (NMR) chemical shifts from sequence and structure. In addition to providing accurate predictions of experimental chemical shifts, NapShift is fully differentiable with respect to atomic coordinates, which allows us to use it for structural refinement. By employing NapShift to predict chemical shifts from the protein conformation at each simulation step, we can compute an energy penalty and the corresponding hybrid restraint forces based on the difference between the predicted values and the experimental chemical shifts. The performance of the hybrid restraint potential was benchmarked using both basin-hopping global optimization and molecular dynamics simulations. In each case, the NapShift hybrid potential improved the accuracy, leading to better structure prediction via basin-hopping and increased local stability in molecular dynamics simulations. Our results suggest that neural network hybrid potentials based on NMR observables can enhance a broad range of molecular simulation methods, and the prediction accuracy will improve as more experimental training data become available.
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Affiliation(s)
- Guowei Qi
- Department of Chemistry, University of Cambridge, Lensfield Road, CambridgeCB2 1EW, U.K
| | - Michail D Vrettas
- Department of Pharmacy, University of Naples Federico II, 80131Naples, Italy
| | - Carmen Biancaniello
- Department of Pharmacy, University of Naples Federico II, 80131Naples, Italy
| | - Maximo Sanz-Hernandez
- Department of Life Sciences, Imperial College London, South Kensington, LondonSW7 2AZ, U.K
| | - Conor T Cafolla
- Department of Chemistry, University of Cambridge, Lensfield Road, CambridgeCB2 1EW, U.K
| | - John W R Morgan
- Department of Chemistry, University of Cambridge, Lensfield Road, CambridgeCB2 1EW, U.K
| | - Yifei Wang
- Department of Chemistry, University of Cambridge, Lensfield Road, CambridgeCB2 1EW, U.K
| | - Alfonso De Simone
- Department of Pharmacy, University of Naples Federico II, 80131Naples, Italy
| | - David J Wales
- Department of Chemistry, University of Cambridge, Lensfield Road, CambridgeCB2 1EW, U.K
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Horvath I, Wales DJ, Fejer SN. Design of self-assembling mesoscopic Goldberg polyhedra. NANOSCALE ADVANCES 2022; 4:4272-4278. [PMID: 36321154 PMCID: PMC9552754 DOI: 10.1039/d2na00447j] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 08/15/2022] [Indexed: 06/16/2023]
Abstract
Palladium ions complexed with nonlinear bidentate ligands have been shown to form hollow, spherical shells with high symmetries. We show that such structures can be reproduced using model anisotropic mesoscale building blocks featuring excluded volume and long-range ionic interactions. A linear building block with a central charged particle, in combination with a bent 'ligand' particle with opposite charges at the ends is sufficient to drive the system towards planar coordination, and the charge ratio determines the coordination number. Similar to the molecular systems, the bend in the 'ligand' particle determines the curvature of the shells that these building blocks prefer. Besides reproducing exotic structures such as M30L60 and M48L96 tetravalent Goldberg polyhedra, we identify highly cooperative single transition state rearrangements between low-energy competing structures as well, corresponding to rotatory motions of a planar subunit within the spherical shell.
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Affiliation(s)
- Istvan Horvath
- Provitam Foundation Caisului Street 16 Cluj-Napoca Romania
- University of Pécs, Institute of Chemistry 6 Ifjúság Street Pécs Hungary
| | - David J Wales
- Department of Chemistry, University of Cambridge Lensfield Road Cambridge CB2 1EW UK
| | - Szilard N Fejer
- Provitam Foundation Caisului Street 16 Cluj-Napoca Romania
- University of Pécs, Institute of Chemistry 6 Ifjúság Street Pécs Hungary
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Valério M, Borges-Araújo L, Melo MN, Lousa D, Soares CM. SARS-CoV-2 variants impact RBD conformational dynamics and ACE2 accessibility. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 4:1009451. [PMID: 36277437 PMCID: PMC9581196 DOI: 10.3389/fmedt.2022.1009451] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 09/20/2022] [Indexed: 02/05/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has killed over 6 million people and is having a devastating social and economic impact around the world. The rise of new variants of concern (VOCs) represents a difficult challenge due to the loss of vaccine and natural immunity, as well as increased transmissibility. All VOCs contain mutations in the spike glycoprotein, which mediates fusion between the viral and host cell membranes. The spike glycoprotein binds to angiotensin-converting enzyme 2 (ACE2) via its receptor binding domain (RBD) initiating the infection process. Attempting to understand the effect of RBD mutations in VOCs, a lot of attention has been given to the RBD-ACE2 interaction. However, this type of analysis ignores more indirect effects, such as the conformational dynamics of the RBD itself. Observing that some mutations occur in residues that are not in direct contact with ACE2, we hypothesized that they could affect the RBD conformational dynamics. To test this, we performed long atomistic (AA) molecular dynamics (MD) simulations to investigate the structural dynamics of wt RBD, and that of four VOCs (Alpha, Beta, Delta, and Omicron). Our results show that the wt RBD presents two distinct conformations: an "open" conformation where it is free to bind ACE2; and a "closed" conformation, where the RBM ridge blocks the binding surface. The Alpha and Beta variants shift the open/closed equilibrium towards the open conformation by roughly 20%, likely increasing ACE2 binding affinity. Simulations of the Delta and Omicron variants showed extreme results, with the closed conformation being rarely observed. The Delta variant also differed substantially from the other variants, alternating between the open conformation and an alternative "reversed" one, with a significantly changed orientation of the RBM ridge. This alternate conformation could provide a fitness advantage due to increased availability for ACE2 binding, and by aiding antibody escape through epitope occlusion. These results support the hypothesis that VOCs, and particularly the Omicron and Delta variants, impact RBD conformational dynamics in a direction that promotes efficient binding to ACE2 and, in the case of Delta, may assist antibody escape.
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Affiliation(s)
- Mariana Valério
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
- Associated Laboratory LS4FUTURE, ITQB NOVA, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Luís Borges-Araújo
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
- Associated Laboratory LS4FUTURE, ITQB NOVA, Universidade Nova de Lisboa, Oeiras, Portugal
- iBB-Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
- Associate Laboratory i4HB—Institute for Health and Bioeconomy at Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Manuel N. Melo
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
- Associated Laboratory LS4FUTURE, ITQB NOVA, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Diana Lousa
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
- Associated Laboratory LS4FUTURE, ITQB NOVA, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Cláudio M. Soares
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
- Associated Laboratory LS4FUTURE, ITQB NOVA, Universidade Nova de Lisboa, Oeiras, Portugal
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Colberg M, Schofield J. Configurational entropy, transition rates, and optimal interactions for rapid folding in coarse-grained model proteins. J Chem Phys 2022; 157:125101. [PMID: 36182418 DOI: 10.1063/5.0098612] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Under certain conditions, the dynamics of coarse-grained models of solvated proteins can be described using a Markov state model, which tracks the evolution of populations of configurations. The transition rates among states that appear in the Markov model can be determined by computing the relative entropy of states and their mean first passage times. In this paper, we present an adaptive method to evaluate the configurational entropy and the mean first passage times for linear chain models with discontinuous potentials. The approach is based on event-driven dynamical sampling in a massively parallel architecture. Using the fact that the transition rate matrix can be calculated for any choice of interaction energies at any temperature, it is demonstrated how each state's energy can be chosen such that the average time to transition between any two states is minimized. The methods are used to analyze the optimization of the folding process of two protein systems: the crambin protein and a model with frustration and misfolding. It is shown that the folding pathways for both systems are comprised of two regimes: first, the rapid establishment of local bonds, followed by the subsequent formation of more distant contacts. The state energies that lead to the most rapid folding encourage multiple pathways, and they either penalize folding pathways through kinetic traps by raising the energies of trapping states or establish an escape route from the trapping states by lowering free energy barriers to other states that rapidly reach the native state.
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Affiliation(s)
- Margarita Colberg
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Jeremy Schofield
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
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36
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On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations. Antibodies (Basel) 2022; 11:antib11030051. [PMID: 35997345 PMCID: PMC9397028 DOI: 10.3390/antib11030051] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/23/2022] [Accepted: 08/01/2022] [Indexed: 02/05/2023] Open
Abstract
The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process.
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Schäffler M, Khaled M, Strodel B. ATRANET – Automated generation of transition networks for the structural characterization of intrinsically disordered proteins. Methods 2022; 206:18-26. [DOI: 10.1016/j.ymeth.2022.07.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 07/19/2022] [Accepted: 07/20/2022] [Indexed: 10/16/2022] Open
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38
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Global analysis of the energy landscapes of molecular crystal structures by applying the threshold algorithm. Commun Chem 2022; 5:86. [PMID: 36697680 PMCID: PMC9814927 DOI: 10.1038/s42004-022-00705-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 07/15/2022] [Indexed: 01/28/2023] Open
Abstract
Polymorphism in molecular crystals has important consequences for the control of materials properties and our understanding of crystallization. Computational methods, including crystal structure prediction, have provided important insight into polymorphism, but have usually been limited to assessing the relative energies of structures. We describe the implementation of the Monte Carlo threshold algorithm as a method to provide an estimate of the energy barriers separating crystal structures. By sampling the local energy minima accessible from multiple starting structures, the simulations yield a global picture of the crystal energy landscapes and provide valuable information on the depth of the energy minima associated with crystal structures. We present results from applying the threshold algorithm to four polymorphic organic molecular crystals, examine the influence of applying space group symmetry constraints during the simulations, and discuss the relationship between the structure of the energy landscape and the intermolecular interactions present in the crystals.
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Abstract
Multifunctional systems, such as molecular switches, exhibit multifunnel energy landscapes associated with the alternative functional states. In this contribution the multifunnel organization is decoded from dynamical signatures in the first passage time distribution between reactants and products. Characteristic relaxation rates are revealed by analyzing the kinetics as a function of the observation time scale, which scans the underlying distribution. Extracting the corresponding dynamical signatures provides direct insight into the organization of the molecular energy landscape, which will facilitate a rational design of target functionality. Examples are illustrated for multifunnel landscapes in biomolecular systems and an atomic cluster.
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40
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Zhuang Y, Thota N, Quirk S, Hernandez R. Implementation of Telescoping Boxes in Adaptive Steered Molecular Dynamics. J Chem Theory Comput 2022; 18:4649-4659. [PMID: 35830368 DOI: 10.1021/acs.jctc.2c00498] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Long-time dynamical processes, such as those involving protein unfolding and ligand interactions, can be accelerated and realized through steered molecular dynamics (SMD). The challenge has been the extraction of information from such simulations that generalize for complex nonequilibrium processes. The use of Jarzynski's equality opened the possibility of determining the free energy along the steered coordinate, but sampling over the nonequilibrium trajectories is slow to converge. Adaptive steered molecular dynamics (ASMD) and other related techniques have been introduced to overcome this challenge through the use of stages. Here, we take advantage of these stages to address the numerical cost that arises from the required use of very large solvent boxes. We introduce telescoping box schemes within adaptive steered molecular dynamics (ASMD) in which we adjust the solvent box between stages and thereby vary (and optimize) the required number of solvent molecules. We have benchmarked the method on a relatively long α-helical peptide, Ala30, with respect to the potential of mean force and hydrogen bonds. We show that the use of telescoping boxes introduces little numerical error while significantly reducing the computational cost.
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Affiliation(s)
- Yi Zhuang
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Nikhil Thota
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Stephen Quirk
- Kimberly-Clark Corporation, Atlanta, Georgia 30076-2199, United States
| | - Rigoberto Hernandez
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States.,Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States.,Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
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41
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Dang NL, Baranger AM, Beveridge DL. High Energy Channeling and Malleable Transition States: Molecular Dynamics Simulations and Free Energy Landscapes for the Thermal Unfolding of Protein U1A and 13 Mutants. Biomolecules 2022; 12:940. [PMID: 35883496 PMCID: PMC9312810 DOI: 10.3390/biom12070940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/20/2022] [Accepted: 06/27/2022] [Indexed: 11/20/2022] Open
Abstract
The spliceosome protein U1A is a prototype case of the RNA recognition motif (RRM) ubiquitous in biological systems. The in vitro kinetics of the chemical denaturation of U1A indicate that the unfolding of U1A is a two-state process but takes place via high energy channeling and a malleable transition state, an interesting variation of typical two-state behavior. Molecular dynamics (MD) simulations have been applied extensively to the study of two-state unfolding and folding of proteins and provide an opportunity to obtain a theoretical account of the experimental results and a molecular model for the transition state ensemble. We describe herein all-atom MD studies including explicit solvent of up to 100 ns on the thermal unfolding (UF) of U1A and 13 mutants. Multiple MD UF trajectories are carried out to ensure accuracy and reproducibility. A vector representation of the MD unfolding process in RMSD space is obtained and used to calculate a free energy landscape for U1A unfolding. A corresponding MD simulation and free energy landscape for the protein CI2, well known to follow a simple two state folding/unfolding model, is provided as a control. The results indicate that the unfolding pathway on the MD calculated free energy landscape of U1A shows a markedly extended transition state compared with that of CI2. The MD results support the interpretation of the observed chevron plots for U1A in terms of a high energy, channel-like transition state. Analysis of the MDUF structures shows that the transition state ensemble involves microstates with most of the RRM secondary structure intact but expanded by ~14% with respect to the radius of gyration. Comparison with results on a prototype system indicates that the transition state involves an ensemble of molten globule structures and extends over the region of ~1-35 ns in the trajectories. Additional MDUF simulations were carried out for 13 U1A mutants, and the calculated φ-values show close accord with observed results and serve to validate our methodology.
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Affiliation(s)
| | | | - David L. Beveridge
- Department of Chemistry and Molecular Biophysics Program, Wesleyan University, Middletown, CT 06459, USA; (N.L.D.); (A.M.B.)
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Kamberaj H. Random walks in a free energy landscape combining augmented molecular dynamics simulations with a dynamic graph neural network model. J Mol Graph Model 2022; 114:108199. [DOI: 10.1016/j.jmgm.2022.108199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 04/09/2022] [Accepted: 04/11/2022] [Indexed: 10/18/2022]
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Taghavi A, Riveros I, Wales DJ, Yildirim I. Evaluating Geometric Definitions of Stacking for RNA Dinucleoside Monophosphates Using Molecular Mechanics Calculations. J Chem Theory Comput 2022; 18:3637-3653. [PMID: 35652685 DOI: 10.1021/acs.jctc.2c00178] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
RNA modulation via small molecules is a novel approach in pharmacotherapies, where the determination of the structural properties of RNA motifs is considered a promising way to develop drugs capable of targeting RNA structures to control diseases. However, due to the complexity and dynamic nature of RNA molecules, the determination of RNA structures using experimental approaches is not always feasible, and computational models employing force fields can provide important insight. The quality of the force field will determine how well the predictions are compared to experimental observables. Stacking in nucleic acids is one such structural property, originating mainly from London dispersion forces, which are quantum mechanical and are included in molecular mechanics force fields through nonbonded interactions. Geometric descriptions are utilized to decide if two residues are stacked and hence to calculate the stacking free energies for RNA dinucleoside monophosphates (DNMPs) through statistical mechanics for comparison with experimental thermodynamics data. Here, we benchmark four different stacking definitions using molecular dynamics (MD) trajectories for 16 RNA DNMPs produced by two different force fields (RNA-IL and ff99OL3) and show that our stacking definition better correlates with the experimental thermodynamics data. While predictions within an accuracy of 0.2 kcal/mol at 300 K were observed in RNA CC, CU, UC, AG, GA, and GG, stacked states of purine-pyrimidine and pyrimidine-purine DNMPs, respectively, were typically underpredicted and overpredicted. Additionally, population distributions of RNA UU DNMPs were poorly predicted by both force fields, implying a requirement for further force field revisions. We further discuss the differences predicted by each RNA force field. Finally, we show that discrete path sampling (DPS) calculations can provide valuable information and complement the MD simulations. We propose the use of experimental thermodynamics data for RNA DNMPs as benchmarks for testing RNA force fields.
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Affiliation(s)
- Amirhossein Taghavi
- Department of Chemistry and Biochemistry, Florida Atlantic University, Jupiter, Florida 33458, United States.,Department of Chemistry, Scripps Research Institute Florida, Jupiter, Florida 33458, United States
| | - Ivan Riveros
- Department of Chemistry and Biochemistry, Florida Atlantic University, Jupiter, Florida 33458, United States
| | - David J Wales
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K
| | - Ilyas Yildirim
- Department of Chemistry and Biochemistry, Florida Atlantic University, Jupiter, Florida 33458, United States
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44
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Röder K, Barker AM, Whitehouse A, Pasquali S. Investigating the structural changes due to adenosine methylation of the Kaposi’s sarcoma-associated herpes virus ORF50 transcript. PLoS Comput Biol 2022; 18:e1010150. [PMID: 35617364 PMCID: PMC9176763 DOI: 10.1371/journal.pcbi.1010150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 06/08/2022] [Accepted: 04/28/2022] [Indexed: 11/20/2022] Open
Abstract
Kaposi’s sarcoma-associated herpes virus (KSHV) is a human oncovirus. KSHV relies on manipulating the host cell N6-methyl adenosine (m6A) RNA modification pathway to enhance virus replication. Methylation within a RNA stem loop of the open reading frame 50 (ORF50) increases transcript stability via the recruitment of the m6A reader, SND1. In this contribution we explore the energy landscapes of the unmethylated and methylated RNA stem loops of ORF50 to investigate the effect of methylation on the structure of the stem loop. We observe a significant shift upon methylation between an open and closed configuration of the top of the stem loop. In the unmethylated stem loop the closed configuration is much lower in energy, and, as a result, exhibits higher occupancy. In this article we present the investigation of the change in structure of an RNA regulatory molecule upon a change in the chemistry of one of its bases. Eukaryotic RNAs contain more than 100 different types of chemical modifications, which can fine-tune the structure and function of RNA. Since RNA systems need to adopt a specific 3D shape to be functional, it is important to understand how a chemical modification impacts the structure adopted. Using the computational technique of energy landscape explorations, that is exploring what structures are available to the system at a given energy, we are able to characterise the RNA before and after the modification, and understand what the main differences between the ensembles of structures, which can be adopted by the system, are. In this work, we present our results of this investigation on an oncogenic virus-encoded RNA. We show how a chemical modification at a precise location of the native structure affects the system globally, inducing a rearrangement of parts of the structure, which are far away from the modification site.
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Affiliation(s)
- Konstantin Röder
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
- * E-mail: (KR); (SP)
| | - Amy M. Barker
- School of Molecular and Cellular Biology and Astbury Centre of Structural Biology, University of Leeds, Leeds, United Kingdom
| | - Adrian Whitehouse
- School of Molecular and Cellular Biology and Astbury Centre of Structural Biology, University of Leeds, Leeds, United Kingdom
| | - Samuela Pasquali
- Laboratoire CiTCoM, UMR 8038 CNRS, and Laboratoire BFA, UMR 8251 CNRS, Université de Paris, Paris, France
- * E-mail: (KR); (SP)
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45
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Bauer MN, Probert MIJ, Panosetti C. Systematic Comparison of Genetic Algorithm and Basin Hopping Approaches to the Global Optimization of Si(111) Surface Reconstructions. J Phys Chem A 2022; 126:3043-3056. [PMID: 35522778 PMCID: PMC9126620 DOI: 10.1021/acs.jpca.2c00647] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
![]()
We present a systematic
study of two widely used material structure
prediction methods, the Genetic Algorithm and Basin Hopping approaches
to global optimization, in a search for the 3 × 3, 5 × 5,
and 7 × 7 reconstructions of the Si(111) surface. The Si(111)
7 × 7 reconstruction is the largest and most complex surface
reconstruction known, and finding it is a very exacting test for global
optimization methods. In this paper, we introduce a modification to
previous Genetic Algorithm work on structure search for periodic systems,
to allow the efficient search for surface reconstructions, and present
a rigorous study of the effect of the different parameters of the
algorithm. We also perform a detailed comparison with the recently
improved Basin Hopping algorithm using Delocalized Internal Coordinates.
Both algorithms succeeded in either resolving the 3 × 3, 5 ×
5, and 7 × 7 DAS surface reconstructions or getting “sufficiently
close”, i.e., identifying structures that only differ for the
positions of a few atoms as well as thermally accessible structures
within kBT/unit area
of the global minimum, with T = 300 K. Overall, the
Genetic Algorithm is more robust with respect to parameter choice
and in success rate, while the Basin Hopping method occasionally exhibits
some advantages in speed of convergence. In line with previous studies,
the results confirm that robustness, success, and speed of convergence
of either approach are strongly influenced by how much the trial moves
tend to preserve favorable bonding patterns once these appear.
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Affiliation(s)
- Maximilian N Bauer
- Department of Physics, University of York, York YO10 5DD, United Kingdom.,Technical University of Munich, Lichtenbergstraße 4, 85748 Garching, Germany
| | - Matt I J Probert
- Department of Physics, University of York, York YO10 5DD, United Kingdom
| | - Chiara Panosetti
- Technical University of Munich, Lichtenbergstraße 4, 85748 Garching, Germany.,Fritz Haber Institute of the Max Planck Society, Faradayweg 4, 14195 Berlin, Germany
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46
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Nicy, Chakraborty D, Wales DJ. Energy Landscapes for Base-Flipping in a Model DNA Duplex. J Phys Chem B 2022; 126:3012-3028. [PMID: 35427136 PMCID: PMC9098180 DOI: 10.1021/acs.jpcb.2c00340] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 03/24/2022] [Indexed: 12/31/2022]
Abstract
We explore the process of base-flipping for four central bases, adenine, guanine, cytosine, and thymine, in a deoxyribonucleic acid (DNA) duplex using the energy landscape perspective. NMR imino-proton exchange and fluorescence correlation spectroscopy studies have been used in previous experiments to obtain lifetimes for bases in paired and extrahelical states. However, the difference of almost 4 orders of magnitude in the base-flipping rates obtained by the two methods implies that they are exploring different pathways and possibly different open states. Our results support the previous suggestion that minor groove opening may be favored by distortions in the DNA backbone and reveal links between sequence effects and the direction of opening, i.e., whether the base flips toward the major or the minor groove side. In particular, base flipping along the minor groove pathway was found to align toward the 5' side of the backbone. We find that bases align toward the 3' side of the backbone when flipping along the major groove pathway. However, in some cases for cytosine and thymine, the base flipping along the major groove pathway also aligns toward the 5' side. The sequence effect may be caused by the polar interactions between the flipping-base and its neighboring bases on either of the strands. For guanine flipping toward the minor groove side, we find that the equilibrium constant for opening is large compared to flipping via the major groove. We find that the estimated rates of base opening, and hence the lifetimes of the closed state, obtained for thymine flipping through small and large angles along the major groove differ by 6 orders of magnitude, whereas for thymine flipping through small angles along the minor groove and large angles along the major groove, the rates differ by 3 orders of magnitude.
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Affiliation(s)
- Nicy
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge, CB2 1EW, U.K.
| | - Debayan Chakraborty
- Department
of Chemistry, The University of Texas at
Austin, Austin, Texas 78712, United States
| | - David J. Wales
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge, CB2 1EW, U.K.
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47
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Abstract
We calculate transformation pathways between fullerene and octahedral carbon clusters and between a buckyball and its bowl-shaped isomer. The energies and gradients are provided by efficient tight-binding potentials, which are interfaced to our Energy Landscape exploration software. From the global energy landscape, we extract the mechanistic and kinetic parameters as a function of temperature, and compare our results to selected density functional theory (DFT) (PBE/cc-pVTZ) benchmarks. Infrared spectra are calculated to provide data for experimental identification of the clusters and differentiation of their isomers. Our results suggest that the formation of buckyballs from a buckybowl will be suppressed at elevated temperatures (above around 5250 K) due to entropic effects, which may provide useful insight into the detection of cosmic fullerenes.
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48
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Tsutsumi T, Ono Y, Taketsugu T. Reaction Space Projector (ReSPer) for Visualizing Dynamic Reaction Routes Based on Reduced-Dimension Space. Top Curr Chem (Cham) 2022; 380:19. [PMID: 35266073 DOI: 10.1007/s41061-022-00377-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 02/21/2022] [Indexed: 11/26/2022]
Abstract
To analyze chemical reaction dynamics based on a reaction path network, we have developed the "Reaction Space Projector" (ReSPer) method with the aid of the dimensionality reduction method. This program has two functions: the construction of a reduced-dimensionality reaction space from a molecular structure dataset, and the projection of dynamic trajectories into the low-dimensional reaction space. In this paper, we apply ReSPer to isomerization and bifurcation reactions of the Au5 cluster and succeed in analyzing dynamic reaction routes involved in multiple elementary reaction processes, constructing complicated networks (called "closed islands") of nuclear permutation-inversion (NPI) isomerization reactions, and elucidating dynamic behaviors in bifurcation reactions with reference to bundles of trajectories. Interestingly, in the second application, we find a correspondence between the contribution ratios in the ability to visualize and the symmetry of the morphology of closed islands. In addition, the third application suggests the existence of boundaries that determine the selectivity in bifurcation reactions, which was discussed in the phase space. The ReSPer program is a versatile and robust tool to clarify dynamic reaction mechanisms based on the reduced-dimensionality reaction space without prior knowledge of target reactions.
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Affiliation(s)
- Takuro Tsutsumi
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo, 060-0810, Japan
| | - Yuriko Ono
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, 001-0021, Japan
| | - Tetsuya Taketsugu
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo, 060-0810, Japan.
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, 001-0021, Japan.
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49
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Dicks L, Wales DJ. Elucidating the solution structure of the K-means cost function using energy landscape theory. J Chem Phys 2022; 156:054109. [DOI: 10.1063/5.0078793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- L. Dicks
- University Chemical Laboratories, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - D. J. Wales
- University Chemical Laboratories, Lensfield Road, Cambridge CB2 1EW, United Kingdom
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50
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Röder K, Wales DJ. The Energy Landscape Perspective: Encoding Structure and Function for Biomolecules. Front Mol Biosci 2022; 9:820792. [PMID: 35155579 PMCID: PMC8829389 DOI: 10.3389/fmolb.2022.820792] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 01/07/2022] [Indexed: 12/02/2022] Open
Abstract
The energy landscape perspective is outlined with particular reference to biomolecules that perform multiple functions. We associate these multifunctional molecules with multifunnel energy landscapes, illustrated by some selected examples, where understanding the organisation of the landscape has provided new insight into function. Conformational selection and induced fit may provide alternative routes to realisation of multifunctionality, exploiting the possibility of environmental control and distinct binding modes.
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Affiliation(s)
| | - David J. Wales
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
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