1
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Zhang J, Zhang O, Bonati L, Hou T. Combining Transition Path Sampling with Data-Driven Collective Variables through a Reactivity-Biased Shooting Algorithm. J Chem Theory Comput 2024; 20:4523-4532. [PMID: 38801759 DOI: 10.1021/acs.jctc.4c00423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Rare event sampling is a central problem in modern computational chemistry research. Among the existing methods, transition path sampling (TPS) can generate unbiased representations of reaction processes. However, its efficiency depends on the ability to generate reactive trial paths, which in turn depends on the quality of the shooting algorithm used. We propose a new algorithm based on the shooting success rate, i.e., reactivity, measured as a function of a reduced set of collective variables (CVs). These variables are extracted with a machine learning approach directly from TPS simulations, using a multitask objective function. Iteratively, this workflow significantly improves the shooting efficiency without any prior knowledge of the process. In addition, the optimized CVs can be used with biased enhanced sampling methodologies to accurately reconstruct the free energy profiles. We tested the method on three different systems: a two-dimensional toy model, conformational transitions of alanine dipeptide, and hydrolysis of acetyl chloride in bulk water. In the latter, we integrated our workflow with an active learning scheme to learn a reactive machine learning-based potential, which allowed us to study the mechanism and free energy profile with an ab initio-like accuracy.
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Affiliation(s)
- Jintu Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
- Atomistic Simulations, Italian Institute of Technology, Genova 16152, Italy
| | - Odin Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Luigi Bonati
- Atomistic Simulations, Italian Institute of Technology, Genova 16152, Italy
| | - TingJun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang 310058, China
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2
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Fu H, Bian H, Shao X, Cai W. Collective Variable-Based Enhanced Sampling: From Human Learning to Machine Learning. J Phys Chem Lett 2024; 15:1774-1783. [PMID: 38329095 DOI: 10.1021/acs.jpclett.3c03542] [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: 02/09/2024]
Abstract
Enhanced-sampling algorithms relying on collective variables (CVs) are extensively employed to study complex (bio)chemical processes that are not amenable to brute-force molecular simulations. The selection of appropriate CVs characterizing the slow movement modes is of paramount importance for reliable and efficient enhanced-sampling simulations. In this Perspective, we first review the application and limitations of CVs obtained from chemical and geometrical intuition. We also introduce path-sampling algorithms, which can identify path-like CVs in a high-dimensional free-energy space. Machine-learning algorithms offer a viable approach to finding suitable CVs by analyzing trajectories from preliminary simulations. We discuss both the performance of machine-learning-derived CVs in enhanced-sampling simulations of experimental models and the challenges involved in applying these CVs to realistic, complex molecular assemblies. Moreover, we provide a prospective view of the potential advancements of machine-learning algorithms for the development of CVs in the field of enhanced-sampling simulations.
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Affiliation(s)
- Haohao Fu
- Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Hengwei Bian
- Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Xueguang Shao
- Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Wensheng Cai
- Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
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3
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Hulm A, Ochsenfeld C. Improved Sampling of Adaptive Path Collective Variables by Stabilized Extended-System Dynamics. J Chem Theory Comput 2023; 19:9202-9210. [PMID: 38078670 PMCID: PMC10753802 DOI: 10.1021/acs.jctc.3c00938] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 11/08/2023] [Accepted: 11/08/2023] [Indexed: 12/27/2023]
Abstract
Because of the complicated multistep nature of many biocatalytic reactions, an a priori definition of reaction coordinates is difficult. Therefore, we apply enhanced sampling algorithms along with adaptive path collective variables (PCVs), which converge to the minimum free energy path (MFEP) during the simulation. We show how PCVs can be combined with the highly efficient well-tempered metadynamics extended-system adaptive biasing force (WTM-eABF) hybrid sampling algorithm, offering dramatically increased sampling efficiency due to its fast adaptation to path updates. For this purpose, we address discontinuities of PCVs that can arise due to path shortcutting or path updates with a novel stabilization algorithm for extended-system methods. In addition, we show how the convergence of simulations can be further accelerated by utilizing the multistate Bennett's acceptance ratio (MBAR) estimator. These methods are applied to the first step of the enzymatic reaction mechanism of pseudouridine synthases, where the ability of path WTM-eABF to efficiently explore intricate molecular transitions is demonstrated.
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Affiliation(s)
- Andreas Hulm
- Chair
of Theoretical Chemistry, Department of Chemistry, LMU Munich, Butenandtstr. 5, München D-81377, Germany
| | - Christian Ochsenfeld
- Chair
of Theoretical Chemistry, Department of Chemistry, LMU Munich, Butenandtstr. 5, München D-81377, Germany
- Max
Planck Institute for Solid State Research, Heisenbergstr. 1, Stuttgart D-70569, Germany
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4
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Mouaffac L, Palacio-Rodriguez K, Pietrucci F. Optimal Reaction Coordinates and Kinetic Rates from the Projected Dynamics of Transition Paths. J Chem Theory Comput 2023; 19:5701-5711. [PMID: 37550088 DOI: 10.1021/acs.jctc.3c00158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
Finding optimal reaction coordinates and predicting accurate kinetic rates for activated processes are two of the foremost challenges of molecular simulations. We introduce an algorithm that tackles the two problems at once: starting from a limited number of reactive molecular dynamics trajectories (transition paths), we automatically generate with a Monte Carlo approach a sequence of different reaction coordinates that progressively reduce the kinetic rate of their projected effective dynamics. Based on a variational principle, the minimal rate accurately approximates the exact one, and it corresponds to the optimal reaction coordinate. After benchmarking the method on an analytic double-well system, we apply it to complex atomistic systems: the interaction of carbon nanoparticles of different sizes in water.
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Affiliation(s)
- Line Mouaffac
- Sorbonne Université, Muséum National d'Histoire Naturelle, UMR CNRS 7590, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, F-75005 Paris, France
| | - Karen Palacio-Rodriguez
- Sorbonne Université, Muséum National d'Histoire Naturelle, UMR CNRS 7590, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, F-75005 Paris, France
| | - Fabio Pietrucci
- Sorbonne Université, Muséum National d'Histoire Naturelle, UMR CNRS 7590, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, F-75005 Paris, France
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5
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Van Speybroeck V, Bocus M, Cnudde P, Vanduyfhuys L. Operando Modeling of Zeolite-Catalyzed Reactions Using First-Principles Molecular Dynamics Simulations. ACS Catal 2023; 13:11455-11493. [PMID: 37671178 PMCID: PMC10476167 DOI: 10.1021/acscatal.3c01945] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 07/27/2023] [Indexed: 09/07/2023]
Abstract
Within this Perspective, we critically reflect on the role of first-principles molecular dynamics (MD) simulations in unraveling the catalytic function within zeolites under operating conditions. First-principles MD simulations refer to methods where the dynamics of the nuclei is followed in time by integrating the Newtonian equations of motion on a potential energy surface that is determined by solving the quantum-mechanical many-body problem for the electrons. Catalytic solids used in industrial applications show an intriguing high degree of complexity, with phenomena taking place at a broad range of length and time scales. Additionally, the state and function of a catalyst critically depend on the operating conditions, such as temperature, moisture, presence of water, etc. Herein we show by means of a series of exemplary cases how first-principles MD simulations are instrumental to unravel the catalyst complexity at the molecular scale. Examples show how the nature of reactive species at higher catalytic temperatures may drastically change compared to species at lower temperatures and how the nature of active sites may dynamically change upon exposure to water. To simulate rare events, first-principles MD simulations need to be used in combination with enhanced sampling techniques to efficiently sample low-probability regions of phase space. Using these techniques, it is shown how competitive pathways at operating conditions can be discovered and how broad transition state regions can be explored. Interestingly, such simulations can also be used to study hindered diffusion under operating conditions. The cases shown clearly illustrate how first-principles MD simulations reveal insights into the catalytic function at operating conditions, which could not be discovered using static or local approaches where only a few points are considered on the potential energy surface (PES). Despite these advantages, some major hurdles still exist to fully integrate first-principles MD methods in a standard computational catalytic workflow or to use the output of MD simulations as input for multiple length/time scale methods that aim to bridge to the reactor scale. First of all, methods are needed that allow us to evaluate the interatomic forces with quantum-mechanical accuracy, albeit at a much lower computational cost compared to currently used density functional theory (DFT) methods. The use of DFT limits the currently attainable length/time scales to hundreds of picoseconds and a few nanometers, which are much smaller than realistic catalyst particle dimensions and time scales encountered in the catalysis process. One solution could be to construct machine learning potentials (MLPs), where a numerical potential is derived from underlying quantum-mechanical data, which could be used in subsequent MD simulations. As such, much longer length and time scales could be reached; however, quite some research is still necessary to construct MLPs for the complex systems encountered in industrially used catalysts. Second, most currently used enhanced sampling techniques in catalysis make use of collective variables (CVs), which are mostly determined based on chemical intuition. To explore complex reactive networks with MD simulations, methods are needed that allow the automatic discovery of CVs or methods that do not rely on a priori definition of CVs. Recently, various data-driven methods have been proposed, which could be explored for complex catalytic systems. Lastly, first-principles MD methods are currently mostly used to investigate local reactive events. We hope that with the rise of data-driven methods and more efficient methods to describe the PES, first-principles MD methods will in the future also be able to describe longer length/time scale processes in catalysis. This might lead to a consistent dynamic description of all steps-diffusion, adsorption, and reaction-as they take place at the catalyst particle level.
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Affiliation(s)
| | - Massimo Bocus
- Center for Molecular Modeling, Ghent University, Technologiepark 46, 9052 Zwijnaarde, Belgium
| | - Pieter Cnudde
- Center for Molecular Modeling, Ghent University, Technologiepark 46, 9052 Zwijnaarde, Belgium
| | - Louis Vanduyfhuys
- Center for Molecular Modeling, Ghent University, Technologiepark 46, 9052 Zwijnaarde, Belgium
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6
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Magrino T, Huet L, Saitta AM, Pietrucci F. Critical Assessment of Data-Driven versus Heuristic Reaction Coordinates in Solution Chemistry. J Phys Chem A 2022; 126:8887-8900. [DOI: 10.1021/acs.jpca.2c07640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Théo Magrino
- Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Sorbonne Université, Muséum National d’Histoire Naturelle, CNRS UMR 7590, Paris 75005, France
| | - Léon Huet
- Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Sorbonne Université, Muséum National d’Histoire Naturelle, CNRS UMR 7590, Paris 75005, France
| | - A. Marco Saitta
- Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Sorbonne Université, Muséum National d’Histoire Naturelle, CNRS UMR 7590, Paris 75005, France
| | - Fabio Pietrucci
- Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Sorbonne Université, Muséum National d’Histoire Naturelle, CNRS UMR 7590, Paris 75005, France
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7
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Bocus M, Van Speybroeck V. Insights into the Mechanism and Reactivity of Zeolite-Catalyzed Alkylphenol Dealkylation. ACS Catal 2022. [DOI: 10.1021/acscatal.2c03844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Massimo Bocus
- Center for Molecular Modeling, Ghent University, Technologiepark 46, 9052Zwijnaarde, Belgium
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8
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Pérez de Alba Ortíz A, Vreede J, Ensing B. Sequence dependence of transient Hoogsteen base pairing in DNA. PLoS Comput Biol 2022; 18:e1010113. [PMID: 35617357 PMCID: PMC9177043 DOI: 10.1371/journal.pcbi.1010113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 06/08/2022] [Accepted: 04/19/2022] [Indexed: 11/19/2022] Open
Abstract
Hoogsteen (HG) base pairing is characterized by a 180° rotation of the purine base with respect to the Watson-Crick-Franklin (WCF) motif. Recently, it has been found that both conformations coexist in a dynamical equilibrium and that several biological functions require HG pairs. This relevance has motivated experimental and computational investigations of the base-pairing transition. However, a systematic simulation of sequence variations has remained out of reach. Here, we employ advanced path-based methods to perform unprecedented free-energy calculations. Our methodology enables us to study the different mechanisms of purine rotation, either remaining inside or after flipping outside of the double helix. We study seven different sequences, which are neighbor variations of a well-studied A⋅T pair in A6-DNA. We observe the known effect of A⋅T steps favoring HG stability, and find evidence of triple-hydrogen-bonded neighbors hindering the inside transition. More importantly, we identify a dominant factor: the direction of the A rotation, with the 6-ring pointing either towards the longer or shorter segment of the chain, respectively relating to a lower or higher barrier. This highlights the role of DNA's relative flexibility as a modulator of the WCF/HG dynamic equilibrium. Additionally, we provide a robust methodology for future HG proclivity studies.
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Affiliation(s)
- Alberto Pérez de Alba Ortíz
- Van ’t Hoff Institute for Molecular Sciences and Amsterdam Center for Multiscale Modeling, University of Amsterdam, Amsterdam, The Netherlands
- Soft Condensed Matter, Debye Institute for Nanomaterials Science, Utrecht University, Utrecht, The Netherlands
| | - Jocelyne Vreede
- Van ’t Hoff Institute for Molecular Sciences and Amsterdam Center for Multiscale Modeling, University of Amsterdam, Amsterdam, The Netherlands
| | - Bernd Ensing
- Van ’t Hoff Institute for Molecular Sciences and Amsterdam Center for Multiscale Modeling, University of Amsterdam, Amsterdam, The Netherlands
- AI4Science Laboratory, University of Amsterdam, Amsterdam, The Netherlands
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9
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Bocus M, Vanduyfhuys L, De Proft F, Weckhuysen BM, Van Speybroeck V. Mechanistic Characterization of Zeolite-Catalyzed Aromatic Electrophilic Substitution at Realistic Operating Conditions. JACS AU 2022; 2:502-514. [PMID: 35252999 PMCID: PMC8889610 DOI: 10.1021/jacsau.1c00544] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Indexed: 05/11/2023]
Abstract
Zeolite-catalyzed benzene ethylation is an important industrial reaction, as it is the first step in the production of styrene for polymer manufacturing. Furthermore, it is a prototypical example of aromatic electrophilic substitution, a key reaction in the synthesis of many bulk and fine chemicals. Despite extensive research, the reaction mechanism and the nature of elusive intermediates at realistic operating conditions is not properly understood. More in detail, the existence of the elusive arenium ion (better known as Wheland complex) formed upon electrophilic attack on the aromatic ring is still a matter of debate. Temperature effects and the presence of protic guest molecules such as water are expected to impact the reaction mechanism and lifetime of the reaction intermediates. Herein, we used enhanced sampling ab initio molecular dynamics simulations to investigate the complete mechanism of benzene ethylation with ethene and ethanol in the H-ZSM-5 zeolite. We show that both the stepwise and concerted mechanisms are active at reaction conditions and that the Wheland intermediate spontaneously appears as a shallow minimum in the free energy surface after the electrophilic attack on the benzene ring. Addition of water enhances the protonation kinetics by about 1 order of magnitude at coverages of one water molecule per Brønsted acidic site. In the fully solvated regime, an overstabilization of the BAS as hydronium ion occurs and the rate enhancement disappears. The obtained results give critical atomistic insights in the role of water to selectively tune the kinetics of protonation reactions in zeolites.
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Affiliation(s)
- Massimo Bocus
- Center
for Molecular Modeling, Ghent University, Technologiepark 46, 9052 Zwijnaarde, Belgium
| | - Louis Vanduyfhuys
- Center
for Molecular Modeling, Ghent University, Technologiepark 46, 9052 Zwijnaarde, Belgium
| | - Frank De Proft
- Eenheid
Algemene Chemie (ALGC), Vrije Universiteit
Brussel, Pleinlaan 2, 1050 Brussels, Belgium
| | - Bert M. Weckhuysen
- Inorganic
Chemistry and Catalysis Group, Debye Institute for Nanomaterials Science, Utrecht University, Universiteitsweg 99, 3584 CG Utrecht, The Netherlands
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10
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Ketkaew R, Creazzo F, Luber S. Closer Look at Inverse Electron Demand Diels–Alder and Nucleophilic Addition Reactions on s-Tetrazines Using Enhanced Sampling Methods. Top Catal 2021; 65:1-17. [PMID: 35153451 PMCID: PMC8816378 DOI: 10.1007/s11244-021-01516-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/25/2021] [Indexed: 12/30/2022]
Abstract
Inverse electron demand [4+2] Diels–Alder (iEDDA) reactions as well as unprecedented nucleophilic (azaphilic) additions of R-substituted silyl-enol ethers (where R is Phenyl, Methyl, and Hydrogen) to 1,2,4,5-tetrazine (s-tetrazine) catalyzed by \documentclass[12pt]{minimal}
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\begin{document}$$\hbox {BF}_{3}$$\end{document}BF3 have recently been discovered (Simon et al. in Org Lett 23(7):2426–2430, 2021), where static calculations were employed for calculation of activation energies. In order to have a more realistic dynamic description of these reactions in explicit solution at ambient conditions, in this work we use a semiempirical tight-binding method combined with enhanced sampling techniques to calculate free energy surfaces (FESs) of the iEDDA and azaphilic addition reactions. Relevant products of not only s-tetrazine but also its derivatives such as \documentclass[12pt]{minimal}
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\begin{document}$$\hbox {BF}_{3}$$\end{document}BF3-mediated s-tetrazine adducts are investigated. We reconstruct the FESs of the iEDDA and azaphilic addition reactions using metadynamics and blue moon ensemble, and compare the ability of different collective variables (CVs) including bond distances, Social PeRmutation INvarianT (SPRINT) coordinates, and path-CV to describe the reaction pathway. We find that when a bulky Phenyl is used as a substituent at the dienophile the azaphilic addition is preferred over the iEDDA reaction. In addition, we also investigate the effect of \documentclass[12pt]{minimal}
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\begin{document}$$\hbox {BF}_{3}$$\end{document}BF3 in the diene and steric hindrance in the dienophile on the competition between the iEDDA and azaphilic addition reactions, providing chemical insight for reaction design.
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Affiliation(s)
- Rangsiman Ketkaew
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
| | - Fabrizio Creazzo
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
| | - Sandra Luber
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
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11
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Hooft F, Pérez de Alba Ortíz A, Ensing B. Discovering Collective Variables of Molecular Transitions via Genetic Algorithms and Neural Networks. J Chem Theory Comput 2021; 17:2294-2306. [PMID: 33662202 PMCID: PMC8047796 DOI: 10.1021/acs.jctc.0c00981] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Indexed: 01/13/2023]
Abstract
With the continual improvement of computing hardware and algorithms, simulations have become a powerful tool for understanding all sorts of (bio)molecular processes. To handle the large simulation data sets and to accelerate slow, activated transitions, a condensed set of descriptors, or collective variables (CVs), is needed to discern the relevant dynamics that describes the molecular process of interest. However, proposing an adequate set of CVs that can capture the intrinsic reaction coordinate of the molecular transition is often extremely difficult. Here, we present a framework to find an optimal set of CVs from a pool of candidates using a combination of artificial neural networks and genetic algorithms. The approach effectively replaces the encoder of an autoencoder network with genes to represent the latent space, i.e., the CVs. Given a selection of CVs as input, the network is trained to recover the atom coordinates underlying the CV values at points along the transition. The network performance is used as an estimator of the fitness of the input CVs. Two genetic algorithms optimize the CV selection and the neural network architecture. The successful retrieval of optimal CVs by this framework is illustrated at the hand of two case studies: the well-known conformational change in the alanine dipeptide molecule and the more intricate transition of a base pair in B-DNA from the classic Watson-Crick pairing to the alternative Hoogsteen pairing. Key advantages of our framework include the following: optimal interpretable CVs, avoiding costly calculation of committor or time-correlation functions, and automatic hyperparameter optimization. In addition, we show that applying a time-delay between the network input and output allows for enhanced selection of slow variables. Moreover, the network can also be used to generate molecular configurations of unexplored microstates, for example, for augmentation of the simulation data.
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Affiliation(s)
- Ferry Hooft
- Van ’t Hoff Institute
for Molecular Sciences, AI4Science Laboratory, and Amsterdam Center
for Multiscale Modeling, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Alberto Pérez de Alba Ortíz
- Van ’t Hoff Institute
for Molecular Sciences, AI4Science Laboratory, and Amsterdam Center
for Multiscale Modeling, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Bernd Ensing
- Van ’t Hoff Institute
for Molecular Sciences, AI4Science Laboratory, and Amsterdam Center
for Multiscale Modeling, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
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12
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Mroginski MA, Adam S, Amoyal GS, Barnoy A, Bondar AN, Borin VA, Church JR, Domratcheva T, Ensing B, Fanelli F, Ferré N, Filiba O, Pedraza-González L, González R, González-Espinoza CE, Kar RK, Kemmler L, Kim SS, Kongsted J, Krylov AI, Lahav Y, Lazaratos M, NasserEddin Q, Navizet I, Nemukhin A, Olivucci M, Olsen JMH, Pérez de Alba Ortíz A, Pieri E, Rao AG, Rhee YM, Ricardi N, Sen S, Solov'yov IA, De Vico L, Wesolowski TA, Wiebeler C, Yang X, Schapiro I. Frontiers in Multiscale Modeling of Photoreceptor Proteins. Photochem Photobiol 2021; 97:243-269. [PMID: 33369749 DOI: 10.1111/php.13372] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 11/01/2020] [Indexed: 02/06/2023]
Abstract
This perspective article highlights the challenges in the theoretical description of photoreceptor proteins using multiscale modeling, as discussed at the CECAM workshop in Tel Aviv, Israel. The participants have identified grand challenges and discussed the development of new tools to address them. Recent progress in understanding representative proteins such as green fluorescent protein, photoactive yellow protein, phytochrome, and rhodopsin is presented, along with methodological developments.
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Affiliation(s)
| | - Suliman Adam
- Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Gil S Amoyal
- Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Avishai Barnoy
- Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ana-Nicoleta Bondar
- Freie Universität Berlin, Department of Physics, Theoretical Molecular Biophysics Group, Berlin, Germany
| | - Veniamin A Borin
- Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Jonathan R Church
- Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Tatiana Domratcheva
- Department of Chemistry, Lomonosov Moscow State University, Moscow, Russia.,Department Biomolecular Mechanisms, Max Planck Institute for Medical Research, Heidelberg, Germany
| | - Bernd Ensing
- Van 't Hoff Institute for Molecular Science and Amsterdam Center for Multiscale Modeling, University of Amsterdam, Amsterdam, The Netherlands
| | - Francesca Fanelli
- Department of Life Sciences, Center for Neuroscience and Neurotechnology, Università degli Studi di Modena e Reggio Emilia, Modena, Italy
| | | | - Ofer Filiba
- Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Laura Pedraza-González
- Department of Biotechnology, Chemistry and Pharmacy, Università degli Studi di Siena, Siena, Italy
| | - Ronald González
- Institut für Chemie, Technische Universität Berlin, Berlin, Germany
| | | | - Rajiv K Kar
- Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Lukas Kemmler
- Freie Universität Berlin, Department of Physics, Theoretical Molecular Biophysics Group, Berlin, Germany
| | - Seung Soo Kim
- Department of Chemistry, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Jacob Kongsted
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Odense, Denmark
| | - Anna I Krylov
- Department of Chemistry, University of Southern California, Los Angeles, CA, USA
| | - Yigal Lahav
- Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel.,MIGAL - Galilee Research Institute, S. Industrial Zone, Kiryat Shmona, Israel
| | - Michalis Lazaratos
- Freie Universität Berlin, Department of Physics, Theoretical Molecular Biophysics Group, Berlin, Germany
| | - Qays NasserEddin
- Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Isabelle Navizet
- MSME, Univ Gustave Eiffel, CNRS UMR 8208, Univ Paris Est Creteil, Marne-la-Vallée, France
| | - Alexander Nemukhin
- Department of Chemistry, Lomonosov Moscow State University, Moscow, Russia.,Emanuel Institute of Biochemical Physics, Russian Academy of Sciences, Moscow, Russia
| | - Massimo Olivucci
- Department of Biotechnology, Chemistry and Pharmacy, Università degli Studi di Siena, Siena, Italy.,Chemistry Department, Bowling Green State University, Bowling Green, OH, USA
| | - Jógvan Magnus Haugaard Olsen
- Department of Chemistry, Aarhus University, Aarhus, Denmark.,Department of Chemistry, Hylleraas Centre for Quantum Molecular Sciences, UiT The Arctic University of Norway, Tromsø, Norway
| | - Alberto Pérez de Alba Ortíz
- Van 't Hoff Institute for Molecular Science and Amsterdam Center for Multiscale Modeling, University of Amsterdam, Amsterdam, The Netherlands
| | - Elisa Pieri
- Aix-Marseille Univ, CNRS, ICR, Marseille, France
| | - Aditya G Rao
- Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Young Min Rhee
- Department of Chemistry, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Niccolò Ricardi
- Département de Chimie Physique, Université de Genève, Genève, Switzerland
| | - Saumik Sen
- Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ilia A Solov'yov
- Department of Physics, Carl von Ossietzky University Oldenburg, Oldenburg, Germany
| | - Luca De Vico
- Department of Biotechnology, Chemistry and Pharmacy, Università degli Studi di Siena, Siena, Italy
| | | | - Christian Wiebeler
- Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Xuchun Yang
- Chemistry Department, Bowling Green State University, Bowling Green, OH, USA
| | - Igor Schapiro
- Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel
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13
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Dutta P, Sengupta N. Expectation maximized molecular dynamics: Toward efficient learning of rarely sampled features in free energy surfaces from unbiased simulations. J Chem Phys 2020; 153:154104. [DOI: 10.1063/5.0021910] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Affiliation(s)
- Pallab Dutta
- Department of Biological Sciences, Indian Institute of Science Education and Research (IISER) Kolkata, Mohanpur 741246, India
| | - Neelanjana Sengupta
- Department of Biological Sciences, Indian Institute of Science Education and Research (IISER) Kolkata, Mohanpur 741246, India
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14
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Rossi K, Jurásková V, Wischert R, Garel L, Corminbœuf C, Ceriotti M. Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH 3SO 3H and H 2O 2 in Phenol. J Chem Theory Comput 2020; 16:5139-5149. [PMID: 32567854 DOI: 10.1021/acs.jctc.0c00362] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
We present a generally applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in an explicit solvent using H2O2 and CH3SO3H in phenol as an example. To address the challenges posed by the complexity of the problem, we resort to a set of data-driven methods and enhanced sampling algorithms. The synergistic application of these techniques makes the first-principle estimation of the chemical properties feasible without renouncing to the use of explicit solvation, involving extensive statistical sampling. Ensembles of neural network (NN) potentials are trained on a set of configurations carefully selected out of preliminary simulations performed at a low-cost density functional tight-binding (DFTB) level. The energy and forces of these configurations are then recomputed at the hybrid density functional theory (DFT) level and used to train the neural networks. The stability of the NN model is enhanced by using DFTB energetics as a baseline, but the efficiency of the direct NN (i.e., baseline-free) is exploited via a multiple-time-step integrator. The neural network potentials are combined with enhanced sampling techniques, such as replica exchange and metadynamics, and used to characterize the relevant protonated species and dominant noncovalent interactions in the mixture, also considering nuclear quantum effects.
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Affiliation(s)
- Kevin Rossi
- Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Veronika Jurásková
- Laboratory for Computational Molecular Design (LCMD), Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Raphael Wischert
- Eco-Efficient Products and Processes Laboratory, Solvay, RIC Shanghai, Shanghai 201108, China
| | - Laurent Garel
- Aroma Performance Laboratory, Solvay, RIC Lyon, 69190 Saint-Fons, France
| | - Clémence Corminbœuf
- Laboratory for Computational Molecular Design (LCMD), Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Michele Ceriotti
- Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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15
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Cota R, Tiwari A, Ensing B, Bakker HJ, Woutersen S. Hydration interactions beyond the first solvation shell in aqueous phenolate solution. Phys Chem Chem Phys 2020; 22:19940-19947. [DOI: 10.1039/d0cp01209b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We investigate the orientational dynamics of water molecules solvating phenolate ions using ultrafast vibrational spectroscopy and density functional theory-based molecular dynamics simulations.
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Affiliation(s)
- Roberto Cota
- Van 't Hoff Institute for Molecular Sciences
- University of Amsterdam
- 1098 XH Amsterdam
- The Netherlands
- AMOLF
| | - Ambuj Tiwari
- Van 't Hoff Institute for Molecular Sciences
- University of Amsterdam
- 1098 XH Amsterdam
- The Netherlands
| | - Bernd Ensing
- Van 't Hoff Institute for Molecular Sciences
- University of Amsterdam
- 1098 XH Amsterdam
- The Netherlands
| | | | - Sander Woutersen
- Van 't Hoff Institute for Molecular Sciences
- University of Amsterdam
- 1098 XH Amsterdam
- The Netherlands
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16
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Rizzi V, Mendels D, Sicilia E, Parrinello M. Blind Search for Complex Chemical Pathways Using Harmonic Linear Discriminant Analysis. J Chem Theory Comput 2019; 15:4507-4515. [DOI: 10.1021/acs.jctc.9b00358] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Valerio Rizzi
- Department of Chemistry and Applied Biosciences, ETH Zurich, c/o USI Campus, Via Giuseppe Buffi 13, CH-6900, Lugano, Ticino, Switzerland
- Facoltà di Informatica, Istituto di Scienze Computazionali, Università della Svizzera italiana (USI), Via Giuseppe Buffi 13, CH-6900, Lugano, Ticino, Switzerland
| | - Dan Mendels
- Department of Chemistry and Applied Biosciences, ETH Zurich, c/o USI Campus, Via Giuseppe Buffi 13, CH-6900, Lugano, Ticino, Switzerland
- Facoltà di Informatica, Istituto di Scienze Computazionali, Università della Svizzera italiana (USI), Via Giuseppe Buffi 13, CH-6900, Lugano, Ticino, Switzerland
- Institute for Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Emilia Sicilia
- Dipartimento di Chimica e Tecnologie Chimiche, Università della Calabria, 87036 Rende CS, Italy
| | - Michele Parrinello
- Department of Chemistry and Applied Biosciences, ETH Zurich, c/o USI Campus, Via Giuseppe Buffi 13, CH-6900, Lugano, Ticino, Switzerland
- Facoltà di Informatica, Istituto di Scienze Computazionali, Università della Svizzera italiana (USI), Via Giuseppe Buffi 13, CH-6900, Lugano, Ticino, Switzerland
- Istituto Italiano di Tecnologia (IIT), Via Morego, 30, 16163 Genova GE, Italy
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17
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O'Connor MB, Bennie SJ, Deeks HM, Jamieson-Binnie A, Jones AJ, Shannon RJ, Walters R, Mitchell TJ, Mulholland AJ, Glowacki DR. Interactive molecular dynamics in virtual reality from quantum chemistry to drug binding: An open-source multi-person framework. J Chem Phys 2019; 150:220901. [PMID: 31202243 DOI: 10.1063/1.5092590] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
As molecular scientists have made progress in their ability to engineer nanoscale molecular structure, we face new challenges in our ability to engineer molecular dynamics (MD) and flexibility. Dynamics at the molecular scale differs from the familiar mechanics of everyday objects because it involves a complicated, highly correlated, and three-dimensional many-body dynamical choreography which is often nonintuitive even for highly trained researchers. We recently described how interactive molecular dynamics in virtual reality (iMD-VR) can help to meet this challenge, enabling researchers to manipulate real-time MD simulations of flexible structures in 3D. In this article, we outline various efforts to extend immersive technologies to the molecular sciences, and we introduce "Narupa," a flexible, open-source, multiperson iMD-VR software framework which enables groups of researchers to simultaneously cohabit real-time simulation environments to interactively visualize and manipulate the dynamics of molecular structures with atomic-level precision. We outline several application domains where iMD-VR is facilitating research, communication, and creative approaches within the molecular sciences, including training machines to learn potential energy functions, biomolecular conformational sampling, protein-ligand binding, reaction discovery using "on-the-fly" quantum chemistry, and transport dynamics in materials. We touch on iMD-VR's various cognitive and perceptual affordances and outline how these provide research insight for molecular systems. By synergistically combining human spatial reasoning and design insight with computational automation, technologies such as iMD-VR have the potential to improve our ability to understand, engineer, and communicate microscopic dynamical behavior, offering the potential to usher in a new paradigm for engineering molecules and nano-architectures.
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Affiliation(s)
- Michael B O'Connor
- Intangible Realities Laboratory, University of Bristol, Cantock's Close, Bristol BS8 1TS, United Kingdom
| | - Simon J Bennie
- Intangible Realities Laboratory, University of Bristol, Cantock's Close, Bristol BS8 1TS, United Kingdom
| | - Helen M Deeks
- Intangible Realities Laboratory, University of Bristol, Cantock's Close, Bristol BS8 1TS, United Kingdom
| | - Alexander Jamieson-Binnie
- Intangible Realities Laboratory, University of Bristol, Cantock's Close, Bristol BS8 1TS, United Kingdom
| | - Alex J Jones
- Intangible Realities Laboratory, University of Bristol, Cantock's Close, Bristol BS8 1TS, United Kingdom
| | - Robin J Shannon
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, United Kingdom
| | - Rebecca Walters
- Intangible Realities Laboratory, University of Bristol, Cantock's Close, Bristol BS8 1TS, United Kingdom
| | - Thomas J Mitchell
- Intangible Realities Laboratory, University of Bristol, Cantock's Close, Bristol BS8 1TS, United Kingdom
| | - Adrian J Mulholland
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, United Kingdom
| | - David R Glowacki
- Intangible Realities Laboratory, University of Bristol, Cantock's Close, Bristol BS8 1TS, United Kingdom
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18
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Abstract
Acid-base reactions are ubiquitous in nature. Understanding their mechanisms is crucial in many fields, from biochemistry to industrial catalysis. Unfortunately, experiments give only limited information without much insight into the molecular behavior. Atomistic simulations could complement experiments and shed precious light on microscopic mechanisms. The large free-energy barriers connected to proton dissociation, however, make the use of enhanced sampling methods mandatory. Here we perform an ab initio molecular dynamics (MD) simulation and enhance sampling with the help of metadynamics. This has been made possible by the introduction of descriptors or collective variables (CVs) that are based on a conceptually different outlook on acid-base equilibria. We test successfully our approach on three different aqueous solutions of acetic acid, ammonia, and bicarbonate. These are representative of acid, basic, and amphoteric behavior.
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19
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The Adaptive Path Collective Variable: A Versatile Biasing Approach to Compute the Average Transition Path and Free Energy of Molecular Transitions. Methods Mol Biol 2019; 2022:255-290. [PMID: 31396907 DOI: 10.1007/978-1-4939-9608-7_11] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
In the past decade, great progress has been made in the development of enhanced sampling methods, aimed at overcoming the time-scale limitations of molecular dynamics (MD) simulations. Many sampling schemes rely on adding an external bias to favor the sampling of transitions and to estimate the underlying free energy landscape. Nevertheless, sampling molecular processes described by many order parameters, or collective variables (CVs), such as complex biomolecular transitions, remains often very challenging. The computational cost has a prohibitive scaling with the dimensionality of the CV-space. Inspiration can be taken from methods that focus on localizing transition pathways: the CV-space can be projected onto a path-CV that connects two stable states, and a bias can be exerted onto a one-dimensional parameter that captures the progress of the transition along the path-CV. In principle, such a sampling scheme can handle an arbitrarily large number of CVs. A standard enhanced sampling technique combined with an adaptive path-CV can then locate the mean transition pathway and obtain the free energy profile along the path. In this chapter, we discuss the adaptive path-CV formalism and its numerical implementation. We apply the path-CV with several enhanced sampling methods-steered MD, metadynamics, and umbrella sampling-to a biologically relevant process: the Watson-Crick to Hoogsteen base-pairing transition in double-stranded DNA. A practical guide is provided on how to recognize and circumvent possible pitfalls during the calculation of a free energy landscape that contains multiple pathways. Examples are presented on how to perform enhanced sampling simulations using PLUMED, a versatile plugin that can work with many popular MD engines.
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