1
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Oh M, Rosa M, Xie H, Khelashvili G. Automated collective variable discovery for MFSD2A transporter from molecular dynamics simulations. Biophys J 2024; 123:2934-2955. [PMID: 38932456 PMCID: PMC11393714 DOI: 10.1016/j.bpj.2024.06.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 06/03/2024] [Accepted: 06/24/2024] [Indexed: 06/28/2024] Open
Abstract
Biomolecules often exhibit complex free energy landscapes in which long-lived metastable states are separated by large energy barriers. Overcoming these barriers to robustly sample transitions between the metastable states with classical molecular dynamics (MD) simulations presents a challenge. To circumvent this issue, collective variable (CV)-based enhanced sampling MD approaches are often employed. Traditional CV selection relies on intuition and prior knowledge of the system. This approach introduces bias, which can lead to incomplete mechanistic insights. Thus, automated CV detection is desired to gain a deeper understanding of the system/process. Analysis of MD data with various machine-learning algorithms, such as principal component analysis (PCA), support vector machine, and linear discriminant analysis (LDA) based approaches have been implemented for automated CV detection. However, their performance has not been systematically evaluated on structurally and mechanistically complex biological systems. Here, we applied these methods to MD simulations of the MFSD2A (Major Facilitator Superfamily Domain 2A) lysolipid transporter in multiple functionally relevant metastable states with the goal of identifying optimal CVs that would structurally discriminate these states. Specific emphasis was on the automated detection and interpretive power of LDA-based CVs. We found that LDA methods, which included a novel gradient descent-based multiclass harmonic variant, termed GDHLDA, we developed here, outperform PCA in class separation, exhibiting remarkable consistency in extracting CVs critical for distinguishing metastable states. Furthermore, the identified CVs included features previously associated with conformational transitions in MFSD2A. Specifically, conformational shifts in transmembrane helix 7 and in residue Y294 on this helix emerged as critical features discriminating the metastable states in MFSD2A. This highlights the effectiveness of LDA-based approaches in automatically extracting from MD trajectories CVs of functional relevance that can be used to drive biased MD simulations to efficiently sample conformational transitions in the molecular system.
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Affiliation(s)
- Myongin Oh
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - Margarida Rosa
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - Hengyi Xie
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - George Khelashvili
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York.
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2
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Yuan S, Han X, Zhang J, Xie Z, Fan C, Xiao Y, Gao YQ, Yang YI. Generating High-Precision Force Fields for Molecular Dynamics Simulations to Study Chemical Reaction Mechanisms Using Molecular Configuration Transformer. J Phys Chem A 2024; 128:4378-4390. [PMID: 38759697 DOI: 10.1021/acs.jpca.4c01267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2024]
Abstract
Theoretical studies on chemical reaction mechanisms have been crucial in organic chemistry. Traditionally, calculating the manually constructed molecular conformations of transition states for chemical reactions using quantum chemical calculations is the most commonly used method. However, this way is heavily dependent on individual experience and chemical intuition. In our previous study, we proposed a research paradigm that used enhanced sampling in molecular dynamics simulations to study chemical reactions. This approach can directly simulate the entire process of a chemical reaction. However, the computational speed limited the use of high-precision potential energy functions for simulations. To address this issue, we presented a scheme for training high-precision force fields for molecular modeling using a previously developed graph-neural-network-based molecular model, molecular configuration transformer. This potential energy function allowed for highly accurate simulations at a low computational cost, leading to more precise calculations of the mechanism of chemical reactions. We applied this approach to study a Claisen rearrangement reaction and a carbonyl insertion reaction catalyzed by manganese.
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Affiliation(s)
- Sihao Yuan
- Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Xu Han
- Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Jun Zhang
- Changping Laboratory, Beijing 102200, China
| | - Zhaoxin Xie
- Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Cheng Fan
- Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Yunlong Xiao
- Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Yi Qin Gao
- Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China
- Changping Laboratory, Beijing 102200, China
| | - Yi Isaac Yang
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China
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3
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da Hora GCA, Oh M, Nguyen JDM, Swanson JMJ. One Descriptor to Fold Them All: Harnessing Intuition and Machine Learning to Identify Transferable Lasso Peptide Reaction Coordinates. J Phys Chem B 2024; 128:4063-4075. [PMID: 38568862 PMCID: PMC11282586 DOI: 10.1021/acs.jpcb.3c08492] [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] [Indexed: 04/05/2024]
Abstract
Identifying optimal reaction coordinates for complex conformational changes and protein folding remains an outstanding challenge. This study combines collective variable (CV) discovery based on chemical intuition and machine learning with enhanced sampling to converge the folding free energy landscape of lasso peptides, a unique class of natural products with knot-like tertiary structures. This knotted scaffold imparts remarkable stability, making lasso peptides resistant to proteolytic degradation, thermal denaturation, and extreme pH conditions. Although their direct synthesis would enable therapeutic design, it has not yet been possible due to the improbable occurrence of spontaneous lasso folding. Thus, simulations characterizing the folding propensity are needed to identify strategies for increasing access to the lasso architecture by stabilizing the pre-lasso ensemble before isopeptide bond formation. Herein, harmonic linear discriminant analysis (HLDA) is combined with metadynamics-enhanced sampling to discover CVs capable of distinguishing the pre-lasso fold and converging the folding propensity. Intuitive CVs are compared to iterative rounds of HLDA to identify CVs that not only accomplish these goals for one lasso peptide but also seem to be transferable to others, establishing a protocol for the identification of folding reaction coordinates for lasso peptides.
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Affiliation(s)
- Gabriel C A da Hora
- Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
| | - Myongin Oh
- Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
| | - John D M Nguyen
- Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
| | - Jessica M J Swanson
- Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
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4
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Ren FD, Liu YZ, Ding KW, Chang LL, Cao DL, Liu S. Finite temperature string by K-means clustering sampling with order parameters as collective variables for molecular crystals: application to polymorphic transformation between β-CL-20 and ε-CL-20. Phys Chem Chem Phys 2024; 26:3500-3515. [PMID: 38206084 DOI: 10.1039/d3cp05389j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Polymorphic transformation of molecular crystals is a fundamental phase transition process, and it is important practically in the chemical, material, biopharmaceutical, and energy storage industries. However, understanding of the transformation mechanism at the molecular level is poor due to the extreme simulating challenges in enhanced sampling and formulating order parameters (OPs) as the collective variables that can distinguish polymorphs with quite similar and complicated structures so as to describe the reaction coordinate. In this work, two kinds of OPs for CL-20 were constructed by the bond distances, bond orientations and relative orientations. A K-means clustering algorithm based on the Euclidean distance and sample weight was used to smooth the initial finite temperature string (FTS), and the minimum free energy path connecting β-CL-20 and ε-CL-20 was sketched by the string method in collective variables, and the free energy profile along the path and the nucleation kinetics were obtained by Markovian milestoning with Voronoi tessellations. In comparison with the average-based sampling, the K-means clustering algorithm provided an improved convergence rate of FTS. The simulation of transformation was independent of OP types but was affected greatly by finite-size effects. A surface-mediated local nucleation mechanism was confirmed and the configuration located at the shoulder of potential of mean force, rather than overall maximum, was confirmed to be the critical nucleus formed by the cooperative effect of the intermolecular interactions. This work provides an effective way to explore the polymorphic transformation of caged molecular crystals at the molecular level.
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Affiliation(s)
- Fu-de Ren
- School of Chemical Engineering and Technology, North University of China, Taiyuan 030051, China.
| | - Ying-Zhe Liu
- Xi'an Modern Chemistry Research Institute, Xi'an 710065, China
| | - Ke-Wei Ding
- Xi'an Modern Chemistry Research Institute, Xi'an 710065, China
| | - Ling-Ling Chang
- School of Chemical Engineering and Technology, North University of China, Taiyuan 030051, China.
| | - Duan-Lin Cao
- School of Chemical Engineering and Technology, North University of China, Taiyuan 030051, China.
| | - Shubin Liu
- Research Computing Center, University of North Carolina, Chapel Hill, North Carolina 27599-3420, USA.
- Depaertment of Chemistry, University of North Carolina, Chapel Hill, North Carolina 27599-3290, USA
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5
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Blumer O, Reuveni S, Hirshberg B. Combining stochastic resetting with Metadynamics to speed-up molecular dynamics simulations. Nat Commun 2024; 15:240. [PMID: 38172126 PMCID: PMC10764788 DOI: 10.1038/s41467-023-44528-w] [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: 08/09/2023] [Accepted: 12/18/2023] [Indexed: 01/05/2024] Open
Abstract
Metadynamics is a powerful method to accelerate molecular dynamics simulations, but its efficiency critically depends on the identification of collective variables that capture the slow modes of the process. Unfortunately, collective variables are usually not known a priori and finding them can be very challenging. We recently presented a collective variables-free approach to enhanced sampling using stochastic resetting. Here, we combine the two methods, showing that it can lead to greater acceleration than either of them separately. We also demonstrate that resetting Metadynamics simulations performed with suboptimal collective variables can lead to speedups comparable with those obtained with optimal collective variables. Therefore, applying stochastic resetting can be an alternative to the challenging task of improving suboptimal collective variables, at almost no additional computational cost. Finally, we propose a method to extract unbiased mean first-passage times from Metadynamics simulations with resetting, resulting in an improved tradeoff between speedup and accuracy. This work enables combining stochastic resetting with other enhanced sampling methods to accelerate a broad range of molecular simulations.
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Affiliation(s)
- Ofir Blumer
- School of Chemistry, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Shlomi Reuveni
- School of Chemistry, Tel Aviv University, Tel Aviv, 6997801, Israel
- The Center for Computational Molecular and Materials Science, Tel Aviv University, Tel Aviv, 6997801, Israel
- The Center for Physics and Chemistry of Living Systems, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Barak Hirshberg
- School of Chemistry, Tel Aviv University, Tel Aviv, 6997801, Israel.
- The Center for Computational Molecular and Materials Science, Tel Aviv University, Tel Aviv, 6997801, Israel.
- The Center for Physics and Chemistry of Living Systems, Tel Aviv University, Tel Aviv, 6997801, Israel.
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6
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Siddiqui GA, Stebani JA, Wragg D, Koutsourelakis PS, Casini A, Gagliardi A. Application of Machine Learning Algorithms to Metadynamics for the Elucidation of the Binding Modes and Free Energy Landscape of Drug/Target Interactions: a Case Study. Chemistry 2023; 29:e202302375. [PMID: 37555841 DOI: 10.1002/chem.202302375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 08/09/2023] [Indexed: 08/10/2023]
Abstract
In the context of drug discovery, computational methods were able to accelerate the challenging process of designing and optimizing a new drug candidate. Amongst the possible atomistic simulation approaches, metadynamics (metaD) has proven very powerful. However, the choice of collective variables (CVs) is not trivial for complex systems. To automate the process of CVs identification, two different machine learning algorithms were applied in this study, namely DeepLDA and Autoencoder, to the metaD simulation of a well-researched drug/target complex, consisting in a pharmacologically relevant non-canonical DNA secondary structure (G-quadruplex) and a metallodrug acting as its stabilizer, as well as solvent molecules.
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Affiliation(s)
- Gohar Ali Siddiqui
- Professorship of Simulation of Nanosystems for Energy Conversion Department of Electrical and Computer Engineering School of Computation, Information and Technology, Technical University of Munich (TUM), Hans-Piloty-Str. 1, 85748, Garching b. München, Germany
| | - Julia A Stebani
- Chair of Medicinal and Bioinorganic Chemistry Department of Chemistry, School of Natural Sciences, Technical University of Munich (TUM), Lichtenbergstr. 4, 85748, Garching b. München, Germany
| | - Darren Wragg
- Chair of Medicinal and Bioinorganic Chemistry Department of Chemistry, School of Natural Sciences, Technical University of Munich (TUM), Lichtenbergstr. 4, 85748, Garching b. München, Germany
| | - Phaedon-Stelios Koutsourelakis
- Professorship for Data-driven Materials Modeling School of Engineering and Design, Technical University of Munich (TUM), Boltzmannstr. 15, 85748, Garching b. München, Germany
| | - Angela Casini
- Chair of Medicinal and Bioinorganic Chemistry Department of Chemistry, School of Natural Sciences, Technical University of Munich (TUM), Lichtenbergstr. 4, 85748, Garching b. München, Germany
| | - Alessio Gagliardi
- Professorship of Simulation of Nanosystems for Energy Conversion Department of Electrical and Computer Engineering School of Computation, Information and Technology, Technical University of Munich (TUM), Hans-Piloty-Str. 1, 85748, Garching b. München, Germany
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7
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Deng Y, Fu S, Guo J, Xu X, Li H. Anisotropic Collective Variables with Machine Learning Potential for Ab Initio Crystallization of Complex Ceramics. ACS NANO 2023; 17:14099-14113. [PMID: 37458408 DOI: 10.1021/acsnano.3c04602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
Enhanced sampling molecular dynamics (MD) simulations have been extensively used in the phase transition study of simple crystalline materials, such as aluminum, silica, and ice. However, MD simulation of the crystallization process for complex crystalline materials still faces a formidable challenge due to their multicomponent induced multiphase problem. Here, we realize the ab initio accuracy MD crystallization simulations of complex ceramics by using anisotropic collective variables (CVs) and machine learning (ML) potential. The anisotropic X-ray diffraction intensity CVs provide precise identification of complex crystal structures with detailed crystallography information, while the ML potential makes it feasible to further perform enhanced sampling simulations with ab initio accuracy. We verify the universality and accuracy of this method through complex ceramics with three kinds of representative structures, i.e., Ti3SiC2 for the MAX structure, zircon for the mineral structure, and lead zirconate titanate for the perovskite structure. It demonstrates exceptional efficiency and ab initio quality in achieving crystallization and generating free energy surfaces of all these ceramics, facilitating the analysis and design of complex crystalline materials.
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Affiliation(s)
- Yuanpeng Deng
- Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology and Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin 150090, China
| | - Shubin Fu
- Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology and Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin 150090, China
| | - Jingran Guo
- Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology and Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin 150090, China
| | - Xiang Xu
- Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology and Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin 150090, China
| | - Hui Li
- Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology and Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin 150090, China
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8
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Sasmal S, McCullagh M, Hocky GM. Reaction Coordinates for Conformational Transitions Using Linear Discriminant Analysis on Positions. J Chem Theory Comput 2023; 19:4427-4435. [PMID: 37130367 PMCID: PMC10373481 DOI: 10.1021/acs.jctc.3c00051] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Indexed: 05/04/2023]
Abstract
In this work, we demonstrate that Linear Discriminant Analysis (LDA) applied to atomic positions in two different states of a biomolecule produces a good reaction coordinate between those two states. Atomic coordinates of a macromolecule are a direct representation of a macromolecular configuration, and yet, they are not used in enhanced sampling studies due to a lack of rotational and translational invariance. We resolve this issue using the technique of our prior work, whereby a molecular configuration is considered a member of an equivalence class in size-and-shape space, which is the set of all configurations that can be translated and rotated to a single point within a reference multivariate Gaussian distribution characterizing a single molecular state. The reaction coordinates produced by LDA applied to positions are shown to be good reaction coordinates both in terms of characterizing the transition between two states of a system within a long molecular dynamics (MD) simulation and also ones that allow us to readily produce free energy estimates along that reaction coordinate using enhanced sampling MD techniques.
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Affiliation(s)
- Subarna Sasmal
- Department
of Chemistry and Simons Center for Computational Physical Chemistry, New York University, New York, New York 10003, United States
| | - Martin McCullagh
- Department
of Chemistry, Oklahoma State University, Stillwater, Oklahoma 74078, United States
| | - Glen M. Hocky
- Department
of Chemistry and Simons Center for Computational Physical Chemistry, New York University, New York, New York 10003, United States
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9
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Mendels D, Byléhn F, Sirk TW, de Pablo JJ. Systematic modification of functionality in disordered elastic networks through free energy surface tailoring. SCIENCE ADVANCES 2023; 9:eadf7541. [PMID: 37285442 DOI: 10.1126/sciadv.adf7541] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 05/01/2023] [Indexed: 06/09/2023]
Abstract
A combined machine learning-physics-based approach is explored for molecular and materials engineering. Specifically, collective variables, akin to those used in enhanced sampled simulations, are constructed using a machine learning model trained on data gathered from a single system. Through the constructed collective variables, it becomes possible to identify critical molecular interactions in the considered system, the modulation of which enables a systematic tailoring of the system's free energy landscape. To explore the efficacy of the proposed approach, we use it to engineer allosteric regulation and uniaxial strain fluctuations in a complex disordered elastic network. Its successful application in these two cases provides insights regarding how functionality is governed in systems characterized by extensive connectivity and points to its potential for design of complex molecular systems.
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Affiliation(s)
- Dan Mendels
- Pritzker School of Molecular Engineering, University of Chicago, 5640 S. Ellis Avenue, Chicago, IL 60637 USA
| | - Fabian Byléhn
- Pritzker School of Molecular Engineering, University of Chicago, 5640 S. Ellis Avenue, Chicago, IL 60637 USA
| | - Timothy W Sirk
- Polymers Branch, U.S. CCDC Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA
| | - Juan J de Pablo
- Pritzker School of Molecular Engineering, University of Chicago, 5640 S. Ellis Avenue, Chicago, IL 60637 USA
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10
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Elishav O, Podgaetsky R, Meikler O, Hirshberg B. Collective Variables for Conformational Polymorphism in Molecular Crystals. J Phys Chem Lett 2023; 14:971-976. [PMID: 36689770 PMCID: PMC9900638 DOI: 10.1021/acs.jpclett.2c03491] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 01/12/2023] [Indexed: 06/17/2023]
Abstract
Controlling polymorphism in molecular crystals is crucial in the pharmaceutical, dye, and pesticide industries. However, its theoretical description is extremely challenging, due to the associated long time scales (>1 μs). We present an efficient procedure for identifying collective variables that promote transitions between conformational polymorphs in molecular dynamics simulations. It involves applying a simple dimensionality reduction algorithm to data from short (∼ps) simulations of the isolated conformers that correspond to each polymorph. We demonstrate the utility of our method in the challenging case of the important energetic material, CL-20, which has three anhydrous conformational polymorphs at ambient pressure. Using these collective variables in Metadynamics simulations, we observe transitions between all solid polymorphs in the biased trajectories. We reconstruct the free energy surface and identify previously unknown defect and intermediate forms in the transition from one known polymorph to another. Our method provides insights into complex conformational polymorphic transitions of flexible molecular crystals.
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Affiliation(s)
- Oren Elishav
- School
of Chemistry, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Roy Podgaetsky
- School
of Chemistry, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Olga Meikler
- Rafael
Ltd., P.O. Box 2250, Haifa 3102102, Israel
| | - Barak Hirshberg
- School
of Chemistry, Tel Aviv University, Tel Aviv 6997801, Israel
- The
Center for Computational Molecular and Materials Science, Tel Aviv University, Tel Aviv 6997801, Israel
- The
Ratner Center for Single Molecule Science, Tel Aviv University, Tel Aviv 6997801, Israel
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11
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Neha, Tiwari V, Mondal S, Kumari N, Karmakar T. Collective Variables for Crystallization Simulations-from Early Developments to Recent Advances. ACS OMEGA 2023; 8:127-146. [PMID: 36643553 PMCID: PMC9835087 DOI: 10.1021/acsomega.2c06310] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 12/08/2022] [Indexed: 03/11/2024]
Abstract
Crystallization is an important physicochemical process which has relevance in material science, biology, and the environment. Decades of experimental and theoretical efforts have been made to understand this fundamental symmetry-breaking transition. While experiments provide equilibrium structures and shapes of crystals, they are limited to unraveling how molecules aggregate to form crystal nuclei that subsequently transform into bulk crystals. Computer simulations, mainly molecular dynamics (MD), can provide such microscopic details during the early stage of a crystallization event. Crystallization is a rare event that takes place in time scales much longer than a typical equilibrium MD simulation can sample. This inadequate sampling of the MD method can be easily circumvented by the use of enhanced sampling (ES) simulations. In most of the ES methods, the fluctuations of a system's slow degrees of freedom, called collective variables (CVs), are enhanced by applying a bias potential. This transforms the system from one state to the other within a short time scale. The most crucial part of such CV-based ES methods is to find suitable CVs, which often needs intuition and several trial-and-error optimization steps. Over the years, a plethora of CVs has been developed and applied in the study of crystallization. In this review, we provide a brief overview of CVs that have been developed and used in ES simulations to study crystallization from melt or solution. These CVs can be categorized mainly into four types: (i) spherical particle-based, (ii) molecular template-based, (iii) physical property-based, and (iv) CVs obtained from dimensionality reduction techniques. We present the context-based evolution of CVs, discuss the current challenges, and propose future directions to further develop effective CVs for the study of crystallization of complex systems.
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Affiliation(s)
| | | | | | | | - Tarak Karmakar
- Department of Chemistry, Indian Institute of Technology, Delhi, Hauz Khas, New Delhi110016, India
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12
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Bjelobrk Z, Rajagopalan AK, Mendels D, Karmakar T, Parrinello M, Mazzotti M. Solubility of Organic Salts in Solvent-Antisolvent Mixtures: A Combined Experimental and Molecular Dynamics Simulations Approach. J Chem Theory Comput 2022; 18:4952-4959. [PMID: 35833664 PMCID: PMC9367008 DOI: 10.1021/acs.jctc.2c00304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We combine molecular dynamics simulations with experiments to estimate solubilities of an organic salt in complex growth environments. We predict the solubility by simulations of the growth and dissolution of ions at the crystal surface kink sites at different solution concentrations. Thereby, the solubility is identified as the solution's salt concentration, where the energy of the ion pair dissolved in solution equals the energy of the ion pair crystallized at the kink sites. The simulation methodology is demonstrated for the case of anhydrous sodium acetate crystallized from various solvent-antisolvent mixtures. To validate the predicted solubilities, we have measured the solubilities of sodium acetate in-house, using an experimental setup and measurement protocol that guarantees moisture-free conditions, which is key for a hygroscopic compound like sodium acetate. We observe excellent agreement between the experimental and the computationally evaluated solubilities for sodium acetate in different solvent-antisolvent mixtures. Given the agreement and the rich data the simulations produce, we can use them to complement experimental tasks, which in turn will reduce time and capital in the design of complicated industrial crystallization processes of organic salts.
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Affiliation(s)
- Zoran Bjelobrk
- Institute of Energy and Process Engineering, ETH Zürich, Zürich CH-8092, Switzerland
| | - Ashwin Kumar Rajagopalan
- Department of Chemical Engineering, University of Manchester, Manchester M13 9PL, United Kingdom
| | - Dan Mendels
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Tarak Karmakar
- Department of Chemistry, Indian Institute of Technology, Delhi, Hauz Khas, New Delhi 110016, India
| | - Michele Parrinello
- Istituto Italiano di Tecnologia (IIT), Via Morego, 30, Genova 16163, Italy
| | - Marco Mazzotti
- Institute of Energy and Process Engineering, ETH Zürich, Zürich CH-8092, Switzerland
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13
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Han X, Zhang J, Yang YI, Zhang Z, Yang L, Gao YQ. Enhanced Sampling Simulation Reveals How Solvent Influences Chirogenesis of the Intra-Molecular Diels-Alder Reaction. J Chem Theory Comput 2022; 18:4318-4326. [PMID: 35666128 DOI: 10.1021/acs.jctc.2c00233] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
The timescale involved in chemical reactions is quite often beyond that of normal molecular dynamics simulations. Here, we combine metadynamics with selective integrated tempering sampling to simulate an intra-molecular Diels-Alder reaction in explicit solvents. Based on a one-dimensional collective variable obtained from harmonic linear discriminant analysis, four chiral isomers of products were observed in the simulation. Analyses of reactive trajectories showed that this reaction follows a concerted mechanism in all four solvents. In addition, the hydrogen bond between the reactant and water solvent plays an important role in the water-accelerated reaction mechanism. The dynamics of chirality formation varies significantly with solvents. The chirality of products forms significantly before the transition state, especially in ionic liquid.
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Affiliation(s)
- Xu Han
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Jun Zhang
- Changping Laboratory, Beijing 102206, China
| | - Yi Isaac Yang
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong 518132, China
| | - Zhen Zhang
- School of Physics and Technology, Tangshan Normal University, Tangshan 063000, China
| | - Lijiang Yang
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Yi Qin Gao
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.,Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong 518132, China
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14
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Mendels D, de Pablo JJ. Collective Variables for Free Energy Surface Tailoring: Understanding and Modifying Functionality in Systems Dominated by Rare Events. J Phys Chem Lett 2022; 13:2830-2837. [PMID: 35324208 DOI: 10.1021/acs.jpclett.2c00317] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
We introduce a method for elucidating and modifying the functionality of systems dominated by rare events that relies on the semiautomated tuning of their underlying free energy surface. The proposed approach seeks to construct collective variables (CVs) that encode the essential information regarding the rare events of the system of interest. The appropriate CVs are identified using harmonic linear discriminant analysis (HLDA), a machine-learning-based method that is trained solely on data collected from short ordinary simulations in the relevant metastable states of the system. Utilizing the interpretable form of the resulting CVs, the critical interaction potentials that determine the system's rare transitions are identified and purposely modified to tailor the free energy surface in a manner that alters functionality as desired. The applicability of the method is illustrated in the context of three different systems, thereby demonstrating that thermodynamic and kinetic properties can be tractably modified with little to no prior knowledge or intuition.
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Affiliation(s)
- Dan Mendels
- Pritzker School of Molecular Engineering, University of Chicago, South Ellise, Chicago, Illinois 60637, United States
| | - Juan J de Pablo
- Pritzker School of Molecular Engineering, University of Chicago, South Ellise, Chicago, Illinois 60637, United States
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15
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Krep L, Roy IS, Kopp W, Schmalz F, Huang C, Leonhard K. Efficient Reaction Space Exploration with ChemTraYzer-TAD. J Chem Inf Model 2022; 62:890-902. [PMID: 35142513 DOI: 10.1021/acs.jcim.1c01197] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The development of a reaction model is often a time-consuming process, especially if unknown reactions have to be found and quantified. To alleviate the reaction modeling process, automated procedures for reaction space exploration are highly desired. We present ChemTraYzer-TAD, a new reactive molecular dynamics acceleration technique aimed at efficient reaction space exploration. The new method is based on the basin confinement strategy known from the temperature-accelerated dynamics (TAD) acceleration method. Our method features integrated ChemTraYzer bond-order processing steps for the automatic and on-the-fly determination of the positions of virtual walls in configuration space that confine the system in a potential energy basin. We use the example of 1,3-dioxolane-4-hydroperoxide-2-yl radical oxidation to show that ChemTraYzer-TAD finds more than 100 different parallel reactions for the given set of reactants in less than 2 ns of simulation time. Among the many observed reactions, ChemTraYzer-TAD finds the expected typical low-temperature reactions despite the use of extremely high simulation temperatures up to 5000 K. Our method also finds a new concerted β-scission plus O2 addition with a lower reaction barrier than the literature-known and so-far dominant β-scission.
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Affiliation(s)
- Lukas Krep
- Institute of Technical Thermodynamics, RWTH Aachen University, Aachen 52062, Germany
| | - Indu Sekhar Roy
- Institute of Technical Thermodynamics, RWTH Aachen University, Aachen 52062, Germany
| | - Wassja Kopp
- Institute of Technical Thermodynamics, RWTH Aachen University, Aachen 52062, Germany
| | - Felix Schmalz
- Institute of Technical Thermodynamics, RWTH Aachen University, Aachen 52062, Germany
| | - Can Huang
- Institute of Technical Thermodynamics, RWTH Aachen University, Aachen 52062, Germany
| | - Kai Leonhard
- Institute of Technical Thermodynamics, RWTH Aachen University, Aachen 52062, Germany
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16
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Paul TK, Taraphder S. Nonlinear Reaction Coordinate of an Enzyme Catalyzed Proton Transfer Reaction. J Phys Chem B 2022; 126:1413-1425. [PMID: 35138854 DOI: 10.1021/acs.jpcb.1c08760] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
We present an in-depth study on the theoretical calculation of an optimum reaction coordinate as a linear or nonlinear combination of important collective variables (CVs) sampled from an ensemble of reactive transition paths for an intramolecular proton transfer reaction catalyzed by the enzyme human carbonic anhydrase (HCA) II. The linear models are optimized by likelihood maximization for a given number of CVs. The nonlinear models are based on an artificial neural network with the same number of CVs and optimized by minimizing the root-mean-square error in comparison to a training set of committor estimators generated for the given transition. The nonlinear reaction coordinate thus obtained yields the free energy of activation and rate constant as 9.46 kcal mol-1 and 1.25 × 106 s-1, respectively. These estimates are found to be in quantitative agreement with the known experimental results. We have also used an extended autoencoder model to show that a similar analysis can be carried out using a single CV only. The resultant free energies and kinetics of the reaction slightly overestimate the experimental data. The implications of these results are discussed using a detailed microkinetic scheme of the proton transfer reaction catalyzed by HCA II.
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Affiliation(s)
- Tanmoy Kumar Paul
- Department of Chemistry, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
| | - Srabani Taraphder
- Department of Chemistry, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
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17
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Chen X, Liu M, Gao J. CARNOT: a Fragment-Based Direct Molecular Dynamics and Virtual-Reality Simulation Package for Reactive Systems. J Chem Theory Comput 2022; 18:1297-1313. [PMID: 35129348 DOI: 10.1021/acs.jctc.1c01032] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Traditionally, the study of reaction mechanisms of complex reaction systems such as combustion has been performed on an individual basis by optimizations of transition structure and minimum energy path or by reaction dynamics trajectory calculations for one elementary reaction at a time. It is effective, but time-consuming, whereas important and unexpected processes could have been missed. In this article, we present a direct molecular dynamics (DMD) approach and a virtual-reality simulation program, CARNOT, in which plausible chemical reactions are simulated simultaneously at finite temperature and pressure conditions. A key concept of the present ab initio molecular dynamics method is to partition a large, chemically reactive system into molecular fragments that can be adjusted on the fly of a DMD simulation. The theory represents an extension of the explicit polarization method to reactive events, called ReX-Pol. We propose a highest-and-lowest adapted-spin approximation to define the local spins of individual fragments, rather than treating the entire system by a delocalized wave function. Consequently, the present ab initio DMD can be applied to reactive systems consisting of an arbitrarily varying number of closed and open-shell fragments such as free radicals, zwitterions, and separate ions found in combustion and other reactions. A graph-data structure algorithm was incorporated in CARNOT for the analysis of reaction networks, suitable for reaction mechanism reduction. Employing the PW91 density functional theory and the 6-31+G(d) basis set, the capabilities of the CARNOT program were illustrated by a combustion reaction, consisting of 28 650 atoms, and by reaction network analysis that revealed a range of mechanistic and dynamical events. The method may be useful for applications to other types of complex reactions.
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Affiliation(s)
- Xin Chen
- Peking University Shenzhen Graduate School, Shenzhen, Guangdong 581055, China.,Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong 581055, China
| | - Meiyi Liu
- Peking University Shenzhen Graduate School, Shenzhen, Guangdong 581055, China.,Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong 581055, China
| | - Jiali Gao
- Peking University Shenzhen Graduate School, Shenzhen, Guangdong 581055, China.,Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong 581055, China.,Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455, United States
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18
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Baiardi A, Grimmel SA, Steiner M, Türtscher PL, Unsleber JP, Weymuth T, Reiher M. Expansive Quantum Mechanical Exploration of Chemical Reaction Paths. Acc Chem Res 2022; 55:35-43. [PMID: 34918903 DOI: 10.1021/acs.accounts.1c00472] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Quantum mechanical methods have been well-established for the elucidation of reaction paths of chemical processes and for the explicit dynamics of molecular systems. While they are usually deployed in routine manual calculations on reactions for which some insights are already available (typically from experiment), new algorithms and continuously increasing capabilities of modern computer hardware allow for exploratory open-ended computational campaigns that are unbiased and therefore enable unexpected discoveries. Highly efficient and even automated procedures facilitate systematic approaches toward the exploration of uncharted territory in molecular transformations and dynamics. In this work, we elaborate on such explorative approaches that range from reaction network explorations with (stationary) quantum chemical methods to explorative molecular dynamics and migrant wave packet dynamics. The focus is on recent developments that cover the following strategies. (i) Pruning search options for elementary reaction steps by heuristic rules based on the first-principles of quantum mechanics: Rules are required for reducing the combinatorial explosion of potentially reactive atom pairings, and rooting them in concepts derived from the electronic wave function makes them applicable to any molecular system. (ii) Enforcing reactive events by external biases: Inducing a reaction requires constraints that steer and direct elementary-step searches, which can be formulated in terms of forces, velocities, or supplementary potentials. (iii) Manual steering facilitated by interactive quantum mechanics: As ultrafast quantum chemical methods allow for real-time manual interactions with molecular systems, human-intuition-guided paths can be easily explored with suitable human-machine interfaces. (iv) New approaches for transition-state optimization with continuous curve representations can provide stable schemes to be driven in an automated way by allowing for an efficient tuning of the curve's parameters (instead of a manipulation of a collection of structures along the path), and (v) reactive molecular dynamics and direct wave packet propagation exploit the equations of motion of an underlying mechanical theory (usually, classical Newtonian mechanics or Schrödinger quantum mechanics). Explorative approaches are likely to replace the current state of the art in computational chemistry, because they reduce the human effort to be invested in reaction path elucidations, they are less prone to errors and bias-free, and they cover more extensive regions of the relevant configuration space. As a result, computational investigations that rely on these techniques are more likely to deliver surprising discoveries.
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Affiliation(s)
- Alberto Baiardi
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Stephanie A. Grimmel
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Miguel Steiner
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Paul L. Türtscher
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Jan P. Unsleber
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Thomas Weymuth
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Markus Reiher
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
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19
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Piccini G, Lee MS, Yuk SF, Zhang D, Collinge G, Kollias L, Nguyen MT, Glezakou VA, Rousseau R. Ab initio molecular dynamics with enhanced sampling in heterogeneous catalysis. Catal Sci Technol 2022. [DOI: 10.1039/d1cy01329g] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Enhanced sampling ab initio simulations enable to study chemical phenomena in catalytic systems including thermal effects & anharmonicity, & collective dynamics describing enthalpic & entropic contributions, which can significantly impact on reaction free energy landscapes.
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Affiliation(s)
- GiovanniMaria Piccini
- Basic & Applied Molecular Foundations, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
- Istituto Eulero, Università della Svizzera italiana, Via Giuseppe Buffi 13, Lugano, Ticino, Switzerland
| | - Mal-Soon Lee
- Basic & Applied Molecular Foundations, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Simuck F. Yuk
- Basic & Applied Molecular Foundations, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
- Department of Chemistry and Life Science, United States Military Academy, West Point, NY 10996, USA
| | - Difan Zhang
- Basic & Applied Molecular Foundations, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Greg Collinge
- Basic & Applied Molecular Foundations, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Loukas Kollias
- Basic & Applied Molecular Foundations, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Manh-Thuong Nguyen
- Basic & Applied Molecular Foundations, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Vassiliki-Alexandra Glezakou
- Basic & Applied Molecular Foundations, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Roger Rousseau
- Basic & Applied Molecular Foundations, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
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20
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Bal KM. Reweighted Jarzynski Sampling: Acceleration of Rare Events and Free Energy Calculation with a Bias Potential Learned from Nonequilibrium Work. J Chem Theory Comput 2021; 17:6766-6774. [PMID: 34714088 DOI: 10.1021/acs.jctc.1c00574] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
We introduce a simple enhanced sampling approach for the calculation of free energy differences and barriers along a one-dimensional reaction coordinate. First, a small number of short nonequilibrium simulations are carried out along the reaction coordinate, and the Jarzynski equality is used to learn an approximate free energy surface from the nonequilibrium work distribution. This free energy estimate is represented in a compact form as an artificial neural network and used as an external bias potential to accelerate rare events in a subsequent molecular dynamics simulation. The final free energy estimate is then obtained by reweighting the equilibrium probability distribution of the reaction coordinate sampled under the influence of the external bias. We apply our reweighted Jarzynski sampling recipe to four processes of varying scales and complexities─spanning chemical reaction in the gas phase, pair association in solution, and droplet nucleation in supersaturated vapor. In all cases, we find reweighted Jarzynski sampling to be a very efficient strategy, resulting in rapid convergence of the free energy to high precision.
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Affiliation(s)
- Kristof M Bal
- Department of Chemistry and NANOlab Center of Excellence, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium
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21
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Bonati L, Piccini G, Parrinello M. Deep learning the slow modes for rare events sampling. Proc Natl Acad Sci U S A 2021; 118:e2113533118. [PMID: 34706940 PMCID: PMC8612227 DOI: 10.1073/pnas.2113533118] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/19/2021] [Indexed: 02/08/2023] Open
Abstract
The development of enhanced sampling methods has greatly extended the scope of atomistic simulations, allowing long-time phenomena to be studied with accessible computational resources. Many such methods rely on the identification of an appropriate set of collective variables. These are meant to describe the system's modes that most slowly approach equilibrium under the action of the sampling algorithm. Once identified, the equilibration of these modes is accelerated by the enhanced sampling method of choice. An attractive way of determining the collective variables is to relate them to the eigenfunctions and eigenvalues of the transfer operator. Unfortunately, this requires knowing the long-term dynamics of the system beforehand, which is generally not available. However, we have recently shown that it is indeed possible to determine efficient collective variables starting from biased simulations. In this paper, we bring the power of machine learning and the efficiency of the recently developed on the fly probability-enhanced sampling method to bear on this approach. The result is a powerful and robust algorithm that, given an initial enhanced sampling simulation performed with trial collective variables or generalized ensembles, extracts transfer operator eigenfunctions using a neural network ansatz and then accelerates them to promote sampling of rare events. To illustrate the generality of this approach, we apply it to several systems, ranging from the conformational transition of a small molecule to the folding of a miniprotein and the study of materials crystallization.
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Affiliation(s)
- Luigi Bonati
- Department of Physics, Eidgenössische Technische Hochschule (ETH) Zürich, 8092 Zürich, Switzerland;
- Atomistic Simulations, Italian Institute of Technology, 16163 Genova, Italy
| | | | - Michele Parrinello
- Atomistic Simulations, Italian Institute of Technology, 16163 Genova, Italy;
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22
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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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23
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Abstract
The determination of efficient collective variables is crucial to the success of many enhanced sampling methods. As inspired by previous discrimination approaches, we first collect a set of data from the different metastable basins. The data are then projected with the help of a neural network into a low-dimensional manifold in which data from different basins are well-discriminated. This is here guaranteed by imposing that the projected data follows a preassigned distribution. The collective variables thus obtained lead to an efficient sampling and often allow reducing the number of collective variables in a multibasin scenario. We first check the validity of the method in two-state systems. We then move to multistep chemical processes. In the latter case, at variance with previous approaches, one single collective variable suffices, leading not only to computational efficiency but also to a very clear representation of the reaction free-energy profile.
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Affiliation(s)
- Enrico Trizio
- Atomistic Simulations, Istituto Italiano di Tecnologia, 16163 Genova, Italy
- Department of Materials Science, Università di Milano-Bicocca, 20126 Milano, Italy
| | - Michele Parrinello
- Atomistic Simulations, Istituto Italiano di Tecnologia, 16163 Genova, Italy
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24
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Bernetti M, Bertazzo M, Masetti M. Data-Driven Molecular Dynamics: A Multifaceted Challenge. Pharmaceuticals (Basel) 2020; 13:E253. [PMID: 32961909 PMCID: PMC7557855 DOI: 10.3390/ph13090253] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 09/14/2020] [Accepted: 09/16/2020] [Indexed: 12/18/2022] Open
Abstract
The big data concept is currently revolutionizing several fields of science including drug discovery and development. While opening up new perspectives for better drug design and related strategies, big data analysis strongly challenges our current ability to manage and exploit an extraordinarily large and possibly diverse amount of information. The recent renewal of machine learning (ML)-based algorithms is key in providing the proper framework for addressing this issue. In this respect, the impact on the exploitation of molecular dynamics (MD) simulations, which have recently reached mainstream status in computational drug discovery, can be remarkable. Here, we review the recent progress in the use of ML methods coupled to biomolecular simulations with potentially relevant implications for drug design. Specifically, we show how different ML-based strategies can be applied to the outcome of MD simulations for gaining knowledge and enhancing sampling. Finally, we discuss how intrinsic limitations of MD in accurately modeling biomolecular systems can be alleviated by including information coming from experimental data.
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Affiliation(s)
- Mattia Bernetti
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), via Bonomea 265, I-34136 Trieste, Italy;
| | - Martina Bertazzo
- Computational Sciences, Istituto Italiano di Tecnologia, via Morego 30, I-16163 Genova, Italy;
| | - Matteo Masetti
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum—Università di Bologna, via Belmeloro 6, I-40126 Bologna, Italy
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25
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Debnath J, Parrinello M. Gaussian Mixture-Based Enhanced Sampling for Statics and Dynamics. J Phys Chem Lett 2020; 11:5076-5080. [PMID: 32510225 DOI: 10.1021/acs.jpclett.0c01125] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
We introduce an enhanced sampling method that is based on constructing a model probability density from which a bias potential is derived. The model relies on the fact that in a physical system most of the configurations visited can be grouped into isolated metastable islands. With each island we associate a distribution that is fitted to a Gaussian mixture. The different distributions are linearly combined together with coefficients that are computed self-consistently. This leads to an integrated procedure for discovering new metastable states, exploring reaction pathways, computing free energy differences, and estimating reaction rates.
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Affiliation(s)
- Jayashrita Debnath
- Department of Chemistry and Applied Biosciences, ETH Zürich, c/o USI Campus, Via Giuseppe Buffi 13, CH-6900 Lugano, Switzerland
- Facoltà di Informatica, Istituto di Scienze Computazionali, Universitá della Svizzera Italiana (USI), Via Giuseppe Buffi 13, CH-6900 Lugano, Switzerland
| | - Michele Parrinello
- Department of Chemistry and Applied Biosciences, ETH Zürich, c/o USI Campus, Via Giuseppe Buffi 13, CH-6900 Lugano, Switzerland
- Facoltà di Informatica, Istituto di Scienze Computazionali, Universitá della Svizzera Italiana (USI), Via Giuseppe Buffi 13, CH-6900 Lugano, Switzerland
- Instituto Italiano di Tecnologia, Via Morego 30, 16163 Genoa, Italy
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26
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Bottaro S, Nichols PJ, Vögeli B, Parrinello M, Lindorff-Larsen K. Integrating NMR and simulations reveals motions in the UUCG tetraloop. Nucleic Acids Res 2020; 48:5839-5848. [PMID: 32427326 PMCID: PMC7293013 DOI: 10.1093/nar/gkaa399] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Revised: 04/03/2020] [Accepted: 05/17/2020] [Indexed: 12/21/2022] Open
Abstract
We provide an atomic-level description of the structure and dynamics of the UUCG RNA stem-loop by combining molecular dynamics simulations with experimental data. The integration of simulations with exact nuclear Overhauser enhancements data allowed us to characterize two distinct states of this molecule. The most stable conformation corresponds to the consensus three-dimensional structure. The second state is characterized by the absence of the peculiar non-Watson-Crick interactions in the loop region. By using machine learning techniques we identify a set of experimental measurements that are most sensitive to the presence of non-native states. We find that although our MD ensemble, as well as the consensus UUCG tetraloop structures, are in good agreement with experiments, there are remaining discrepancies. Together, our results show that (i) the MD simulation overstabilize a non-native loop conformation, (ii) eNOE data support its presence with a population of ≈10% and (iii) the structural interpretation of experimental data for dynamic RNAs is highly complex, even for a simple model system such as the UUCG tetraloop.
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Affiliation(s)
- Sandro Bottaro
- Atomistic Simulations Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
- Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Parker J Nichols
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Beat Vögeli
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Michele Parrinello
- Atomistic Simulations Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
| | - Kresten Lindorff-Larsen
- Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark
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27
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Bonati L, Rizzi V, Parrinello M. Data-Driven Collective Variables for Enhanced Sampling. J Phys Chem Lett 2020; 11:2998-3004. [PMID: 32239945 DOI: 10.1021/acs.jpclett.0c00535] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Designing an appropriate set of collective variables is crucial to the success of several enhanced sampling methods. Here we focus on how to obtain such variables from information limited to the metastable states. We characterize these states by a large set of descriptors and employ neural networks to compress this information in a lower-dimensional space, using Fisher's linear discriminant as an objective function to maximize the discriminative power of the network. We test this method on alanine dipeptide, using the nonlinearly separable data set composed by atomic distances. We then study an intermolecular aldol reaction characterized by a concerted mechanism. The resulting variables are able to promote sampling by drawing nonlinear paths in the physical space connecting the fluctuations between metastable basins. Lastly, we interpret the behavior of the neural network by studying its relation to the physical variables. Through the identification of its most relevant features, we are able to gain chemical insight into the process.
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Affiliation(s)
- Luigi Bonati
- Department of Physics, ETH Zurich, 8092 Zurich, Switzerland
- Institute of Computational Sciences, Università della Svizzera italiana, via Buffi 13, 6900 Lugano, Switzerland
| | - Valerio Rizzi
- Institute of Computational Sciences, Università della Svizzera italiana, via Buffi 13, 6900 Lugano, Switzerland
- Department of Chemistry and Applied Biosciences, ETH Zurich, 8092 Zurich, Switzerland
| | - Michele Parrinello
- Institute of Computational Sciences, Università della Svizzera italiana, via Buffi 13, 6900 Lugano, Switzerland
- Department of Chemistry and Applied Biosciences, ETH Zurich, 8092 Zurich, Switzerland
- Italian Institute of Technology, Via Morego 30, 16163 Genova, Italy
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28
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Sidky H, Chen W, Ferguson AL. Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation. Mol Phys 2020. [DOI: 10.1080/00268976.2020.1737742] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Hythem Sidky
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, USA
| | - Wei Chen
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Andrew L. Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, USA
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29
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Sheka EF. Graphene Oxyhydride Catalysts in View of Spin Radical Chemistry. MATERIALS 2020; 13:ma13030565. [PMID: 31991653 PMCID: PMC7040773 DOI: 10.3390/ma13030565] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 01/15/2020] [Accepted: 01/19/2020] [Indexed: 11/21/2022]
Abstract
This article discusses carbocatalysis that are provided with amorphous carbons. The discussion is conducted from the standpoint of the spin chemistry of graphene molecules, in the framework of which the amorphous carbocatalysts are a conglomerate of graphene-oxynitrothiohydride stable radicals presenting the basic structure units (BSUs) of the species. The chemical activity of the BSUs atoms is reliably determined computationally, which allows mapping the distribution of active sites in these molecular catalysts. The presented maps reliably show the BSUs radicalization provided with carbon atoms only, the nonterminated edge part of which presents a set of active sites. Spin mapping of carbocatalysts active sites is suggested as the first step towards the spin carbocatalysis of the species.
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Affiliation(s)
- Elena F Sheka
- Institute of Physical Researches and Technologies, Peoples' Friendship University of Russia (RUDN University), Miklukho-Maklaya 6, 117198 Moscow, Russia
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30
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Ponte F, Piccini G, Sicilia E, Parrinello M. A metadynamics perspective on the reduction mechanism of the Pt(IV) asplatin prodrug. J Comput Chem 2019; 41:290-294. [PMID: 31691997 DOI: 10.1002/jcc.26100] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 10/16/2019] [Accepted: 10/17/2019] [Indexed: 11/07/2022]
Abstract
Enhanced sampling molecular dynamics has been used to model the reduction mechanism of the antitumoral Asplatin Pt(IV) complex, c,c,t-[PtCl2(NH3)2(OH)(aspirin)] in the presence of l-ascorbic acid as reducing agent. In order to overcome the timescale problem, characteristic of many chemical reactions, we enhanced the sampling of the free energy landscape using Metadynamics. To achieve such a goal, the selection of adequate collective variables is crucial for the application of the method. Recently, a new method called Multi-Class Harmonic Linear Discriminant Analysis (MC-HLDA) has been proposed as a tool for constructing collective variables (CVs) for complex chemical processes. The method reduces the dimensionality of the variable space by generating appropriate linear combinations of several relevant chemical descriptors. The aim of this work is to assess the ability and performance of this method in describing the fundamental features of complex chemical reactions such as the Asplatin reduction mechanism in a compact, simple, and physically transparent manner. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- Fortuna Ponte
- Dipartimento di Chimica e Tecnologie Chimiche, Università della Calabria, Ponte P. Bucci, Cubo 14C, Arcavacata di Rende, 87030, Italy
| | - GiovanniMaria Piccini
- Department of Chemistry and Applied Biosciences, ETH Zurich, c/o USI Campus, Via Giuseppe Buffi 13, Lugano, 6900, Switzerland.,Facoltà di Informatica, Istituto di Scienze Computazionali, Università della SvizzeraItaliana (USI), Via Giuseppe Buffi 13, Lugano, 6900, Switzerland
| | - Emilia Sicilia
- Dipartimento di Chimica e Tecnologie Chimiche, Università della Calabria, Ponte P. Bucci, Cubo 14C, Arcavacata di Rende, 87030, Italy
| | - Michele Parrinello
- Department of Chemistry and Applied Biosciences, ETH Zurich, c/o USI Campus, Via Giuseppe Buffi 13, Lugano, 6900, Switzerland.,Facoltà di Informatica, Istituto di Scienze Computazionali, Università della SvizzeraItaliana (USI), Via Giuseppe Buffi 13, Lugano, 6900, Switzerland.,Istituto Italiano di Tecnologia, Via Morego 30, Genova, 16163, Italy
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31
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Yang YI, Shao Q, Zhang J, Yang L, Gao YQ. Enhanced sampling in molecular dynamics. J Chem Phys 2019; 151:070902. [PMID: 31438687 DOI: 10.1063/1.5109531] [Citation(s) in RCA: 177] [Impact Index Per Article: 35.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Although molecular dynamics simulations have become a useful tool in essentially all fields of chemistry, condensed matter physics, materials science, and biology, there is still a large gap between the time scale which can be reached in molecular dynamics simulations and that observed in experiments. To address the problem, many enhanced sampling methods were introduced, which effectively extend the time scale being approached in simulations. In this perspective, we review a variety of enhanced sampling methods. We first discuss collective-variables-based methods including metadynamics and variationally enhanced sampling. Then, collective variable free methods such as parallel tempering and integrated tempering methods are presented. At last, we conclude with a brief introduction of some newly developed combinatory methods. We summarize in this perspective not only the theoretical background and numerical implementation of these methods but also the new challenges and prospects in the field of the enhanced sampling.
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Affiliation(s)
- Yi Isaac Yang
- Institute of Systems Biology, Shenzhen Bay Laboratory, Shenzhen 518055, Guangdong, China
| | - Qiang Shao
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
| | - Jun Zhang
- Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, Berlin 14195, Germany
| | - Lijiang Yang
- Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Yi Qin Gao
- Institute of Systems Biology, Shenzhen Bay Laboratory, Shenzhen 518055, Guangdong, China
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32
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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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33
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Piccini G, Parrinello M. Accurate Quantum Chemical Free Energies at Affordable Cost. J Phys Chem Lett 2019; 10:3727-3731. [PMID: 31244270 DOI: 10.1021/acs.jpclett.9b01301] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Free energy sampling methods allow studying the full dynamics of activated processes. Unfortunately, the affordable accuracy of the potential describing the energy and forces of the system is usually rather low. Here we introduce a new method that by combining metadynamics and free energy perturbation allows calculating accurate quantum chemical free energies for chemical reactions. To prove the effectiveness of this new approach we study the SN2 reaction of CH3F + Cl- → CH3Cl + F- in vacuo and solvated by water. Comparisons are made with harmonic transition-state theory to show how this method could provide accurate equilibrium and rate constants for complex systems.
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Affiliation(s)
- GiovanniMaria Piccini
- Department of Chemistry and Applied Biosciences , ETH Zurich , c/o USI Campus, Via Giuseppe Buffi 13 , CH-6900 Lugano , Switzerland
- Facoltà di Informatica, Istituto di Scienze Computazionali , Università della SvizzeraItaliana (USI) , Via Giuseppe Buffi 13 , CH-6900 Lugano , Switzerland
| | - Michele Parrinello
- Department of Chemistry and Applied Biosciences , ETH Zurich , c/o USI Campus, Via Giuseppe Buffi 13 , CH-6900 Lugano , Switzerland
- Facoltà di Informatica, Istituto di Scienze Computazionali , Università della SvizzeraItaliana (USI) , Via Giuseppe Buffi 13 , CH-6900 Lugano , Switzerland
- Istituto Italiano di Tecnologia , Via Morego 30 , 16163 Genova , Italy
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34
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Capelli R, Bochicchio A, Piccini G, Casasnovas R, Carloni P, Parrinello M. Chasing the Full Free Energy Landscape of Neuroreceptor/Ligand Unbinding by Metadynamics Simulations. J Chem Theory Comput 2019; 15:3354-3361. [DOI: 10.1021/acs.jctc.9b00118] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Riccardo Capelli
- INM-9/IAS-5 Computational Biomedicine, Forschungszentrum Jülich, Wilhelm-Johnen-Straße, D-54245 Jülich, Germany
| | - Anna Bochicchio
- INM-9/IAS-5 Computational Biomedicine, Forschungszentrum Jülich, Wilhelm-Johnen-Straße, D-54245 Jülich, Germany
| | - GiovanniMaria Piccini
- Department of Chemistry and Applied Biosciences, ETH Zürich, 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
| | - Rodrigo Casasnovas
- INM-9/IAS-5 Computational Biomedicine, Forschungszentrum Jülich, Wilhelm-Johnen-Straße, D-54245 Jülich, Germany
- JARA-HPC, Forschungszentrum Jülich, D-54245 Jülich, Germany
| | - Paolo Carloni
- INM-9/IAS-5 Computational Biomedicine, Forschungszentrum Jülich, Wilhelm-Johnen-Straße, D-54245 Jülich, Germany
- Department of Physics, RWTH Aachen University, D-52078 Aachen, Germany
| | - Michele Parrinello
- Department of Chemistry and Applied Biosciences, ETH Zürich, 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, Via Morego 30, I-16163 Genova, Italy
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35
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Zhang YY, Niu H, Piccini G, Mendels D, Parrinello M. Improving collective variables: The case of crystallization. J Chem Phys 2019; 150:094509. [PMID: 30849916 DOI: 10.1063/1.5081040] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Several enhanced sampling methods, such as umbrella sampling or metadynamics, rely on the identification of an appropriate set of collective variables. Recently two methods have been proposed to alleviate the task of determining efficient collective variables. One is based on linear discriminant analysis; the other is based on a variational approach to conformational dynamics and uses time-lagged independent component analysis. In this paper, we compare the performance of these two approaches in the study of the homogeneous crystallization of two simple metals. We focus on Na and Al and search for the most efficient collective variables that can be expressed as a linear combination of X-ray diffraction peak intensities. We find that the performances of the two methods are very similar. Wherever the different metastable states are well-separated, the method based on linear discriminant analysis, based on its harmonic version, is to be preferred because simpler to implement and less computationally demanding. The variational approach, however, has the potential to discover the existence of different metastable states.
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Affiliation(s)
- Yue-Yu Zhang
- Department of Chemistry and Applied Biosciences, ETH Zurich, c/o USI Campus, Via Giuseppe Buffi 13, CH-6900 Lugano, Ticino, Switzerland
| | - Haiyang Niu
- Department of Chemistry and Applied Biosciences, ETH Zurich, c/o USI Campus, Via Giuseppe Buffi 13, CH-6900 Lugano, Ticino, Switzerland
| | - GiovanniMaria Piccini
- Department of Chemistry and Applied Biosciences, ETH Zurich, c/o USI Campus, 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
| | - Michele Parrinello
- Department of Chemistry and Applied Biosciences, ETH Zurich, c/o USI Campus, Via Giuseppe Buffi 13, CH-6900 Lugano, Ticino, Switzerland
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36
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Danese M, Bon M, Piccini G, Passerone D. The reaction mechanism of the azide–alkyne Huisgen cycloaddition. Phys Chem Chem Phys 2019; 21:19281-19287. [DOI: 10.1039/c9cp02386k] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
By means of computational methods to sample reaction free energies, this paper provides novel insights into the Huisgen cycloaddition mechanism.
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Affiliation(s)
- Martina Danese
- Empa, Swiss Federal Laboratories for Materials Science and Technology
- Dübendorf
- Switzerland
| | - Marta Bon
- Empa, Swiss Federal Laboratories for Materials Science and Technology
- Dübendorf
- Switzerland
| | | | - Daniele Passerone
- Empa, Swiss Federal Laboratories for Materials Science and Technology
- Dübendorf
- Switzerland
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37
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Mendels D, Piccini G, Brotzakis ZF, Yang YI, Parrinello M. Folding a small protein using harmonic linear discriminant analysis. J Chem Phys 2018; 149:194113. [DOI: 10.1063/1.5053566] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Dan Mendels
- Department of Chemistry and Applied Biosciences, ETH Zurich, c/o USI Campus, Via Giuseppe Buffi 13, CH-6900 Lugano, Ticino, Switzerland
- Facoltá di Informatica, Istituto di Scienze Computazionali, Universitá della Svizzera italiana (USI), Via Giuseppe Buffi 13, CH-6900 Lugano, Ticino, Switzerland
| | - Giovannimaria Piccini
- Department of Chemistry and Applied Biosciences, ETH Zurich, c/o USI Campus, Via Giuseppe Buffi 13, CH-6900 Lugano, Ticino, Switzerland
- Facoltá di Informatica, Istituto di Scienze Computazionali, Universitá della Svizzera italiana (USI), Via Giuseppe Buffi 13, CH-6900 Lugano, Ticino, Switzerland
| | - Z. Faidon Brotzakis
- Department of Chemistry and Applied Biosciences, ETH Zurich, c/o USI Campus, Via Giuseppe Buffi 13, CH-6900 Lugano, Ticino, Switzerland
- Facoltá di Informatica, Istituto di Scienze Computazionali, Universitá della Svizzera italiana (USI), Via Giuseppe Buffi 13, CH-6900 Lugano, Ticino, Switzerland
| | - Yi I. Yang
- Department of Chemistry and Applied Biosciences, ETH Zurich, c/o USI Campus, Via Giuseppe Buffi 13, CH-6900 Lugano, Ticino, Switzerland
- Facoltá di Informatica, Istituto di Scienze Computazionali, Universitá della Svizzera italiana (USI), Via Giuseppe Buffi 13, CH-6900 Lugano, Ticino, Switzerland
| | - Michele Parrinello
- Department of Chemistry and Applied Biosciences, ETH Zurich, c/o USI Campus, Via Giuseppe Buffi 13, CH-6900 Lugano, Ticino, Switzerland
- Facoltá di Informatica, Istituto di Scienze Computazionali, Universitá della Svizzera italiana (USI), Via Giuseppe Buffi 13, CH-6900 Lugano, Ticino, Switzerland
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