1
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Dong J, Wang S, Cui W, Sun X, Guo H, Yan H, Vogel H, Wang Z, Yuan S. Machine Learning Deciphered Molecular Mechanistics with Accurate Kinetic and Thermodynamic Prediction. J Chem Theory Comput 2024; 20:4499-4513. [PMID: 38394691 DOI: 10.1021/acs.jctc.3c01412] [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: 02/25/2024]
Abstract
Time-lagged independent component analysis (tICA) and the Markov state model (MSM) have been extensively employed for extracting conformational dynamics and kinetic community networks from unbiased trajectory ensembles. However, these techniques may not be the optimal choice for elucidating transition mechanisms within low-dimensional representations, especially for intricate biosystems. Unraveling the association mechanism in such complex systems always necessitates permutations of several essential independent components or collective variables, a process that is inherently obscure and may require empirical knowledge for selection. To address these challenges, we have implemented an integrated unsupervised dimension reduction model: uniform manifold approximation and projection (UMAP) with hierarchy density-based spatial clustering of applications with noise (HDBSCAN). This approach effectively generates low-dimensional configurational embeddings. The hierarchical application of this architecture, in conjunction with MSM, reveals global kinetic connectivity while identifying local conformational states. Consequently, our methodology establishes a multiscale mechanistic elucidation framework. Leveraging the benefits of the uniform sample distribution and a denoising approach, our model demonstrates robustness in preserving global and local data structures compared to traditional dimension reduction methods in the field of MD analysis area. The interpretability of hyperparameter selection and compatibility with downstream tasks are cross-validated across various simulation data sets, utilizing both computational evaluation metrics and experimental kinetic observables. Furthermore, the predicted Mcl1-BH3 association kinetics (0.76 s-1) is in close agreement with surface plasmon resonance experiments (0.12 s-1), affirming the plausibility of the identified pathway composed of representative conformations. We anticipate that the devised workflow will serve as a foundational framework for studying recognition patterns in complex biological systems. Its contributions extend to the exploration of protein functional dynamics and rational drug design, offering a potent avenue for advancing research in these domains.
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Affiliation(s)
- Junlin Dong
- Research Center for Computer-Aided Drug Discovery, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shiyu Wang
- Research Center for Computer-Aided Drug Discovery, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- AlphaMol Science Ltd, Shenzhen 518055, China
| | - Wenqiang Cui
- Research Center for Computer-Aided Drug Discovery, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaolin Sun
- Research Center for Computer-Aided Drug Discovery, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Haojie Guo
- Research Center for Computer-Aided Drug Discovery, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Hailu Yan
- School of Biological Sciences, College of Science and Engineering, University of Edinburgh, Edinburgh EH8 9YL, U.K
| | - Horst Vogel
- Research Center for Computer-Aided Drug Discovery, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Zhi Wang
- Artificial Intelligence Department, Zhejiang Financial College, Hangzhou 310018, China
| | - Shuguang Yuan
- Research Center for Computer-Aided Drug Discovery, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- AlphaMol Science Ltd, Shenzhen 518055, China
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2
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Mehdi S, Smith Z, Herron L, Zou Z, Tiwary P. Enhanced Sampling with Machine Learning. Annu Rev Phys Chem 2024; 75:347-370. [PMID: 38382572 PMCID: PMC11213683 DOI: 10.1146/annurev-physchem-083122-125941] [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] [Indexed: 02/23/2024]
Abstract
Molecular dynamics (MD) enables the study of physical systems with excellent spatiotemporal resolution but suffers from severe timescale limitations. To address this, enhanced sampling methods have been developed to improve the exploration of configurational space. However, implementing these methods is challenging and requires domain expertise. In recent years, integration of machine learning (ML) techniques into different domains has shown promise, prompting their adoption in enhanced sampling as well. Although ML is often employed in various fields primarily due to its data-driven nature, its integration with enhanced sampling is more natural with many common underlying synergies. This review explores the merging of ML and enhanced MD by presenting different shared viewpoints. It offers a comprehensive overview of this rapidly evolving field, which can be difficult to stay updated on. We highlight successful strategies such as dimensionality reduction, reinforcement learning, and flow-based methods. Finally, we discuss open problems at the exciting ML-enhanced MD interface.
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Affiliation(s)
- Shams Mehdi
- Institute for Physical Science and Technology, University of Maryland, College Park, Maryland, USA;
- Biophysics Program, University of Maryland, College Park, Maryland, USA
| | - Zachary Smith
- Institute for Physical Science and Technology, University of Maryland, College Park, Maryland, USA;
- Biophysics Program, University of Maryland, College Park, Maryland, USA
| | - Lukas Herron
- Institute for Physical Science and Technology, University of Maryland, College Park, Maryland, USA;
- Biophysics Program, University of Maryland, College Park, Maryland, USA
| | - Ziyue Zou
- Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland, USA
| | - Pratyush Tiwary
- Institute for Physical Science and Technology, University of Maryland, College Park, Maryland, USA;
- Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland, USA
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3
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Müllender L, Rizzi A, Parrinello M, Carloni P, Mandelli D. Effective data-driven collective variables for free energy calculations from metadynamics of paths. PNAS NEXUS 2024; 3:pgae159. [PMID: 38665160 PMCID: PMC11044970 DOI: 10.1093/pnasnexus/pgae159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 04/04/2024] [Indexed: 04/28/2024]
Abstract
A variety of enhanced sampling (ES) methods predict multidimensional free energy landscapes associated with biological and other molecular processes as a function of a few selected collective variables (CVs). The accuracy of these methods is crucially dependent on the ability of the chosen CVs to capture the relevant slow degrees of freedom of the system. For complex processes, finding such CVs is the real challenge. Machine learning (ML) CVs offer, in principle, a solution to handle this problem. However, these methods rely on the availability of high-quality datasets-ideally incorporating information about physical pathways and transition states-which are difficult to access, therefore greatly limiting their domain of application. Here, we demonstrate how these datasets can be generated by means of ES simulations in trajectory space via the metadynamics of paths algorithm. The approach is expected to provide a general and efficient way to generate efficient ML-based CVs for the fast prediction of free energy landscapes in ES simulations. We demonstrate our approach with two numerical examples, a 2D model potential and the isomerization of alanine dipeptide, using deep targeted discriminant analysis as our ML-based CV of choice.
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Affiliation(s)
- Lukas Müllender
- Department of Applied Physics, Science for Life Laboratory, KTH Royal Institute of Technology, SE-171 21 Solna, Sweden
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
- Department of Physics, RWTH Aachen University, 52062 Aachen, Germany
| | - Andrea Rizzi
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
- Atomistic Simulations, Italian Institute of Technology, 16163 Genova, Italy
| | - Michele Parrinello
- Atomistic Simulations, Italian Institute of Technology, 16163 Genova, Italy
| | - Paolo Carloni
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
- Department of Physics, RWTH Aachen University, 52062 Aachen, Germany
- Universitätsklinikum, RWTH Aachen University, 52062 Aachen, Germany
| | - Davide Mandelli
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
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4
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Deng Y, Zhang Q, Feringa BL. Dynamic Chemistry Toolbox for Advanced Sustainable Materials. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308666. [PMID: 38321810 PMCID: PMC11005721 DOI: 10.1002/advs.202308666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/28/2023] [Indexed: 02/08/2024]
Abstract
Developing dynamic chemistry for polymeric materials offers chemical solutions to solve key problems associated with current plastics. Mechanical performance and dynamic function are equally important in material design because the former determines the application scope and the latter enables chemical recycling and hence sustainability. However, it is a long-term challenge to balance the subtle trade-off between mechanical robustness and dynamic properties in a single material. The rise of dynamic chemistry, including supramolecular and dynamic covalent chemistry, provides many opportunities and versatile molecular tools for designing constitutionally dynamic materials that can adapt, repair, and recycle. Facing the growing social need for developing advanced sustainable materials without compromising properties, recent progress showing how the toolbox of dynamic chemistry can be explored to enable high-performance sustainable materials by molecular engineering strategies is discussed here. The state of the art and recent milestones are summarized and discussed, followed by an outlook toward future opportunities and challenges present in this field.
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Affiliation(s)
- Yuanxin Deng
- Key Laboratory for Advanced Materials and Feringa Nobel Prize Scientist Joint Research CenterSchool of Chemistry and Technology130 Meilong RoadShanghai200237China
- Stratingh Institute for Chemistry and Zernike Institute for Advanced MaterialsFaculty of Science and EngineeringUniversity of GroningenNijenborgh 4Groningen9747 AGThe Netherlands
| | - Qi Zhang
- Key Laboratory for Advanced Materials and Feringa Nobel Prize Scientist Joint Research CenterSchool of Chemistry and Technology130 Meilong RoadShanghai200237China
- Stratingh Institute for Chemistry and Zernike Institute for Advanced MaterialsFaculty of Science and EngineeringUniversity of GroningenNijenborgh 4Groningen9747 AGThe Netherlands
| | - Ben L. Feringa
- Key Laboratory for Advanced Materials and Feringa Nobel Prize Scientist Joint Research CenterSchool of Chemistry and Technology130 Meilong RoadShanghai200237China
- Stratingh Institute for Chemistry and Zernike Institute for Advanced MaterialsFaculty of Science and EngineeringUniversity of GroningenNijenborgh 4Groningen9747 AGThe Netherlands
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5
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Ruiz Munevar M, Rizzi V, Portioli C, Vidossich P, Cao E, Parrinello M, Cancedda L, De Vivo M. Cation Chloride Cotransporter NKCC1 Operates through a Rocking-Bundle Mechanism. J Am Chem Soc 2024; 146:552-566. [PMID: 38146212 PMCID: PMC10786066 DOI: 10.1021/jacs.3c10258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 12/05/2023] [Accepted: 12/06/2023] [Indexed: 12/27/2023]
Abstract
The sodium, potassium, and chloride cotransporter 1 (NKCC1) plays a key role in tightly regulating ion shuttling across cell membranes. Lately, its aberrant expression and function have been linked to numerous neurological disorders and cancers, making it a novel and highly promising pharmacological target for therapeutic interventions. A better understanding of how NKCC1 dynamically operates would therefore have broad implications for ongoing efforts toward its exploitation as a therapeutic target through its modulation. Based on recent structural data on NKCC1, we reveal conformational motions that are key to its function. Using extensive deep-learning-guided atomistic simulations of NKCC1 models embedded into the membrane, we captured complex dynamical transitions between alternate open conformations of the inner and outer vestibules of the cotransporter and demonstrated that NKCC1 has water-permeable states. We found that these previously undefined conformational transitions occur via a rocking-bundle mechanism characterized by the cooperative angular motion of transmembrane helices (TM) 4 and 9, with the contribution of the extracellular tip of TM 10. We found these motions to be critical in modulating ion transportation and in regulating NKCC1's water transporting capabilities. Specifically, we identified interhelical dynamical contacts between TM 10 and TM 6, which we functionally validated through mutagenesis experiments of 4 new targeted NKCC1 mutants. We conclude showing that those 4 residues are highly conserved in most Na+-dependent cation chloride cotransporters (CCCs), which highlights their critical mechanistic implications, opening the way to new strategies for NKCC1's function modulation and thus to potential drug action on selected CCCs.
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Affiliation(s)
- Manuel
José Ruiz Munevar
- Laboratory
of Molecular Modelling & Drug Discovery, Istituto Italiano di Tecnologia, Via Morego 30, Genoa 16163, Italy
| | - Valerio Rizzi
- Biomolecular
& Pharmaceutical Modelling Group, Université
de Genève, Rue Michel-Servet 1, Geneva CH-1211 4, Switzerland
| | - Corinne Portioli
- Laboratory
of Nanotechnology for Precision Medicine, Istituto Italiano di Tecnologia, Via Morego 30, Genoa 16163, Italy
- Laboratory
of Brain Development and Disease, Istituto
Italiano di Tecnologia, Via Morego 30, Genoa 16163, Italy
| | - Pietro Vidossich
- Laboratory
of Molecular Modelling & Drug Discovery, Istituto Italiano di Tecnologia, Via Morego 30, Genoa 16163, Italy
| | - Erhu Cao
- Department
of Biochemistry, University of Utah School
of Medicine, Salt Lake City, Utah 84112-5650, United States
| | - Michele Parrinello
- Laboratory
of Atomistic Simulations, Istituto Italiano
di Tecnologia, Via Morego 30, Genoa 16163, Italy
| | - Laura Cancedda
- Laboratory
of Brain Development and Disease, Istituto
Italiano di Tecnologia, Via Morego 30, Genoa 16163, Italy
| | - Marco De Vivo
- Laboratory
of Molecular Modelling & Drug Discovery, Istituto Italiano di Tecnologia, Via Morego 30, Genoa 16163, Italy
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6
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Herringer NSM, Dasetty S, Gandhi D, Lee J, Ferguson AL. Permutationally Invariant Networks for Enhanced Sampling (PINES): Discovery of Multimolecular and Solvent-Inclusive Collective Variables. J Chem Theory Comput 2024; 20:178-198. [PMID: 38150421 DOI: 10.1021/acs.jctc.3c00923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
The typically rugged nature of molecular free-energy landscapes can frustrate efficient sampling of the thermodynamically relevant phase space due to the presence of high free-energy barriers. Enhanced sampling techniques can improve phase space exploration by accelerating sampling along particular collective variables (CVs). A number of techniques exist for the data-driven discovery of CVs parametrizing the important large-scale motions of the system. A challenge to CV discovery is learning CVs invariant to the symmetries of the molecular system, frequently rigid translation, rigid rotation, and permutational relabeling of identical particles. Of these, permutational invariance has proved a persistent challenge in frustrating the data-driven discovery of multimolecular CVs in systems of self-assembling particles and solvent-inclusive CVs for solvated systems. In this work, we integrate permutation invariant vector (PIV) featurizations with autoencoding neural networks to learn nonlinear CVs invariant to translation, rotation, and permutation and perform interleaved rounds of CV discovery and enhanced sampling to iteratively expand the sampling of configurational phase space and obtain converged CVs and free-energy landscapes. We demonstrate the permutationally invariant network for enhanced sampling (PINES) approach in applications to the self-assembly of a 13-atom argon cluster, association/dissociation of a NaCl ion pair in water, and hydrophobic collapse of a C45H92 n-pentatetracontane polymer chain. We make the approach freely available as a new module within the PLUMED2 enhanced sampling libraries.
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Affiliation(s)
| | - Siva Dasetty
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Diya Gandhi
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Junhee Lee
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
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7
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Fu H, Chipot C, Shao X, Cai W. Standard Binding Free-Energy Calculations: How Far Are We from Automation? J Phys Chem B 2023; 127:10459-10468. [PMID: 37824848 DOI: 10.1021/acs.jpcb.3c04370] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
Recent success stories suggest that in silico protein-ligand binding free-energy calculations are approaching chemical accuracy. However, their widespread application remains limited by the extensive human intervention required, posing challenges for the neophyte. As such, it is critical to develop automated workflows for estimating protein-ligand binding affinities with minimum personal involvement. Key human efforts include setting up and tuning enhanced-sampling or alchemical-transformation algorithms as a preamble to computational binding free-energy estimations. Additionally, preparing input files, bookkeeping, and postprocessing represent nontrivial tasks. In this Perspective, we discuss recent progress in automating standard binding free-energy calculations, featuring the development of adaptive or parameter-free algorithms, standardization of binding free-energy calculation workflows, and the implementation of user-friendly software. We also assess the current state of automated standard binding free-energy calculations and evaluate the limitations of existing methods. Last, we outline the requirements for future algorithms and workflows to facilitate automated free-energy calculations for diverse protein-ligand complexes.
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Affiliation(s)
- Haohao Fu
- State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Christophe Chipot
- Laboratoire International Associé CNRS and University of Illinois at Urbana-Champaign, UMR no. 7019, Université de Lorraine, BP 70239, F-54506 Vandoeuvre-lès-Nancy, France
- Department of Physics, University of Illinois at Urbana-Champaign, 1110 West Green Street, Urbana, Illinois 61801, United States
- Department of Chemistry, The University of Chicago, 5735 South Ellis Avenue, Chicago, Illinois 60637, United States
- Department of Chemistry, The University of Hawai'i at Ma̅noa, 2545 McCarthy Mall, Honolulu, Hawaii 96822, United States
| | - Xueguang Shao
- State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Wensheng Cai
- State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
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8
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Saar A, Ghahremanpour MM, Tirado-Rives J, Jorgensen WL. Assessing Metadynamics and Docking for Absolute Binding Free Energy Calculations Using Severe Acute Respiratory Syndrome Coronavirus 2 Main Protease Inhibitors. J Chem Inf Model 2023; 63:7210-7218. [PMID: 37934762 DOI: 10.1021/acs.jcim.3c01453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Abstract
Absolute binding free energy (ABFE) calculations can be an important part of the drug discovery process by identifying molecules that have the potential to be strong binders for a biomolecular target. Recent work has used free energy perturbation (FEP) theory for these calculations, focusing on a set of 16 inhibitors of the severe acute respiratory syndrome coronavirus 2 main protease (Mpro). Herein, the same data set is evaluated by metadynamics (MetaD), four different docking programs, and molecular mechanics with generalized Born and surface area solvation. MetaD yields a Kendall τ distance of 0.28 and Pearson r2 of 0.49, which reflect somewhat less accuracy than that from the ABFE FEP results. Notably, it is demonstrated that an ensemble docking protocol by which each ligand is docked into the 13 crystal structures in this data set provides improved performance, particularly when docking is carried out with Glide XP (Kendall τ distance = 0.20, Pearson r2 = 0.71), Glide SP (Kendall τ distance = 0.19, Pearson r2 = 0.66), or AutoDock 4 (Kendall τ distance = 0.21, Pearson r2 = 0.55). The best results are obtained with "superconsensus" docking by averaging the 52 results for each compound using the 4 docking protocols and all 13 crystal structures (Kendall τ distance = 0.18, Pearson r2 = 0.73).
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Affiliation(s)
- Anastasia Saar
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | | | - Julian Tirado-Rives
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - William L Jorgensen
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
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9
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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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10
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Rizzi V, Aureli S, Ansari N, Gervasio FL. OneOPES, a Combined Enhanced Sampling Method to Rule Them All. J Chem Theory Comput 2023; 19:5731-5742. [PMID: 37603295 PMCID: PMC10500989 DOI: 10.1021/acs.jctc.3c00254] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Indexed: 08/22/2023]
Abstract
Enhanced sampling techniques have revolutionized molecular dynamics (MD) simulations, enabling the study of rare events and the calculation of free energy differences in complex systems. One of the main families of enhanced sampling techniques uses physical degrees of freedom called collective variables (CVs) to accelerate a system's dynamics and recover the original system's statistics. However, encoding all the relevant degrees of freedom in a limited number of CVs is challenging, particularly in large biophysical systems. Another category of techniques, such as parallel tempering, simulates multiple replicas of the system in parallel, without requiring CVs. However, these methods may explore less relevant high-energy portions of the phase space and become computationally expensive for large systems. To overcome the limitations of both approaches, we propose a replica exchange method called OneOPES that combines the power of multireplica simulations and CV-based enhanced sampling. This method efficiently accelerates the phase space sampling without the need for ideal CVs, extensive parameters fine tuning nor the use of a large number of replicas, as demonstrated by its successful applications to protein-ligand binding and protein folding benchmark systems. Our approach shows promise as a new direction in the development of enhanced sampling techniques for molecular dynamics simulations, providing an efficient and robust framework for the study of complex and unexplored problems.
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Affiliation(s)
- Valerio Rizzi
- School
of Pharmaceutical Sciences, University of
Geneva, Rue Michel Servet 1, 1206 Genève, Switzerland
- Institute
of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, 1206 Genève, Switzerland
- Swiss
Institute of Bioinformatics, University
of Geneva, 1206 Genève, Switzerland
| | - Simone Aureli
- School
of Pharmaceutical Sciences, University of
Geneva, Rue Michel Servet 1, 1206 Genève, Switzerland
- Institute
of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, 1206 Genève, Switzerland
- Swiss
Institute of Bioinformatics, University
of Geneva, 1206 Genève, Switzerland
| | - Narjes Ansari
- Atomistic
Simulations, Italian Institute of Technology, Via Enrico Melen 83, 16152 Genova, Italy
| | - Francesco Luigi Gervasio
- School
of Pharmaceutical Sciences, University of
Geneva, Rue Michel Servet 1, 1206 Genève, Switzerland
- Institute
of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, 1206 Genève, Switzerland
- Swiss
Institute of Bioinformatics, University
of Geneva, 1206 Genève, Switzerland
- Department
of Chemistry, University College London, WC1E 6BT London, U.K.
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11
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Salehi SM, Pezzella M, Willard A, Meuwly M, Karplus M. Water dynamics around T 0 vs R 4 of hemoglobin from local hydrophobicity analysis. J Chem Phys 2023; 158:025101. [PMID: 36641390 DOI: 10.1063/5.0129990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The local hydration around tetrameric hemoglobin (Hb) in its T0 and R4 conformational substates is analyzed based on molecular dynamics simulations. Analysis of the local hydrophobicity (LH) for all residues at the α1β2 and α2β1 interfaces, responsible for the quaternary T → R transition, which is encoded in the Monod-Wyman-Changeux model, as well as comparison with earlier computations of the solvent accessible surface area, makes clear that the two quantities measure different aspects of hydration. Local hydrophobicity quantifies the presence and structure of water molecules at the interface, whereas "buried surface" reports on the available space for solvent. For simulations with Hb frozen in its T0 and R4 states, the correlation coefficient between LH and buried surface is 0.36 and 0.44, respectively, but it increases considerably if the 95% confidence interval is used. The LH with Hb frozen and flexible changes little for most residues at the interfaces but is significantly altered for a few select ones: Thr41α, Tyr42α, Tyr140α, Trp37β, Glu101β (for T0) and Thr38α, Tyr42α, Tyr140α (for R4). The number of water molecules at the interface is found to increase by ∼25% for T0 → R4, which is consistent with earlier measurements. Since hydration is found to be essential to protein function, it is clear that hydration also plays an essential role in allostery.
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Affiliation(s)
- Seyedeh Maryam Salehi
- Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
| | - Marco Pezzella
- Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
| | - Adam Willard
- Department of Chemistry MIT, Cambridge, Massachusetts 02139, USA
| | - Markus Meuwly
- Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
| | - Martin Karplus
- Department of Chemistry, Harvard University, Cambridge, Massachusetts 02138, USA
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12
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Zhang S, Li W, Luan J, Srivastava A, Carnevale V, Klein ML, Sun J, Wang D, Teora SP, Rijpkema SJ, Meeldijk JD, Wilson DA. Adaptive insertion of a hydrophobic anchor into a poly(ethylene glycol) host for programmable surface functionalization. Nat Chem 2023; 15:240-247. [PMID: 36411361 PMCID: PMC9899690 DOI: 10.1038/s41557-022-01090-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 10/17/2022] [Indexed: 11/22/2022]
Abstract
Covalent and non-covalent molecular binding are two strategies to tailor surface properties and functions. However, the lack of responsiveness and requirement for specific binding groups makes spatiotemporal control challenging. Here, we report the adaptive insertion of a hydrophobic anchor into a poly(ethylene glycol) (PEG) host as a non-covalent binding strategy for surface functionalization. By using polycyclic aromatic hydrocarbons as the hydrophobic anchor, hydrophilic charged and non-charged functional modules were spontaneously loaded onto PEG corona in 2 min without the assistance of any catalysts and binding groups. The thermodynamically favourable insertion of the hydrophobic anchor can be reversed by pulling the functional module, enabling programmable surface functionalization. We anticipate that the adaptive molecular recognition between the hydrophobic anchor and the PEG host will challenge the hydrophilic understanding of PEG and enhance the progress in nanomedicine, advanced materials and nanotechnology.
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Affiliation(s)
- Shaohua Zhang
- grid.5590.90000000122931605Institute for Molecules and Materials, Radboud University, Nijmegen, the Netherlands
| | - Wei Li
- grid.5590.90000000122931605Institute for Molecules and Materials, Radboud University, Nijmegen, the Netherlands
| | - Jiabin Luan
- grid.5590.90000000122931605Institute for Molecules and Materials, Radboud University, Nijmegen, the Netherlands
| | - Abhinav Srivastava
- grid.264727.20000 0001 2248 3398Institute for Genomics and Evolutionary Medicine (iGEM) and Department of Biology, Temple University, Philadelphia, PA USA ,grid.264727.20000 0001 2248 3398Institute for Computational Molecular Science, Temple University, Philadelphia, PA USA
| | - Vincenzo Carnevale
- grid.264727.20000 0001 2248 3398Institute for Genomics and Evolutionary Medicine (iGEM) and Department of Biology, Temple University, Philadelphia, PA USA ,grid.264727.20000 0001 2248 3398Institute for Computational Molecular Science, Temple University, Philadelphia, PA USA
| | - Michael L. Klein
- grid.264727.20000 0001 2248 3398Institute for Computational Molecular Science, Temple University, Philadelphia, PA USA
| | - Jiawei Sun
- grid.5590.90000000122931605Institute for Molecules and Materials, Radboud University, Nijmegen, the Netherlands
| | - Danni Wang
- grid.5590.90000000122931605Institute for Molecules and Materials, Radboud University, Nijmegen, the Netherlands
| | - Serena P. Teora
- grid.5590.90000000122931605Institute for Molecules and Materials, Radboud University, Nijmegen, the Netherlands
| | - Sjoerd J. Rijpkema
- grid.5590.90000000122931605Institute for Molecules and Materials, Radboud University, Nijmegen, the Netherlands
| | - Johannes D. Meeldijk
- grid.5477.10000000120346234Inorganic Chemistry and Catalysis, Debye Institute for Nanomaterials Science, Utrecht University, Utrecht, the Netherlands
| | - Daniela A. Wilson
- grid.5590.90000000122931605Institute for Molecules and Materials, Radboud University, Nijmegen, the Netherlands
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13
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Zhang S, Jia Y, Liu J, Feng F, Wei Z, Zhang M, Xu F. A viscoelastic alginate-based hydrogel network coordinated with spermidine for periodontal ligament regeneration. Regen Biomater 2023; 10:rbad009. [PMID: 36923559 PMCID: PMC10010660 DOI: 10.1093/rb/rbad009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 01/11/2023] [Accepted: 02/02/2023] [Indexed: 02/16/2023] Open
Abstract
Periodontitis can cause irreversible defects in the periodontal ligament (PDL), the regeneration of which is the major obstacle to the clinical treatment of periodontitis. Implanting hydrogel for releasing anti-inflammatory drugs is a promising treatment to promote PDL regeneration. However, existing hydrogel systems fail to mimic the typical viscoelastic feature of native periodontium, which may have been shown as an important role in tissue regeneration. Meanwhile, the synergistic benefits of mechanical cues and biochemical agents for PDL regeneration remain elusive. In this study, we developed a bi-crosslinking viscoelastic hydrogel (Alg-PBA/Spd) by integrating phenylboronic acid-modified alginate with anti-inflammatory agent (spermidine) through borate ester and B-N coordination bonds, where spermidine will be released with the degradation of the hydrogel. Alg-PBA/Spd hydrogel is biocompatible, injectable and can quickly adapt to complex periodontal structures due to the dynamic crosslinking. We demonstrated in rat models that the viscoelastic Alg-PBA/Spd hydrogel significantly promotes the deposition of periodontal collagen and accelerates the repair of periodontal damage. Our results suggest that the viscoelastic Alg-PBA/Spd hydrogel would be a promising mechano-biochemically synergistic treatment for periodontal regeneration.
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Affiliation(s)
- Songbai Zhang
- State Key Laboratory of Military Stomatology & National Clinical Research Center for Oral Diseases & Shaanxi International Joint Research Center for Oral Diseases, Department of General Dentistry and Emergency, School of Stomatology, Fourth Military Medical University, Xi'an 710032, P.R. China.,The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China.,Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China
| | - Yuanbo Jia
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China.,Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China
| | - Jingyi Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China.,Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China
| | - Fan Feng
- State Key Laboratory of Military Stomatology & National Clinical Research Center for Oral Diseases & Shaanxi International Joint Research Center for Oral Diseases, Department of General Dentistry and Emergency, School of Stomatology, Fourth Military Medical University, Xi'an 710032, P.R. China.,The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China.,Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China
| | - Zhao Wei
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China.,Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China
| | - Min Zhang
- State Key Laboratory of Military Stomatology & National Clinical Research Center for Oral Diseases & Shaanxi International Joint Research Center for Oral Diseases, Department of General Dentistry and Emergency, School of Stomatology, Fourth Military Medical University, Xi'an 710032, P.R. China
| | - Feng Xu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China.,Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China
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14
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Lukauskis D, Samways ML, Aureli S, Cossins BP, Taylor RD, Gervasio FL. Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein-Ligand Binding Poses. J Chem Inf Model 2022; 62:6209-6216. [PMID: 36401553 DOI: 10.1021/acs.jcim.2c01142] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Predicting the correct pose of a ligand binding to a protein and its associated binding affinity is of great importance in computer-aided drug discovery. A number of approaches have been developed to these ends, ranging from the widely used fast molecular docking to the computationally expensive enhanced sampling molecular simulations. In this context, methods such as coarse-grained metadynamics and binding pose metadynamics (BPMD) use simulations with metadynamics biasing to probe the binding affinity without trying to fully converge the binding free energy landscape in order to decrease the computational cost. In BPMD, the metadynamics bias perturbs the ligand away from the initial pose. The resistance of the ligand to this bias is used to calculate a stability score. The method has been shown to be useful in reranking predicted binding poses from docking. Here, we present OpenBPMD, an open-source Python reimplementation and reinterpretation of BPMD. OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. We also investigated the role of accurate water positioning on the performance of the algorithm and showed how the combination with a grand-canonical Monte Carlo algorithm improves the accuracy of the predictions.
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Affiliation(s)
- Dominykas Lukauskis
- Department of Chemistry, University College London, LondonWC1E 6BT, United Kingdom
| | | | - Simone Aureli
- Biomolecular and Pharmaceutical Modelling Group, School of Pharmaceutical Sciences, University of Geneva, CH1211Geneva, Switzerland.,Institute of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, CH1211Geneva, Switzerland
| | - Benjamin P Cossins
- UCB, 216 Bath Road, SloughSL1 3WE, United Kingdom.,Exscientia Ltd., The Schrödinger Building, Oxford Science Park, OxfordOX4 4GE, United Kingdom
| | | | - Francesco Luigi Gervasio
- Department of Chemistry, University College London, LondonWC1E 6BT, United Kingdom.,Biomolecular and Pharmaceutical Modelling Group, School of Pharmaceutical Sciences, University of Geneva, CH1211Geneva, Switzerland.,Institute of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, CH1211Geneva, Switzerland.,UCB, 216 Bath Road, SloughSL1 3WE, United Kingdom
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15
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Single-Molecule Chemical Reactions Unveiled in Molecular Junctions. Processes (Basel) 2022. [DOI: 10.3390/pr10122574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022] Open
Abstract
Understanding chemical processes at the single-molecule scale represents the ultimate limit of analytical chemistry. Single-molecule detection techniques allow one to reveal the detailed dynamics and kinetics of a chemical reaction with unprecedented accuracy. It has also enabled the discoveries of new reaction pathways or intermediates/transition states that are inaccessible in conventional ensemble experiments, which is critical to elucidating their intrinsic mechanisms. Thanks to the rapid development of single-molecule junction (SMJ) techniques, detecting chemical reactions via monitoring the electrical current through single molecules has received an increasing amount of attention and has witnessed tremendous advances in recent years. Research efforts in this direction have opened a new route for probing chemical and physical processes with single-molecule precision. This review presents detailed advancements in probing single-molecule chemical reactions using SMJ techniques. We specifically highlight recent progress in investigating electric-field-driven reactions, reaction dynamics and kinetics, host–guest interactions, and redox reactions of different molecular systems. Finally, we discuss the potential of single-molecule detection using SMJs across various future applications.
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16
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From structure and dynamics to biomolecular functions: The ubiquitous role of solvent in biology. Curr Opin Struct Biol 2022; 77:102462. [PMID: 36150344 DOI: 10.1016/j.sbi.2022.102462] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 08/22/2022] [Indexed: 12/14/2022]
Abstract
Biological activity requires a solvent that can provide a suitable environment, which satisfies the twin need for stability and the ability to change. Among all the solvents water plays the most important role. We review, analyze, and comment on recent works on the structure and dynamics of water around biomolecules and their role in specific biological functions. While studies in the past have focused on understanding the biomolecule-water interactions through a hydration layer; recently the attention has shifted towards understanding functions at a molecular level. Such a microscopic understanding clearly requires elucidation of detailed dynamical processes where solvent molecules play an important role. Finally, we comment on the advances made in understanding the role of water inside a biological cell.
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17
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Water regulates the residence time of Benzamidine in Trypsin. Nat Commun 2022; 13:5438. [PMID: 36114175 PMCID: PMC9481606 DOI: 10.1038/s41467-022-33104-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 09/01/2022] [Indexed: 12/11/2022] Open
Abstract
The process of ligand-protein unbinding is crucial in biophysics. Water is an essential part of any biological system and yet, many aspects of its role remain elusive. Here, we simulate with state-of-the-art enhanced sampling techniques the binding of Benzamidine to Trypsin which is a much studied and paradigmatic ligand-protein system. We use machine learning methods to determine efficient collective coordinates for the complex non-local network of water. These coordinates are used to perform On-the-fly Probability Enhanced Sampling simulations, which we adapt to calculate also the ligand residence time. Our results, both static and dynamic, are in good agreement with experiments. We find that the presence of a water molecule located at the bottom of the binding pocket allows via a network of hydrogen bonds the ligand to be released into the solution. On a finer scale, even when unbinding is allowed, another water molecule further modulates the exit time. Water is an essential part of any biological system, yet many aspects of its role remain elusive. Here the authors show, in a paradigmatic ligand-protein system, that water modulates the ligand residence time in a complex and non-local way, with possible implications in drug design.
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18
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Odstrcil RE, Dutta P, Liu J. LINES: Log-Probability Estimation via Invertible Neural Networks for Enhanced Sampling. J Chem Theory Comput 2022; 18:6297-6309. [PMID: 36099438 DOI: 10.1021/acs.jctc.2c00254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
It is very challenging to sample a molecular process with large activation energies using molecular dynamics simulations. Current enhanced sampling methodologies, such as umbrella sampling and metadynamics, rely on the identification of appropriate reaction coordinates for a system. In this paper, we developed a method for log-probability estimation via invertible neural networks for enhanced sampling (LINES). This iterative scheme utilizes a normalizing flow machine learning model to learn the underlying free energy surface (FES) of a system as a function of molecular coordinates and then applies a gradient-based optimization method to the learned normalizing flow to identify reaction coordinates. A biasing potential is then evaluated over a tabulated grid of the reaction coordinate values, which can be applied to the next round of simulations for enhanced sampling, resulting in more efficient sampling. We tested the accuracy and efficiency of the LINES method in sampling the FES using the alanine dipeptide system. We also demonstrated the effectiveness of identification of reaction coordinates through simulation of cyclobutanol unbinding from β-cyclodextrin and the folding/unfolding of CLN025─a variant of the peptide Chignolin. The LINES method can be extended to the study of large-scale protein systems with complex nonlinear reaction pathways.
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Affiliation(s)
- Ryan E Odstrcil
- School of Mechanical and Materials Engineering, Washington State University, Pullman, Washington 99164, United States
| | - Prashanta Dutta
- School of Mechanical and Materials Engineering, Washington State University, Pullman, Washington 99164, United States
| | - Jin Liu
- School of Mechanical and Materials Engineering, Washington State University, Pullman, Washington 99164, United States
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19
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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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20
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Capelli R, Muniz-Miranda F, Pavan GM. Ephemeral ice-like local environments in classical rigid models of liquid water. J Chem Phys 2022; 156:214503. [DOI: 10.1063/5.0088599] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Despite great efforts over the past 50 years, the simulation of water still presents significant challenges and open questions. At room temperature and pressure, the collective molecular interactions and dynamics of water molecules may form local structural arrangements that are non-trivial to classify. Here, we employ a data-driven approach built on Smooth Overlap of Atomic Position (SOAP) that allows us to compare and classify how widely used classical models represent liquid water. Macroscopically, the obtained results are rationalized based on water thermodynamic observables. Microscopically, we directly observe how transient ice-like ordered environments may dynamically/statistically form in liquid water, even above freezing temperature, by comparing the SOAP spectra for different ice structures with those of the simulated liquid systems. This confirms recent ab initio-based calculations but also reveals how the emergence of ephemeral local ice-like environments in liquid water at room conditions can be captured by classical water models.
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Affiliation(s)
- Riccardo Capelli
- Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Torino, Italy
| | - Francesco Muniz-Miranda
- Department of Chemical and Geological Sciences, University of Modena and Reggio-Emilia, Via Campi 103, I-41125 Modena, Italy
| | - Giovanni M. Pavan
- Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Torino, Italy
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Polo Universitario Lugano, Campus Est, Via la Santa 1, CH-6962 Lugano-Viganello, Switzerland
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21
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Invernizzi M, Parrinello M. Exploration vs Convergence Speed in Adaptive-Bias Enhanced Sampling. J Chem Theory Comput 2022; 18:3988-3996. [PMID: 35617155 PMCID: PMC9202311 DOI: 10.1021/acs.jctc.2c00152] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
In adaptive-bias
enhanced sampling methods, a bias potential is
added to the system to drive transitions between metastable states.
The bias potential is a function of a few collective variables and
is gradually modified according to the underlying free energy surface.
We show that when the collective variables are suboptimal, there is
an exploration–convergence tradeoff, and one must choose between
a quickly converging bias that will lead to fewer transitions or a
slower to converge bias that can explore the phase space more efficiently
but might require a much longer time to produce an accurate free energy
estimate. The recently proposed on-the-fly probability enhanced sampling
(OPES) method focuses on fast convergence, but there are cases where
fast exploration is preferred instead. For this reason, we introduce
a new variant of the OPES method that focuses on quickly escaping
metastable states at the expense of convergence speed. We illustrate
the benefits of this approach in prototypical systems and show that
it outperforms the popular metadynamics method.
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22
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Suating P, Ernst NE, Alagbe BD, Skinner HA, Mague JT, Ashbaugh HS, Gibb BC. On the Nature of Guest Complexation in Water: Triggered Wetting-Water-Mediated Binding. J Phys Chem B 2022; 126:3150-3160. [PMID: 35438501 PMCID: PMC9059121 DOI: 10.1021/acs.jpcb.2c00628] [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: 01/25/2022] [Revised: 03/18/2022] [Indexed: 11/30/2022]
Abstract
The complexity of macromolecular surfaces means that there are still many open questions regarding how specific areas are solvated and how this might affect the complexation of guests. Contributing to the identification and classification of the different possible mechanisms of complexation events in aqueous solution, and as part of the recent SAMPL8 exercise, we report here on the synthesis and conformational properties of TEEtOA 2, a cavitand with conformationally flexible ethyl groups at its portal. Using a combination of ITC and NMR spectroscopy, we report the binding affinities of a series of carboxylates to 2 and compare it to a related cavitand TEMOA 1. Additionally, we report MD simulations revealing how the wetting of the pocket of 2 is controlled by the conformation of its rim ethyl groups and, correspondingly, a novel triggered wetting, guest complexation mechanism, whereby the approaching guest opens up the pocket of the host, inducing its wetting and ultimately allows the formation of a hydrated host-guest complex (H·G·H2O). A general classification of complexation mechanisms is also suggested.
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Affiliation(s)
- Paolo Suating
- Department
of Chemistry, Tulane University, New Orleans, Louisiana 70118, United States
| | - Nicholas E. Ernst
- Department
of Chemistry, Tulane University, New Orleans, Louisiana 70118, United States
| | - Busayo D. Alagbe
- Department
of Chemical and Biomolecular Engineering, Tulane University, New Orleans, Louisiana 70118, United States
| | - Hannah A. Skinner
- Department
of Chemistry, Tulane University, New Orleans, Louisiana 70118, United States
| | - Joel T. Mague
- Department
of Chemistry, Tulane University, New Orleans, Louisiana 70118, United States
| | - Henry S. Ashbaugh
- Department
of Chemical and Biomolecular Engineering, Tulane University, New Orleans, Louisiana 70118, United States
| | - Bruce C. Gibb
- Department
of Chemistry, Tulane University, New Orleans, Louisiana 70118, United States
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23
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Roussey NM, Dickson A. Local Ion Densities can Influence Transition Paths of Molecular Binding. Front Mol Biosci 2022; 9:858316. [PMID: 35558558 PMCID: PMC9086317 DOI: 10.3389/fmolb.2022.858316] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 04/01/2022] [Indexed: 11/22/2022] Open
Abstract
Improper reaction coordinates can pose significant problems for path-based binding free energy calculations. Particularly, omission of long timescale motions can lead to over-estimation of the energetic barriers between the bound and unbound states. Many methods exist to construct the optimal reaction coordinate using a pre-defined basis set of features. Although simulations are typically conducted in explicit solvent, the solvent atoms are often excluded by these feature sets—resulting in little being known about their role in reaction coordinates, and ultimately, their role in determining (un)binding rates and free energies. In this work, analysis is done on an extensive set of host-guest unbinding trajectories, working to characterize differences between high and low probability unbinding trajectories with a focus on solvent-based features, including host-ion interactions, guest-ion interactions and location-dependent ion densities. We find that differences in ion densities as well as guest-ion interactions strongly correlate with differences in the probabilities of reactive paths that are used to determine free energies of (un)binding and play a significant role in the unbinding process.
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Affiliation(s)
- Nicole M. Roussey
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
| | - Alex Dickson
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
- Department of Computational Mathematics, Science, and Engineering, Michigan State University, East Lansing, MI, United States
- *Correspondence: Alex Dickson,
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24
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Debnath J, Parrinello M. Computing Rates and Understanding Unbinding Mechanisms in Host-Guest Systems. J Chem Theory Comput 2022; 18:1314-1319. [PMID: 35200023 DOI: 10.1021/acs.jctc.1c01075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The long time scale associated with ligand residence times renders their computation challenging. Therefore, the influence of factors like solvation and steric hindrance on residence times is not fully understood. Here, we demonstrate in a set of model host-guest systems that the recently developed Gaussian mixture based enhanced sampling allows residence times to be computed and enables an understanding of their unbinding mechanism. We observe that guest unbinding often proceeds via a series of intermediate states that can be labeled by the number of water molecules present in the binding cavity. In several cases the residence time is correlated to the water trapping times in the cavity.
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Affiliation(s)
- Jayashrita Debnath
- Department of Chemistry and Applied Biosciences, ETH Zürich, 8093 Zürich, Switzerland.,Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
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25
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Raucci U, Rizzi V, Parrinello M. Discover, Sample, and Refine: Exploring Chemistry with Enhanced Sampling Techniques. J Phys Chem Lett 2022; 13:1424-1430. [PMID: 35119863 DOI: 10.1021/acs.jpclett.1c03993] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Over the last few decades, enhanced sampling methods have been continuously improved. Here, we exploit this progress and propose a modular workflow for blind reaction discovery and determination of reaction paths. In a three-step strategy, at first we use a collective variable derived from spectral graph theory in conjunction with the explore variant of the on-the-fly probability enhanced sampling method to drive reaction discovery runs. Once different chemical products are determined, we construct an ad-hoc neural network-based collective variable to improve sampling, and finally we refine the results using the free energy perturbation theory and a more accurate Hamiltonian. We apply this strategy to both intramolecular and intermolecular reactions. Our workflow requires minimal user input and extends the power of ab initio molecular dynamics to explore and characterize the reaction space.
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Affiliation(s)
- Umberto Raucci
- Italian Institute of Technology, Via E. Melen 83, 16152, Genova, Italy
| | - Valerio Rizzi
- Italian Institute of Technology, Via E. Melen 83, 16152, Genova, Italy
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26
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Bhakat S. Collective variable discovery in the age of machine learning: reality, hype and everything in between. RSC Adv 2022; 12:25010-25024. [PMID: 36199882 PMCID: PMC9437778 DOI: 10.1039/d2ra03660f] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/20/2022] [Indexed: 11/21/2022] Open
Abstract
Understanding the kinetics and thermodynamics profile of biomolecules is necessary to understand their functional roles which has a major impact in mechanism driven drug discovery. Molecular dynamics simulation has been routinely used to understand conformational dynamics and molecular recognition in biomolecules. Statistical analysis of high-dimensional spatiotemporal data generated from molecular dynamics simulation requires identification of a few low-dimensional variables which can describe the essential dynamics of a system without significant loss of information. In physical chemistry, these low-dimensional variables are often called collective variables. Collective variables are used to generate reduced representations of free energy surfaces and calculate transition probabilities between different metastable basins. However the choice of collective variables is not trivial for complex systems. Collective variables range from geometric criteria such as distances and dihedral angles to abstract ones such as weighted linear combinations of multiple geometric variables. The advent of machine learning algorithms led to increasing use of abstract collective variables to represent biomolecular dynamics. In this review, I will highlight several nuances of commonly used collective variables ranging from geometric to abstract ones. Further, I will put forward some cases where machine learning based collective variables were used to describe simple systems which in principle could have been described by geometric ones. Finally, I will put forward my thoughts on artificial general intelligence and how it can be used to discover and predict collective variables from spatiotemporal data generated by molecular dynamics simulations. Data driven collective variable discovery methods to capture conformational dynamics in biological macromolecules.![]()
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Affiliation(s)
- Soumendranath Bhakat
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Pennsylvania 19104-6059, USA
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28
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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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29
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González D, Macaya L, Vöhringer-Martinez E. Molecular Environment-Specific Atomic Charges Improve Binding Affinity Predictions of SAMPL5 Host-Guest Systems. J Chem Inf Model 2021; 61:4462-4474. [PMID: 34464129 DOI: 10.1021/acs.jcim.1c00655] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Host-guest systems are widely used in benchmarks as model systems to improve computational methods for absolute binding free energy predictions. Recent advances in sampling algorithms for alchemical free energy calculations and the increase in computational power have made their binding affinity prediction primarily dependent on the quality of the force field. Here, we propose a new methodology to derive the atomic charges of host-guest systems based on quantum mechanics/molecular mechanics calculations and minimal basis iterative stockholder (MBIS) partitioning of the polarized electron density. A newly developed interface between the OpenMM and ORCA software packages provides D-MBIS charges that represent the guest's average electrostatic interactions in the hosts or the solvent. The simulation workflow also calculates the average energy required to polarize the guest in the bound and unbound state. Alchemical free energy calculations using the general Amber force field parameters with D-MBIS charges improve the binding affinity prediction of six guests bound to two octa acid hosts compared to the AM1-BCC charge set after correction with the average energetic polarization cost. This correction originates from the difference in potential energy that is required to polarize the guest in the bound and unbound state and contributes significantly to the binding affinity of anionic guests.
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Affiliation(s)
- Duván González
- Departamento de Físico-Química, Facultad de Ciencias Químicas, Universidad de Concepción, 4070386 Concepción, Chile
| | - Luis Macaya
- Departamento de Físico-Química, Facultad de Ciencias Químicas, Universidad de Concepción, 4070386 Concepción, Chile
| | - Esteban Vöhringer-Martinez
- Departamento de Físico-Química, Facultad de Ciencias Químicas, Universidad de Concepción, 4070386 Concepción, Chile
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30
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Zlobin A, Diankin I, Pushkarev S, Golovin A. Probing the Suitability of Different Ca 2+ Parameters for Long Simulations of Diisopropyl Fluorophosphatase. Molecules 2021; 26:5839. [PMID: 34641383 PMCID: PMC8510429 DOI: 10.3390/molecules26195839] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 09/23/2021] [Accepted: 09/24/2021] [Indexed: 11/16/2022] Open
Abstract
Organophosphate hydrolases are promising as potential biotherapeutic agents to treat poisoning with pesticides or nerve gases. However, these enzymes often need to be further engineered in order to become useful in practice. One example of such enhancement is the alteration of enantioselectivity of diisopropyl fluorophosphatase (DFPase). Molecular modeling techniques offer a unique opportunity to address this task rationally by providing a physical description of the substrate-binding process. However, DFPase is a metalloenzyme, and correct modeling of metal cations is a challenging task generally coming with a tradeoff between simulation speed and accuracy. Here, we probe several molecular mechanical parameter combinations for their ability to empower long simulations needed to achieve a quantitative description of substrate binding. We demonstrate that a combination of the Amber19sb force field with the recently developed 12-6 Ca2+ models allows us to both correctly model DFPase and obtain new insights into the DFP binding process.
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Affiliation(s)
- Alexander Zlobin
- Faculty of Bioengineering, Lomonosov Moscow State University, 119234 Moscow, Russia; (I.D.); (S.P.)
- Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
- Sirius University of Science and Technology, 354340 Sochi, Russia
| | - Igor Diankin
- Faculty of Bioengineering, Lomonosov Moscow State University, 119234 Moscow, Russia; (I.D.); (S.P.)
- Sirius University of Science and Technology, 354340 Sochi, Russia
| | - Sergey Pushkarev
- Faculty of Bioengineering, Lomonosov Moscow State University, 119234 Moscow, Russia; (I.D.); (S.P.)
| | - Andrey Golovin
- Faculty of Bioengineering, Lomonosov Moscow State University, 119234 Moscow, Russia; (I.D.); (S.P.)
- Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
- Sirius University of Science and Technology, 354340 Sochi, Russia
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31
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Barros EP, Ries B, Böselt L, Champion C, Riniker S. Recent developments in multiscale free energy simulations. Curr Opin Struct Biol 2021; 72:55-62. [PMID: 34534706 DOI: 10.1016/j.sbi.2021.08.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 08/06/2021] [Accepted: 08/16/2021] [Indexed: 11/26/2022]
Abstract
Physics-based free energy simulations enable the rigorous calculation of properties, such as conformational equilibria, solvation or binding free energies. While historically most applications have occurred at the atomistic level of resolution, a range of advances in the past years make it possible now to reliably cross the temporal, spatial and theory scales for the modeling of complex systems or the efficient prediction of results at the accuracy level of expensive quantum-mechanical calculations. In this mini-review, we discuss recent methodological advances as well as opportunities opened up by the introduction of machine learning approaches, which tackle the diverse challenges across the different scales, improve the accuracy and feasibility, and push the boundaries of multiscale free energy simulations.
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Affiliation(s)
- Emilia P Barros
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland
| | - Benjamin Ries
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland
| | - Lennard Böselt
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland
| | - Candide Champion
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland
| | - Sereina Riniker
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland.
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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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Ansari N, Rizzi V, Carloni P, Parrinello M. Water-Triggered, Irreversible Conformational Change of SARS-CoV-2 Main Protease on Passing from the Solid State to Aqueous Solution. J Am Chem Soc 2021; 143:12930-12934. [PMID: 34398611 PMCID: PMC8386029 DOI: 10.1021/jacs.1c05301] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Indexed: 11/30/2022]
Abstract
The main protease from SARS-CoV-2 is a homodimer. Yet, a recent 0.1-ms-long molecular dynamics simulation performed by D. E. Shaw's research group shows that it readily undergoes a symmetry-breaking event on passing from the solid state to aqueous solution. As a result, the subunits present distinct conformations of the binding pocket. By analyzing this long simulation, we uncover a previously unrecognized role of water molecules in triggering the transition. Interestingly, each subunit presents a different collection of long-lived water molecules. Enhanced sampling simulations performed here, along with machine learning approaches, further establish that the transition to the asymmetric state is essentially irreversible.
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Affiliation(s)
- Narjes Ansari
- Italian
Institute of Technology, Via E. Melen 83, 16152 Genova, Italy
| | - Valerio Rizzi
- Italian
Institute of Technology, Via E. Melen 83, 16152 Genova, Italy
| | - Paolo Carloni
- Computational
Biomedicine, Institute for Advanced Simulation (IAS-5) and Institute
of Neuroscience and Medicine (INM-9), and JARA-Institute “Molecular
Neuroscience and Neuroimaging” (INM-11), Forschungszentrum Jülich, Jülich 52425, Germany
- Physics
Department, RWTH Aachen University, Aachen 52074, Germany
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