1
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Amin M, Brooks BR. The oxidation of the [4Fe-4S] Cluster of DNA Primase Alters the Binding Interactions with DNA and RNA Primers. Biophys J 2024:S0006-3495(24)00321-7. [PMID: 38733082 DOI: 10.1016/j.bpj.2024.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/14/2024] [Accepted: 05/07/2024] [Indexed: 05/13/2024] Open
Abstract
DNA primase is an iron sulfur enzyme in DNA replication, responsible for synthesizing short RNA primers that serve as starting points for DNA synthesis. The role of the [4Fe-4S] cluster is not well determined. Here, we calculate the redox potential (Em) of the [4Fe-4S] with and without DNA/RNA using continuum electrostatics. In addition, we identify the structural changes coupled to the oxidation/reduction. Our calculations show that the DNA/RNA primer lowers the Em by 110 mV and 50 mV for the [4Fe-4S]+ and [4Fe-4S]2+ states respectively. The oxidation of the cluster is coupled to structural changes that significantly reduces the binding energies between the DNA and the nearby residues. The negative charges accumulated by the DNA and the RNA primers induce the oxidation of the [4Fe-4S] cluster. This in turn stimulates structural changes on the DNA-protein interface that significantly reduces the binding energies.
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Affiliation(s)
- Muhamed Amin
- Laboratory of Computational Biology, National Heart, Lung and Blood, Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood, Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
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2
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Roe DR, Brooks BR. MPI-parallelization of the grid inhomogeneous solvation theory calculation. J Comput Chem 2024; 45:633-637. [PMID: 38071482 PMCID: PMC10922152 DOI: 10.1002/jcc.27278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 11/20/2023] [Accepted: 11/23/2023] [Indexed: 03/02/2024]
Abstract
The grid inhomogeneous solvation theory (GIST) method requires the often time-consuming calculation of water-water and water-solute energy on a grid. Previous efforts to speed up this calculation include using OpenMP, GPUs, and particle mesh Ewald. This article details how the speed of this calculation can be increased by parallelizing it with MPI, where trajectory frames are divided among multiple processors. This requires very little communication between individual processes during trajectory processing, meaning the calculation scales well to large processor counts. This article also details how the entropy calculation, which must happen after trajectory processing since it requires information from all trajectory frames, is parallelized via MPI. This parallelized GIST method has been implemented in the freely-available CPPTRAJ analysis software.
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Affiliation(s)
- Daniel R. Roe
- Laboratory of Computational Biology, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, 20892
| | - Bernard R. Brooks
- Laboratory of Computational Biology, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, 20892
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3
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Amin M, Kaur D, Brudvig GW, Brooks BR. Mapping the Oxygens in the Oxygen-Evolving Complex of Photosystem II by Their Nucleophilicity Using Quantum Descriptors. J Chem Theory Comput 2024. [PMID: 38306696 DOI: 10.1021/acs.jctc.3c00926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2024]
Abstract
The oxygen-evolving complex (OEC) of Photosystem II catalyzes the water-splitting reaction using solar energy. Thus, understanding the reaction mechanism will inspire the design of biomimetic artificial catalysts that convert solar energy to chemical energy. Conceptual Density Functional Theory (CDFT) focuses on understanding the reactivity of molecules and the atomic contribution to the overall nucleophilicity and electrophilicity of the molecule using quantum descriptors. However, this method has not been applied to the OEC before. Here, we use Fukui functions and the dual descriptor to provide quantitative measures of the nucleophilicity and electrophilicity of oxygens in the OEC for different models in different S states. Our results show that the μ-oxo bridges connected to terminal Mn4 are nucleophilic, and those in the cube formed by Mn1, Mn2, and Mn3 are mostly electrophilic. The dual descriptors of the bridging oxygens in the OEC showed a similar reactivity to that of bridging oxygens in Mn model compounds. However, the terminal water W1, which is bound to Mn4, showed very strong reactivity in some of the S3 models. Thus, our calculations support the model that proposes the formation of the O2 molecule through nucleophilic attack by a terminal water.
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Affiliation(s)
- Muhamed Amin
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
- Department of Sciences, University College Groningen, University of Groningen, 9718 BG Groningen, The Netherlands
| | - Divya Kaur
- Department of Chemistry, Brock University, 500 Glenridge Avenue, St. Catharines, Ontario, Canada L2S 3A1
| | - Gary W Brudvig
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
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4
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Abstract
Conformational dynamics in proteins can give rise to aggregation prone states during folding, and these kinetically stable states could form oligomers and aggregates. In this study, we investigate the intermediate states and near-folded states of β2-microglobulin and their physico-chemical properties using molecular dynamics and Markov state modeling. Analysis of hundreds of microseconds simulation show the importance of the edge strands in the misfolded states that give rise to a high exposure of hydrophobic residues in the core of the protein that could initiate oligomerization and aggregate formation. Our study sheds light on the first step of aggregation of β2m monomers and gave a better picture of the landscape of protein misfolding and aggregation.
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Affiliation(s)
- Mahdi Ghorbani
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, Maryland 20742, United States
- Laboratory of Computational Biology, National, Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20824, United States
| | - Bernard R Brooks
- Laboratory of Computational Biology, National, Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20824, United States
| | - Jeffery B Klauda
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, Maryland 20742, United States
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5
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Chen AY, Brooks BR, Damjanovic A. Ion channel selectivity through ion-modulated changes of selectivity filter p Ka values. Proc Natl Acad Sci U S A 2023; 120:e2220343120. [PMID: 37339196 PMCID: PMC10293820 DOI: 10.1073/pnas.2220343120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 04/26/2023] [Indexed: 06/22/2023] Open
Abstract
In bacterial voltage-gated sodium channels, the passage of ions through the pore is controlled by a selectivity filter (SF) composed of four glutamate residues. The mechanism of selectivity has been the subject of intense research, with suggested mechanisms based on steric effects, and ion-triggered conformational change. Here, we propose an alternative mechanism based on ion-triggered shifts in pKa values of SF glutamates. We study the NavMs channel for which the open channel structure is available. Our free-energy calculations based on molecular dynamics simulations suggest that pKa values of the four glutamates are higher in solution of K+ ions than in solution of Na+ ions. Higher pKa in the presence of K+ stems primarily from the higher population of dunked conformations of the protonated Glu sidechain, which exhibit a higher pKa shift. Since pKa values are close to the physiological pH, this results in predominant population of the fully deprotonated state of glutamates in Na+ solution, while protonated states are predominantly populated in K+ solution. Through molecular dynamics simulations we calculate that the deprotonated state is the most conductive, the singly protonated state is less conductive, and the doubly protonated state has significantly reduced conductance. Thus, we propose that a significant component of selectivity is achieved through ion-triggered shifts in the protonation state, which favors more conductive states for Na+ ions and less conductive states for K+ ions. This mechanism also suggests a strong pH dependence of selectivity, which has been experimentally observed in structurally similar NaChBac channels.
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Affiliation(s)
- Ada Y. Chen
- Department of Physics & Astronomy, Johns Hopkins University, Baltimore, MD21218
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, NIH, Bethesda, MD20892
| | - Bernard R. Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, NIH, Bethesda, MD20892
| | - Ana Damjanovic
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, NIH, Bethesda, MD20892
- Department of Biophysics, Johns Hopkins University, Baltimore, MD21218
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6
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Wu X, Brooks BR. Self-guided molecular dynamics simulation based on concerted movement. Biophys J 2023; 122:420a. [PMID: 36784147 DOI: 10.1016/j.bpj.2022.11.2276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023] Open
Affiliation(s)
- Xiongwu Wu
- Laboratory of Cell Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Bernard R Brooks
- Laboratory of Cell Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
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7
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Min J, Britt M, Sukharev SI, Brooks BR, Klauda JB. Improving the CHARMM36m force field for arginine-phosphate interactions. Biophys J 2023; 122:184a. [PMID: 36782877 DOI: 10.1016/j.bpj.2022.11.1135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023] Open
Affiliation(s)
- Jiyeon Min
- Biophysics Program, University of Maryland, College Park, MD, USA; The Laboratory of Computational Biology, National Institutes of Health, Bethesda, MD, USA
| | - Madolyn Britt
- Biophysics Program, University of Maryland, College Park, MD, USA
| | - Sergei I Sukharev
- Biophysics Program, University of Maryland, College Park, MD, USA; Department of Biology, University of Maryland, College Park, MD, USA
| | - Bernard R Brooks
- The Laboratory of Computational Biology, National Institutes of Health, Bethesda, MD, USA
| | - Jeffery B Klauda
- Biophysics Program, University of Maryland, College Park, MD, USA; Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA
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8
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Roe DR, Brooks BR. Quantifying the effects of lossy compression on energies calculated from molecular dynamics trajectories. Protein Sci 2022; 31:e4511. [PMID: 36382864 PMCID: PMC9703595 DOI: 10.1002/pro.4511] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 11/08/2022] [Accepted: 11/10/2022] [Indexed: 11/17/2022]
Abstract
Molecular dynamics (MD) simulations are now able to routinely reach timescales of microseconds and beyond. This has led to a corresponding increase in the amount of MD trajectory data that needs to be stored, particularly when those trajectories contain explicit solvent molecules. As such, it is desirable to be able to compress trajectory data while still retaining as much of the original information as possible. In this work, we describe compressing MD trajectory data using the NetCDF4/HDF5 file format, making use of quantization of the original positions to achieve better compression ratios. We also analyze the affect this has on both the resulting positions and the energies calculated from post-processing these trajectories, and recommend an optimal level of quantization. Overall we find the NetCDF4/HDF5 format to be an excellent choice for storing MD trajectory data in terms of speed, compressibility, and versatility.
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Affiliation(s)
- Daniel R. Roe
- Laboratory of Computational BiologyNational Heart, Lung, and Blood Institute, National Institutes of HealthBethesdaMarylandUSA
| | - Bernard R. Brooks
- Laboratory of Computational BiologyNational Heart, Lung, and Blood Institute, National Institutes of HealthBethesdaMarylandUSA
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9
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Hudson PS, Aviat F, Meana-Pañeda R, Warrensford L, Pollard BC, Prasad S, Jones MR, Woodcock HL, Brooks BR. Obtaining QM/MM binding free energies in the SAMPL8 drugs of abuse challenge: indirect approaches. J Comput Aided Mol Des 2022; 36:263-277. [PMID: 35597880 PMCID: PMC9148874 DOI: 10.1007/s10822-022-00443-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 02/17/2022] [Indexed: 11/28/2022]
Abstract
Accurately predicting free energy differences is essential in realizing the full potential of rational drug design. Unfortunately, high levels of accuracy often require computationally expensive QM/MM Hamiltonians. Fortuitously, the cost of employing QM/MM approaches in rigorous free energy simulation can be reduced through the use of the so-called “indirect” approach to QM/MM free energies, in which the need for QM/MM simulations is avoided via a QM/MM “correction” at the classical endpoints of interest. Herein, we focus on the computation of QM/MM binding free energies in the context of the SAMPL8 Drugs of Abuse host–guest challenge. Of the 5 QM/MM correction coupled with force-matching submissions, PM6-D3H4/MM ranked submission proved the best overall QM/MM entry, with an RMSE from experimental results of 2.43 kcal/mol (best in ranked submissions), a Pearson’s correlation of 0.78 (second-best in ranked submissions), and a Kendall \documentclass[12pt]{minimal}
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\begin{document}$$\tau$$\end{document}τ correlation of 0.52 (best in ranked submissions).
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Affiliation(s)
- Phillip S Hudson
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA.
| | - Félix Aviat
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA
| | - Rubén Meana-Pañeda
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA
| | - Luke Warrensford
- Department of Chemistry, University of South Florida, Tampa, FL, 33620, USA
| | - Benjamin C Pollard
- Department of Chemistry, University of South Florida, Tampa, FL, 33620, USA
| | - Samarjeet Prasad
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA
| | - Michael R Jones
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA
| | - H Lee Woodcock
- Department of Chemistry, University of South Florida, Tampa, FL, 33620, USA
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA
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10
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Abstract
Protonation states of ionizable protein residues modulate many essential biological processes. For correct modeling and understanding of these processes, it is crucial to accurately determine their pKa values. Here, we present four tree-based machine learning models for protein pKa prediction. The four models, Random Forest, Extra Trees, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), were trained on three experimental PDB and pKa datasets, two of which included a notable portion of internal residues. We observed similar performance among the four machine learning algorithms. The best model trained on the largest dataset performs 37% better than the widely used empirical pKa prediction tool PROPKA and 15% better than the published result from the pKa prediction method DelPhiPKa. The overall root-mean-square error (RMSE) for this model is 0.69, with surface and buried RMSE values being 0.56 and 0.78, respectively, considering six residue types (Asp, Glu, His, Lys, Cys, and Tyr), and 0.63 when considering Asp, Glu, His, and Lys only. We provide pKa predictions for proteins in human proteome from the AlphaFold Protein Structure Database and observed that 1% of Asp/Glu/Lys residues have highly shifted pKa values close to the physiological pH.
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Affiliation(s)
- Ada Y. Chen
- Department of Physics & Astronomy, Johns Hopkins
University, Baltimore, Maryland, 21218
- Laboratory of Computational Biology, National Heart, Lung
and Blood Institute, National Institutes of Health, Bethesda, Maryland, 20892
| | - Juyong Lee
- Department of Chemistry, Division of Chemistry and
Biochemistry, Kangwon National University, 1 Gangwondaehak-gil, Chuncheon, 24341,
Republic of Korea
| | - Ana Damjanovic
- Department of Biophysics, Johns Hopkins University,
Baltimore, Maryland, 21218
| | - Bernard R. Brooks
- Laboratory of Computational Biology, National Heart, Lung
and Blood Institute, National Institutes of Health, Bethesda, Maryland, 20892
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11
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Ghorbani M, Prasad S, Brooks BR, Klauda JB. Unraveling the allosteric activation of GPCR using metadynamics and deep learning. Biophys J 2022. [DOI: 10.1016/j.bpj.2021.11.1314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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12
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Wu X, Brooks BR. Identify the conformational states of glycine receptor alpha3 through umbrella sampling. Biophys J 2022. [DOI: 10.1016/j.bpj.2021.11.464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
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13
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Choi YK, Kern NR, Kim S, Kanhaiya K, Afshar Y, Jeon SH, Jo S, Brooks BR, Lee J, Tadmor EB, Heinz H, Im W. CHARMM-GUI Nanomaterial Modeler for Modeling and Simulation of Nanomaterial Systems. J Chem Theory Comput 2022; 18:479-493. [PMID: 34871001 PMCID: PMC8752518 DOI: 10.1021/acs.jctc.1c00996] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Molecular modeling and simulation are invaluable tools for nanoscience that predict mechanical, physicochemical, and thermodynamic properties of nanomaterials and provide molecular-level insight into underlying mechanisms. However, building nanomaterial-containing systems remains challenging due to the lack of reliable and integrated cyberinfrastructures. Here we present Nanomaterial Modeler in CHARMM-GUI, a web-based cyberinfrastructure that provides an automated process to generate various nanomaterial models, associated topologies, and configuration files to perform state-of-the-art molecular dynamics simulations using most simulation packages. The nanomaterial models are based on the interface force field, one of the most reliable force fields (FFs). The transferability of nanomaterial models among the simulation programs was assessed by single-point energy calculations, which yielded 0.01% relative absolute energy differences for various surface models and equilibrium nanoparticle shapes. Three widely used Lennard-Jones (LJ) cutoff methods are employed to evaluate the compatibility of nanomaterial models with respect to conventional biomolecular FFs: simple truncation at r = 12 Å (12 cutoff), force-based switching over 10 to 12 Å (10-12 fsw), and LJ particle mesh Ewald with no cutoff (LJPME). The FF parameters with these LJ cutoff methods are extensively validated by reproducing structural, interfacial, and mechanical properties. We find that the computed density and surface energies are in good agreement with reported experimental results, although the simulation results increase in the following order: 10-12 fsw <12 cutoff < LJPME. Nanomaterials in which LJ interactions are a major component show relatively higher deviations (up to 4% in density and 8% in surface energy differences) compared with the experiment. Nanomaterial Modeler's capability is also demonstrated by generating complex systems of nanomaterial-biomolecule and nanomaterial-polymer interfaces with a combination of existing CHARMM-GUI modules. We hope that Nanomaterial Modeler can be used to carry out innovative nanomaterial modeling and simulations to acquire insight into the structure, dynamics, and underlying mechanisms of complex nanomaterial-containing systems.
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Affiliation(s)
- Yeol Kyo Choi
- Department of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Nathan R. Kern
- Department of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Seonghan Kim
- Department of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Krishan Kanhaiya
- Department of Chemical and Biological Engineering, University of Colorado at Boulder, Boulder, CO 80301, USA
| | - Yaser Afshar
- Department of Aerospace Engineering and Mechanics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Sun Hee Jeon
- Department of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Sunhwan Jo
- Leadership Computing Facility, Argonne National Laboratory, 9700 Cass Ave, Argonne, IL 60439, USA
| | - Bernard R. Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jumin Lee
- Department of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Ellad B. Tadmor
- Department of Aerospace Engineering and Mechanics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Hendrik Heinz
- Department of Chemical and Biological Engineering, University of Colorado at Boulder, Boulder, CO 80301, USA
| | - Wonpil Im
- Department of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA
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14
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Ghorbani M, Prasad S, Klauda JB, Brooks BR. Variational embedding of protein folding simulations using Gaussian mixture variational autoencoders. J Chem Phys 2021; 155:194108. [PMID: 34800961 DOI: 10.1063/5.0069708] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Conformational sampling of biomolecules using molecular dynamics simulations often produces a large amount of high dimensional data that makes it difficult to interpret using conventional analysis techniques. Dimensionality reduction methods are thus required to extract useful and relevant information. Here, we devise a machine learning method, Gaussian mixture variational autoencoder (GMVAE), that can simultaneously perform dimensionality reduction and clustering of biomolecular conformations in an unsupervised way. We show that GMVAE can learn a reduced representation of the free energy landscape of protein folding with highly separated clusters that correspond to the metastable states during folding. Since GMVAE uses a mixture of Gaussians as its prior, it can directly acknowledge the multi-basin nature of the protein folding free energy landscape. To make the model end-to-end differentiable, we use a Gumbel-softmax distribution. We test the model on three long-timescale protein folding trajectories and show that GMVAE embedding resembles the folding funnel with folded states down the funnel and unfolded states outside the funnel path. Additionally, we show that the latent space of GMVAE can be used for kinetic analysis and Markov state models built on this embedding produce folding and unfolding timescales that are in close agreement with other rigorous dynamical embeddings such as time independent component analysis.
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Affiliation(s)
- Mahdi Ghorbani
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20824, USA
| | - Samarjeet Prasad
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20824, USA
| | - Jeffery B Klauda
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, Maryland 20742, USA
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20824, USA
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Epifanovsky E, Gilbert ATB, Feng X, Lee J, Mao Y, Mardirossian N, Pokhilko P, White AF, Coons MP, Dempwolff AL, Gan Z, Hait D, Horn PR, Jacobson LD, Kaliman I, Kussmann J, Lange AW, Lao KU, Levine DS, Liu J, McKenzie SC, Morrison AF, Nanda KD, Plasser F, Rehn DR, Vidal ML, You ZQ, Zhu Y, Alam B, Albrecht BJ, Aldossary A, Alguire E, Andersen JH, Athavale V, Barton D, Begam K, Behn A, Bellonzi N, Bernard YA, Berquist EJ, Burton HGA, Carreras A, Carter-Fenk K, Chakraborty R, Chien AD, Closser KD, Cofer-Shabica V, Dasgupta S, de Wergifosse M, Deng J, Diedenhofen M, Do H, Ehlert S, Fang PT, Fatehi S, Feng Q, Friedhoff T, Gayvert J, Ge Q, Gidofalvi G, Goldey M, Gomes J, González-Espinoza CE, Gulania S, Gunina AO, Hanson-Heine MWD, Harbach PHP, Hauser A, Herbst MF, Hernández Vera M, Hodecker M, Holden ZC, Houck S, Huang X, Hui K, Huynh BC, Ivanov M, Jász Á, Ji H, Jiang H, Kaduk B, Kähler S, Khistyaev K, Kim J, Kis G, Klunzinger P, Koczor-Benda Z, Koh JH, Kosenkov D, Koulias L, Kowalczyk T, Krauter CM, Kue K, Kunitsa A, Kus T, Ladjánszki I, Landau A, Lawler KV, Lefrancois D, Lehtola S, Li RR, Li YP, Liang J, Liebenthal M, Lin HH, Lin YS, Liu F, Liu KY, Loipersberger M, Luenser A, Manjanath A, Manohar P, Mansoor E, Manzer SF, Mao SP, Marenich AV, Markovich T, Mason S, Maurer SA, McLaughlin PF, Menger MFSJ, Mewes JM, Mewes SA, Morgante P, Mullinax JW, Oosterbaan KJ, Paran G, Paul AC, Paul SK, Pavošević F, Pei Z, Prager S, Proynov EI, Rák Á, Ramos-Cordoba E, Rana B, Rask AE, Rettig A, Richard RM, Rob F, Rossomme E, Scheele T, Scheurer M, Schneider M, Sergueev N, Sharada SM, Skomorowski W, Small DW, Stein CJ, Su YC, Sundstrom EJ, Tao Z, Thirman J, Tornai GJ, Tsuchimochi T, Tubman NM, Veccham SP, Vydrov O, Wenzel J, Witte J, Yamada A, Yao K, Yeganeh S, Yost SR, Zech A, Zhang IY, Zhang X, Zhang Y, Zuev D, Aspuru-Guzik A, Bell AT, Besley NA, Bravaya KB, Brooks BR, Casanova D, Chai JD, Coriani S, Cramer CJ, Cserey G, DePrince AE, DiStasio RA, Dreuw A, Dunietz BD, Furlani TR, Goddard WA, Hammes-Schiffer S, Head-Gordon T, Hehre WJ, Hsu CP, Jagau TC, Jung Y, Klamt A, Kong J, Lambrecht DS, Liang W, Mayhall NJ, McCurdy CW, Neaton JB, Ochsenfeld C, Parkhill JA, Peverati R, Rassolov VA, Shao Y, Slipchenko LV, Stauch T, Steele RP, Subotnik JE, Thom AJW, Tkatchenko A, Truhlar DG, Van Voorhis T, Wesolowski TA, Whaley KB, Woodcock HL, Zimmerman PM, Faraji S, Gill PMW, Head-Gordon M, Herbert JM, Krylov AI. Software for the frontiers of quantum chemistry: An overview of developments in the Q-Chem 5 package. J Chem Phys 2021; 155:084801. [PMID: 34470363 PMCID: PMC9984241 DOI: 10.1063/5.0055522] [Citation(s) in RCA: 412] [Impact Index Per Article: 137.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange-correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear-electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an "open teamware" model and an increasingly modular design.
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Affiliation(s)
- Evgeny Epifanovsky
- Q-Chem, Inc., 6601 Owens Drive, Suite 105, Pleasanton, California 94588, USA
| | | | | | - Joonho Lee
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Yuezhi Mao
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | | | - Pavel Pokhilko
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, USA
| | - Alec F. White
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Marc P. Coons
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | - Adrian L. Dempwolff
- Interdisciplinary Center for Scientific Computing, Ruprecht-Karls University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
| | - Zhengting Gan
- Q-Chem, Inc., 6601 Owens Drive, Suite 105, Pleasanton, California 94588, USA
| | - Diptarka Hait
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Paul R. Horn
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Leif D. Jacobson
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | | | - Jörg Kussmann
- Department of Chemistry, Ludwig Maximilian University, Butenandtstr. 7, D-81377 München, Germany
| | - Adrian W. Lange
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | - Ka Un Lao
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | - Daniel S. Levine
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | | | - Simon C. McKenzie
- Research School of Chemistry, Australian National University, Canberra, Australia
| | | | - Kaushik D. Nanda
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, USA
| | | | - Dirk R. Rehn
- Interdisciplinary Center for Scientific Computing, Ruprecht-Karls University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
| | - Marta L. Vidal
- Department of Chemistry, Technical University of Denmark, Kemitorvet Bldg. 207, DK-2800 Kgs Lyngby, Denmark
| | | | - Ying Zhu
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | - Bushra Alam
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | - Benjamin J. Albrecht
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | | | - Ethan Alguire
- Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Josefine H. Andersen
- Department of Chemistry, Technical University of Denmark, Kemitorvet Bldg. 207, DK-2800 Kgs Lyngby, Denmark
| | - Vishikh Athavale
- Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Dennis Barton
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg, Luxembourg
| | - Khadiza Begam
- Department of Physics, Kent State University, Kent, Ohio 44242, USA
| | - Andrew Behn
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Nicole Bellonzi
- Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Yves A. Bernard
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, USA
| | | | - Hugh G. A. Burton
- Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
| | - Abel Carreras
- Donostia International Physics Center, 20080 Donostia, Euskadi, Spain
| | - Kevin Carter-Fenk
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | | | - Alan D. Chien
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, USA
| | | | - Vale Cofer-Shabica
- Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Saswata Dasgupta
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | - Marc de Wergifosse
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, USA
| | - Jia Deng
- Research School of Chemistry, Australian National University, Canberra, Australia
| | | | - Hainam Do
- School of Chemistry, University of Nottingham, Nottingham, United Kingdom
| | - Sebastian Ehlert
- Mulliken Center for Theoretical Chemistry, Institut für Physikalische und Theoretische Chemie, Beringstr. 4, 53115 Bonn, Germany
| | - Po-Tung Fang
- Department of Physics, National Taiwan University, Taipei 10617, Taiwan
| | | | - Qingguo Feng
- Department of Chemistry and Biochemistry, Kent State University, Kent, Ohio 44240, USA
| | - Triet Friedhoff
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, USA
| | - James Gayvert
- Department of Chemistry, Boston University, Boston, Massachusetts 02215, USA
| | - Qinghui Ge
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Gergely Gidofalvi
- Department of Chemistry and Biochemistry, Gonzaga University, Spokane, Washington 99258, USA
| | - Matthew Goldey
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Joe Gomes
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | | | - Sahil Gulania
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, USA
| | - Anastasia O. Gunina
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, USA
| | | | - Phillip H. P. Harbach
- Interdisciplinary Center for Scientific Computing, Ruprecht-Karls University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
| | - Andreas Hauser
- Institute of Experimental Physics, Graz University of Technology, Graz, Austria
| | | | - Mario Hernández Vera
- Department of Chemistry, Ludwig Maximilian University, Butenandtstr. 7, D-81377 München, Germany
| | - Manuel Hodecker
- Interdisciplinary Center for Scientific Computing, Ruprecht-Karls University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
| | - Zachary C. Holden
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | - Shannon Houck
- Department of Chemistry, Virginia Tech, Blacksburg, Virginia 24061, USA
| | - Xunkun Huang
- Department of Chemistry, Xiamen University, Xiamen 361005, China
| | - Kerwin Hui
- Department of Physics, National Taiwan University, Taipei 10617, Taiwan
| | - Bang C. Huynh
- Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
| | - Maxim Ivanov
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, USA
| | - Ádám Jász
- Stream Novation Ltd., Práter utca 50/a, H-1083 Budapest, Hungary
| | - Hyunjun Ji
- Graduate School of Energy, Environment, Water and Sustainability (EEWS), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Hanjie Jiang
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Benjamin Kaduk
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Sven Kähler
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, USA
| | - Kirill Khistyaev
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, USA
| | - Jaehoon Kim
- Graduate School of Energy, Environment, Water and Sustainability (EEWS), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Gergely Kis
- Stream Novation Ltd., Práter utca 50/a, H-1083 Budapest, Hungary
| | | | - Zsuzsanna Koczor-Benda
- Department of Chemistry, Ludwig Maximilian University, Butenandtstr. 7, D-81377 München, Germany
| | - Joong Hoon Koh
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, USA
| | - Dimitri Kosenkov
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, USA
| | - Laura Koulias
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, Florida 32306, USA
| | | | - Caroline M. Krauter
- Interdisciplinary Center for Scientific Computing, Ruprecht-Karls University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
| | - Karl Kue
- Institute of Chemistry, Academia Sinica, 128, Academia Road Section 2, Nangang District, Taipei 11529, Taiwan
| | - Alexander Kunitsa
- Department of Chemistry, Boston University, Boston, Massachusetts 02215, USA
| | - Thomas Kus
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, USA
| | | | - Arie Landau
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, USA
| | - Keith V. Lawler
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Daniel Lefrancois
- Interdisciplinary Center for Scientific Computing, Ruprecht-Karls University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
| | | | - Run R. Li
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, Florida 32306, USA
| | - Yi-Pei Li
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Jiashu Liang
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Marcus Liebenthal
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, Florida 32306, USA
| | - Hung-Hsuan Lin
- Institute of Chemistry, Academia Sinica, 128, Academia Road Section 2, Nangang District, Taipei 11529, Taiwan
| | - You-Sheng Lin
- Department of Physics, National Taiwan University, Taipei 10617, Taiwan
| | - Fenglai Liu
- Q-Chem, Inc., 6601 Owens Drive, Suite 105, Pleasanton, California 94588, USA
| | | | | | - Arne Luenser
- Department of Chemistry, Ludwig Maximilian University, Butenandtstr. 7, D-81377 München, Germany
| | - Aaditya Manjanath
- Institute of Chemistry, Academia Sinica, 128, Academia Road Section 2, Nangang District, Taipei 11529, Taiwan
| | - Prashant Manohar
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, USA
| | - Erum Mansoor
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Sam F. Manzer
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Shan-Ping Mao
- Department of Physics, National Taiwan University, Taipei 10617, Taiwan
| | | | - Thomas Markovich
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, USA
| | - Stephen Mason
- School of Chemistry, University of Nottingham, Nottingham, United Kingdom
| | - Simon A. Maurer
- Department of Chemistry, Ludwig Maximilian University, Butenandtstr. 7, D-81377 München, Germany
| | - Peter F. McLaughlin
- Q-Chem, Inc., 6601 Owens Drive, Suite 105, Pleasanton, California 94588, USA
| | | | - Jan-Michael Mewes
- Interdisciplinary Center for Scientific Computing, Ruprecht-Karls University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
| | - Stefanie A. Mewes
- Interdisciplinary Center for Scientific Computing, Ruprecht-Karls University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
| | - Pierpaolo Morgante
- Department of Chemistry, Florida Institute of Technology, Melbourne, Florida 32901, USA
| | - J. Wayne Mullinax
- Department of Chemistry, Florida Institute of Technology, Melbourne, Florida 32901, USA
| | | | | | - Alexander C. Paul
- Interdisciplinary Center for Scientific Computing, Ruprecht-Karls University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
| | - Suranjan K. Paul
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | - Fabijan Pavošević
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, USA
| | - Zheng Pei
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019, USA
| | - Stefan Prager
- Interdisciplinary Center for Scientific Computing, Ruprecht-Karls University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
| | - Emil I. Proynov
- Q-Chem, Inc., 6601 Owens Drive, Suite 105, Pleasanton, California 94588, USA
| | - Ádám Rák
- Stream Novation Ltd., Práter utca 50/a, H-1083 Budapest, Hungary
| | - Eloy Ramos-Cordoba
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Bhaskar Rana
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | - Alan E. Rask
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Adam Rettig
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Ryan M. Richard
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | - Fazle Rob
- Q-Chem, Inc., 6601 Owens Drive, Suite 105, Pleasanton, California 94588, USA
| | - Elliot Rossomme
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Tarek Scheele
- Institute for Physical and Theoretical Chemistry, University of Bremen, Bremen, Germany
| | - Maximilian Scheurer
- Interdisciplinary Center for Scientific Computing, Ruprecht-Karls University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
| | - Matthias Schneider
- Interdisciplinary Center for Scientific Computing, Ruprecht-Karls University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
| | - Nickolai Sergueev
- Department of Chemistry and Biochemistry, Kent State University, Kent, Ohio 44240, USA
| | - Shaama M. Sharada
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Wojciech Skomorowski
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, USA
| | - David W. Small
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Christopher J. Stein
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Yu-Chuan Su
- Department of Physics, National Taiwan University, Taipei 10617, Taiwan
| | - Eric J. Sundstrom
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Zhen Tao
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, USA
| | - Jonathan Thirman
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Gábor J. Tornai
- Stream Novation Ltd., Práter utca 50/a, H-1083 Budapest, Hungary
| | - Takashi Tsuchimochi
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Norm M. Tubman
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | | | - Oleg Vydrov
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Jan Wenzel
- Interdisciplinary Center for Scientific Computing, Ruprecht-Karls University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
| | - Jon Witte
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Atsushi Yamada
- Department of Chemistry and Biochemistry, Kent State University, Kent, Ohio 44240, USA
| | - Kun Yao
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, USA
| | - Sina Yeganeh
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Shane R. Yost
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - Alexander Zech
- Department of Physical Chemistry, University of Geneva, 30, Quai Ernest-Ansermet, CH-1211 Geneva 4, Switzerland
| | - Igor Ying Zhang
- Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Xing Zhang
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | - Yu Zhang
- Q-Chem, Inc., 6601 Owens Drive, Suite 105, Pleasanton, California 94588, USA
| | - Dmitry Zuev
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, USA
| | - Alán Aspuru-Guzik
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, USA
| | - Alexis T. Bell
- Department of Chemical Engineering, University of California, Berkeley, California 94720, USA
| | - Nicholas A. Besley
- School of Chemistry, University of Nottingham, Nottingham, United Kingdom
| | - Ksenia B. Bravaya
- Department of Chemistry, Boston University, Boston, Massachusetts 02215, USA
| | - Bernard R. Brooks
- Laboratory of Computational Biophysics, National Institute of Health, Bethesda, Maryland 20892, USA
| | - David Casanova
- Donostia International Physics Center, 20080 Donostia, Euskadi, Spain
| | | | - Sonia Coriani
- Department of Chemistry, Technical University of Denmark, Kemitorvet Bldg. 207, DK-2800 Kgs Lyngby, Denmark
| | | | | | - A. Eugene DePrince
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, Florida 32306, USA
| | - Robert A. DiStasio
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, USA
| | - Andreas Dreuw
- Interdisciplinary Center for Scientific Computing, Ruprecht-Karls University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
| | - Barry D. Dunietz
- Department of Chemistry and Biochemistry, Kent State University, Kent, Ohio 44240, USA
| | - Thomas R. Furlani
- Department of Chemistry, University at Buffalo, State University of New York, Buffalo, New York 14260, USA
| | - William A. Goddard
- Materials and Process Simulation Center, California Institute of Technology, Pasadena, California 91125, USA
| | | | - Teresa Head-Gordon
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | | | | | | | - Yousung Jung
- Graduate School of Energy, Environment, Water and Sustainability (EEWS), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Andreas Klamt
- COSMOlogic GmbH & Co. KG, Imbacher Weg 46, D-51379 Leverkusen, Germany
| | - Jing Kong
- Q-Chem, Inc., 6601 Owens Drive, Suite 105, Pleasanton, California 94588, USA
| | - Daniel S. Lambrecht
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | | | | | - C. William McCurdy
- Department of Chemistry, University of California, Davis, California 95616, USA
| | - Jeffrey B. Neaton
- Department of Physics, University of California, Berkeley, California 94720, USA
| | - Christian Ochsenfeld
- Department of Chemistry, Ludwig Maximilian University, Butenandtstr. 7, D-81377 München, Germany
| | - John A. Parkhill
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, USA
| | - Roberto Peverati
- Department of Chemistry, Florida Institute of Technology, Melbourne, Florida 32901, USA
| | - Vitaly A. Rassolov
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, South Carolina 29208, USA
| | | | | | | | - Ryan P. Steele
- Department of Chemistry and Henry Eyring Center for Theoretical Chemistry, University of Utah, Salt Lake City, Utah 84112, USA
| | - Joseph E. Subotnik
- Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Alex J. W. Thom
- Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
| | - Alexandre Tkatchenko
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg, Luxembourg
| | - Donald G. Truhlar
- Department of Chemistry, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - Troy Van Voorhis
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Tomasz A. Wesolowski
- Department of Physical Chemistry, University of Geneva, 30, Quai Ernest-Ansermet, CH-1211 Geneva 4, Switzerland
| | - K. Birgitta Whaley
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - H. Lee Woodcock
- Department of Chemistry, University of South Florida, Tampa, Florida 33620, USA
| | - Paul M. Zimmerman
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Shirin Faraji
- Zernike Institute for Advanced Materials, University of Groningen, 9774AG Groningen, The Netherlands
| | | | - Martin Head-Gordon
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - John M. Herbert
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | - Anna I. Krylov
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, USA,Author to whom correspondence should be addressed:
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Chen AY, Brooks BR, Damjanovic A. Determinants of conductance of a bacterial voltage-gated sodium channel. Biophys J 2021; 120:3050-3069. [PMID: 34214541 DOI: 10.1016/j.bpj.2021.06.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 05/22/2021] [Accepted: 06/08/2021] [Indexed: 10/21/2022] Open
Abstract
Through molecular dynamics (MD) and free energy simulations in electric fields, we examine the factors influencing conductance of bacterial voltage-gated sodium channel NavMs. The channel utilizes four glutamic acid residues in the selectivity filter (SF). Previously, we have shown, through constant pH and free energy calculations of pKa values, that fully deprotonated, singly protonated, and doubly protonated states are all feasible at physiological pH, depending on how many ions are bound in the SF. With 173 MD simulations of 450 or 500 ns and additional free energy simulations, we determine that the conductance is highest for the deprotonated state and decreases with each additional proton bound. We also determine that the pKa value of the four glutamic residues for the transition between deprotonated and singly protonated states is close to the physiological pH and that there is a small voltage dependence. The pKa value and conductance trends are in agreement with experimental work on bacterial Nav channels, which show a decrease in maximal conductance with lowering of pH, with pKa in the physiological range. We examine binding sites for Na+ in the SF, compare with previous work, and note a dependence on starting structures. We find that narrowing of the gate backbone to values lower than the crystal structure's backbone radius reduces the conductance, whereas increasing the gate radius further does not affect the conductance. Simulations with some amount of negatively charged lipids as opposed to purely neutral lipids increases the conductance, as do simulations at higher voltages.
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Affiliation(s)
- Ada Y Chen
- Department of Physics & Astronomy, Johns Hopkins University, Baltimore, Maryland; Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - Ana Damjanovic
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland; Department of Biophysics, Johns Hopkins University, Baltimore, Maryland.
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Narayan B, Herbert C, Rodriguez BJ, Brooks BR, Buchete NV. Replica Exchange Molecular Dynamics of Diphenylalanine Amyloid Peptides in Electric Fields. J Phys Chem B 2021; 125:5233-5242. [PMID: 33990140 PMCID: PMC8279545 DOI: 10.1021/acs.jpcb.1c01939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The self-assembling propensity of amyloid peptides such as diphenylalanine (FF) allows them to form ordered, nanoscale structures, with biocompatible properties important for biomedical applications. Moreover, piezoelectric properties allow FF molecules and their aggregates (e.g., FF nanotubes) to be aligned in a controlled way by the application of external electric fields. However, while the behavior of FF nanostructures emerges from the biophysical properties of the monomers, the detailed responses of individual peptides to both temperature and electric fields are not fully understood. Here, we study the temperature-dependent conformational dynamics of FF peptides solvated in explicit water molecules, an environment relevant to biomedical applications, by using an enhanced sampling method, replica exchange molecular dynamics (REMD), in conjunction with applied electric fields. Our simulations highlight and overcome possible artifacts that may occur during the setup of REMD simulations of explicitly solvated peptides in the presence of external electric fields, a problem particularly important in the case of short peptides such as FF. The presence of the external fields could overstabilize certain conformational states in one or more REMD replicas, leading to distortions of the underlying potential energy distributions observed at each temperature. This can be overcome by correcting the REMD initial conditions to include the lower-energy conformations induced by the external field. We show that the converged REMD data can be analyzed using a Markovian description of conformational states and show that a rather complex, 3-state, temperature-dependent conformational dynamics in the absence of electric fields collapses to only one of these states in the presence of the electric fields. These details on the temperature- and electric-field-dependent thermodynamic and kinetic properties of small FF amyloid peptides can be useful in understanding and devising new methods to control their aggregation-prone biophysical properties and, possibly, the structural and biophysical properties of FF molecular nanostructures.
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Affiliation(s)
- Brajesh Narayan
- School of Physics, University College Dublin, Belfield, Dublin 4, Ireland.,Institute for Discovery, University College Dublin, Belfield, Dublin 4, Ireland
| | - Colm Herbert
- School of Physics, University College Dublin, Belfield, Dublin 4, Ireland.,Institute for Discovery, University College Dublin, Belfield, Dublin 4, Ireland
| | - Brian J Rodriguez
- School of Physics, University College Dublin, Belfield, Dublin 4, Ireland.,Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland
| | - Bernard R Brooks
- Laboratory of Computational Biology, NHLBI, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Nicolae-Viorel Buchete
- School of Physics, University College Dublin, Belfield, Dublin 4, Ireland.,Institute for Discovery, University College Dublin, Belfield, Dublin 4, Ireland
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18
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Prasad S, Simmonett AC, Meana-Pañeda R, Brooks BR. The Extended Eighth-Shell method for periodic boundary conditions with rotational symmetry. J Comput Chem 2021; 42:1373-1383. [PMID: 33977553 DOI: 10.1002/jcc.26545] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 02/18/2021] [Accepted: 03/25/2021] [Indexed: 11/07/2022]
Abstract
The Eighth-Shell method for parallelization of molecular dynamics simulations has previously been shown to be the most optimal for efficiency at large process counts. However, in its current formulation only the P1 space group is supported for periodic boundary conditions (PBC) and thus reflection and/or rotational crystal symmetries are not supported. In this work, we outline the development and implementation of the Extended Eighth-Shell (EES) method that allows rotational symmetry by using an extended import region compared to the ES method. It simulates only the asymmetric unit and communicates coordinates and forces with images that correspond to P21 PBC. The P21 PBC has application in lipid bilayer simulations as it can be used to allow lipids to switch leaftlets, thus rapidly balancing the chemical potential difference between the two layers. Our results show that the EES method scales efficiently over large number of processes and can be used for simulations with P21 symmetry in an orthorhombic crystal.
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Affiliation(s)
- Samarjeet Prasad
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, Maryland, USA
| | - Andrew C Simmonett
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, Maryland, USA
| | - Rubén Meana-Pañeda
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, Maryland, USA
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, Maryland, USA
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19
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Ghorbani M, Hudson PS, Jones MR, Aviat F, Meana-Pañeda R, Klauda JB, Brooks BR. A replica exchange umbrella sampling (REUS) approach to predict host-guest binding free energies in SAMPL8 challenge. J Comput Aided Mol Des 2021; 35:667-677. [PMID: 33939083 PMCID: PMC8131287 DOI: 10.1007/s10822-021-00385-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 04/12/2021] [Indexed: 12/14/2022]
Abstract
In this study, we report binding free energy calculations of various drugs-of-abuse to Cucurbit-[8]-uril as part of the SAMPL8 blind challenge. Force-field parameters were obtained from force-matching with different quantum mechanical levels of theory. The Replica Exchange Umbrella Sampling (REUS) approach was used with a cylindrical restraint to enhance the sampling of host–guest binding. Binding free energy was calculated by pulling the guest molecule from one side of the symmetric and cylindrical host, then into and through the host, and out the other side (bidirectional) as compared to pulling only to the bound pose inside the cylindrical host (unidirectional). The initial results with force-matched MP2 parameter set led to RMSE of 4.68 \documentclass[12pt]{minimal}
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\begin{document}$${\text{kcal}}/{\text{mol}}$$\end{document}kcal/mol from experimental values. However, the follow-up study with CHARMM generalized force field parameters and force-matched PM6-D3H4 parameters resulted in RMSEs from experiment of \documentclass[12pt]{minimal}
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\begin{document}$$1.72 {\text{kcal}}/{\text{mol}}$$\end{document}1.72kcal/mol, respectively, which demonstrates the potential of REUS for accurate binding free energy calculation given a more suitable description of energetics. Moreover, we compared the free energies for the so called bidirectional and unidirectional free energy approach and found that the binding free energies were highly similar. However, one issue in the bidirectional approach is the asymmetry of profile on the two sides of the host. This is mainly due to the insufficient sampling for these larger systems and can be avoided by longer sampling simulations. Overall REUS shows great promise for binding free energy calculations.
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Affiliation(s)
- Mahdi Ghorbani
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA. .,Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, 20740, USA.
| | - Phillip S Hudson
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Michael R Jones
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Félix Aviat
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Rubén Meana-Pañeda
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Jeffery B Klauda
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, 20740, USA
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
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20
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Abstract
The particle mesh Ewald (PME) method has become ubiquitous in the molecular simulation community due to its ability to deliver long range electrostatics accurately with ON log(N) complexity. Despite this widespread use, spanning more than two decades, second derivatives (Hessians) have not been available. In this work, we describe the theory and implementation of PME Hessians, which have applications in normal mode analysis, characterization of stationary points, phonon dispersion curve calculation, crystal structure prediction, and efficient geometry optimization. We outline an exact strategy that requires O(1) effort for each Hessian element; after discussing the excessive memory requirements of such an approach, we develop an accurate, efficient approximation that is far more tractable on commodity hardware.
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Affiliation(s)
- Andrew C Simmonett
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
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21
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Yu Y, Krämer A, Venable RM, Brooks BR, Klauda JB, Pastor RW. CHARMM36 Lipid Force Field with Explicit Treatment of Long-Range Dispersion: Parametrization and Validation for Phosphatidylethanolamine, Phosphatidylglycerol, and Ether Lipids. J Chem Theory Comput 2021; 17:1581-1595. [PMID: 33620194 PMCID: PMC8130185 DOI: 10.1021/acs.jctc.0c01327] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Long-range Lennard-Jones (LJ) interactions have been incorporated into the CHARMM36 (C36) lipid force field (FF) using the LJ particle-mesh Ewald (LJ-PME) method in order to remove the inconsistency of bilayer and monolayer properties arising from the exclusion of long-range dispersion [Yu, Y.; Semi-automated Optimization of the CHARMM36 Lipid Force Field to Include Explicit Treatment of Long-Range Dispersion. J. Chem. Theory Comput. 2021, 10.1021/acs.jctc.0c01326. (preceding article in this issue)]. The new FF is denoted C36/LJ-PME. While the first optimization was based on three phosphatidylcholines (PCs), this work extends the validation and parametrization to more lipids including PC, phosphatidylethanolamine (PE), phosphatidylglycerol (PG), and ether lipids. The agreement with experimental structure data is excellent for PC, PE, and ether lipids. C36/LJ-PME also compares favorably with scattering data of PG bilayers but less so with NMR deuterium order parameters of 1,2-dimyristoyl-sn-glycero-3-phospho-(1'-rac-glycerol) (DMPG) at 303.15 K, indicating a need for future optimization regarding PG-specific parameters. Frequency dependence of NMR T1 spin-lattice relaxation times is well-described by C36/LJ-PME, and the overall agreement with experiment is comparable to C36. Lipid diffusion is slower than C36 due to the added long-range dispersion causing a higher viscosity, although it is still too fast compared to experiment after correction for periodic boundary conditions. When using a 10 Å real-space cutoff, the simulation speed of C36/LJ-PME is roughly equal to C36. While more lipids will be incorporated into the FF in the future, C36/LJ-PME can be readily used for common lipids and extends the capability of the CHARMM FF by supporting monolayers and eliminating the cutoff dependence.
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Affiliation(s)
- Yalun Yu
- Biophysics Graduate Program, University of Maryland, College Park, Maryland 20742, United States
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Andreas Krämer
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Richard M Venable
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Jeffery B Klauda
- Biophysics Graduate Program, University of Maryland, College Park, Maryland 20742, United States
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, Maryland 20742, United States
| | - Richard W Pastor
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
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22
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Yu Y, Krämer A, Venable RM, Simmonett AC, MacKerell AD, Klauda JB, Pastor RW, Brooks BR. Semi-automated Optimization of the CHARMM36 Lipid Force Field to Include Explicit Treatment of Long-Range Dispersion. J Chem Theory Comput 2021; 17:1562-1580. [PMID: 33620214 PMCID: PMC8059446 DOI: 10.1021/acs.jctc.0c01326] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The development of the CHARMM lipid force field (FF) can be traced back to the early 1990s with its current version denoted CHARMM36 (C36). The parametrization of C36 utilized high-level quantum mechanical data and free energy calculations of model compounds before parameters were manually adjusted to yield agreement with experimental properties of lipid bilayers. While such manual fine-tuning of FF parameters is based on intuition and trial-and-error, automated methods can identify beneficial modifications of the parameters via their sensitivities and thereby guide the optimization process. This work introduces a semi-automated approach to reparametrize the CHARMM lipid FF with consistent inclusion of long-range dispersion through the Lennard-Jones particle-mesh Ewald (LJ-PME) approach. The optimization method is based on thermodynamic reweighting with regularization with respect to the C36 set. Two independent optimizations with different topology restrictions are presented. Targets of the optimizations are primarily liquid crystalline phase properties of lipid bilayers and the compression isotherm of monolayers. Pair correlation functions between water and lipid functional groups in aqueous solution are also included to address headgroup hydration. While the physics of the reweighting strategy itself is well-understood, applying it to heterogeneous, complex anisotropic systems poses additional challenges. These were overcome through careful selection of target properties and reweighting settings allowing for the successful incorporation of the explicit treatment of long-range dispersion, and we denote the newly optimized lipid force field as C36/LJ-PME. The current implementation of the optimization protocol will facilitate the future development of the CHARMM and related lipid force fields.
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Affiliation(s)
- Yalun Yu
- Biophysics Graduate Program, University of Maryland, College Park, Maryland 20742, United States
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Andreas Krämer
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Richard M Venable
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Andrew C Simmonett
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Alexander D MacKerell
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, Maryland 21201, United States
| | - Jeffery B Klauda
- Biophysics Graduate Program, University of Maryland, College Park, Maryland 20742, United States
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, Maryland 20742, United States
| | - Richard W Pastor
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
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23
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Man VH, Wu X, He X, Xie XQ, Brooks BR, Wang J. Determination of van der Waals Parameters Using a Double Exponential Potential for Nonbonded Divalent Metal Cations in TIP3P Solvent. J Chem Theory Comput 2021; 17:1086-1097. [PMID: 33503371 DOI: 10.1021/acs.jctc.0c01267] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
A double exponential (DE) functional form for Lennard-Jones (LJ) interactions, proposed in our previous study, has many advantages over LJ potentials including a natural softcore characteristic for the convenience of the pathway-based free-energy calculations, fast convergence, and flexibility in use. In this work, we put the first step on the application of the DE functional form by identifying a DE potential, coined DE-TIP3P, for molecular simulations using the TIP3P water model. The developed DE-TIP3 potential was better than LJ potential in reproducing the experimental water properties. Afterward, we developed the nonbonded models of 15 divalent metal ions, which frequently appear and play vital roles in biological systems, to be consistent with the DE-TIP3P potential and TIP3P water model. Our nonbonded models were as good as the complicated nonbonded dummy cationic models by Jiang et al. and the nonbonded 12-6-4 LJ models by Li and Merz in reproducing the experimental properties of those ions. Moreover, our nonbonded models achieved a better performance than the compromise (CM) LJ models and 12-6-4 LJ models, developed by Li and Merz, in reproducing the properties of MgCl2 in aqueous solution.
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Affiliation(s)
- Viet Hoang Man
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Xiongwu Wu
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institute of Health, Bethesda, Maryland 20892, United States
| | - Xibing He
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Xiang-Qun Xie
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institute of Health, Bethesda, Maryland 20892, United States
| | - Junmei Wang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
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24
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Abstract
Particle Mesh Ewald (PME) has become a standard method for treating long-range electrostatics in molecular simulations. Although the method has inferior asymptotic computational complexity to its linear scaling competitors, it remains enormously popular due to its high efficiency, which stems from the use of fast Fourier transforms (FFTs). This use of FFTs provides great challenges for scaling the method up to massively parallel systems, in large part because of the need to transfer large amounts of data. In this work, we demonstrate that this data transfer volume can be greatly reduced as a natural consequence of the structure of the PME equations. We also suggest an alternative algorithm that supplants the FFT with a linear algebra approach, which further decreases communication costs at the expense of increased asymptotic computational complexity. This linear algebra based approach is demonstrated to have great potential for latency hiding by interleaving communication and computation steps of the short- and long-range electrostatic terms.
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Affiliation(s)
- Andrew C Simmonett
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
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25
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Ghorbani M, Brooks BR, Klauda JB. An Integrative MD Simulation and Network Analysis Approach to Study Glycosylation of Spike in SARS-CoV-2. Biophys J 2021. [PMCID: PMC7879803 DOI: 10.1016/j.bpj.2020.11.364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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26
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Roe DR, Brooks BR. Improving the speed of volumetric density map generation via cubic spline interpolation. J Mol Graph Model 2021; 104:107832. [PMID: 33444979 DOI: 10.1016/j.jmgm.2021.107832] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/29/2020] [Accepted: 12/29/2020] [Indexed: 10/22/2022]
Abstract
Visualizing data generated from molecular dynamics simulations can be difficult, particularly when there can be thousands to millions of trajectory frames. The creation of a 3D grid of atomic density (i.e. a volumetric map) is one way to easily view the long-time average behavior of a system. One way to generate volumetric maps is by approximating each atom with a Gaussian function centered on that atom and spread over neighboring grid cells. However the calculation of the Gaussian function requires evaluation of the exponential function, which is computationally costly. Here we report on speeding up the calculation of volumetric maps from molecular dynamics trajectory data by replacing the expensive exponential function evaluation with an approximation using interpolating cubic splines. We also discuss the errors involved in this approximation, and recommend settings for volumetric map creation based on this.
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Affiliation(s)
- Daniel R Roe
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
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27
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Ghorbani M, Brooks BR, Klauda JB. Critical Sequence Hotspots for Binding of Novel Coronavirus to Angiotensin Converter Enzyme as Evaluated by Molecular Simulations. J Phys Chem B 2020; 124:10034-10047. [PMID: 33112147 PMCID: PMC7605337 DOI: 10.1021/acs.jpcb.0c05994] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 10/07/2020] [Indexed: 12/11/2022]
Abstract
The novel coronavirus (nCOV-2019) outbreak has put the world on edge, causing millions of cases and hundreds of thousands of deaths all around the world, as of June 2020, let alone the societal and economic impacts of the crisis. The spike protein of nCOV-2019 resides on the virion's surface mediating coronavirus entry into host cells by binding its receptor binding domain (RBD) to the host cell surface receptor protein, angiotensin converter enzyme (ACE2). Our goal is to provide a detailed structural mechanism of how nCOV-2019 recognizes and establishes contacts with ACE2 and its difference with an earlier severe acute respiratory syndrome coronavirus (SARS-COV) in 2002 via extensive molecular dynamics (MD) simulations. Numerous mutations have been identified in the RBD of nCOV-2019 strains isolated from humans in different parts of the world. In this study, we investigated the effect of these mutations as well as other Ala-scanning mutations on the stability of the RBD/ACE2 complex. It is found that most of the naturally occurring mutations to the RBD either slightly strengthen or have the same binding affinity to ACE2 as the wild-type nCOV-2019. This means that the virus had sufficient binding affinity to its receptor at the beginning of the crisis. This also has implications for any vaccine design endeavors since these mutations could act as antibody escape mutants. Furthermore, in silico Ala-scanning and long-timescale MD simulations highlight the crucial role of the residues at the interface of RBD and ACE2 that may be used as potential pharmacophores for any drug development endeavors. From an evolutional perspective, this study also identifies how the virus has evolved from its predecessor SARS-COV and how it could further evolve to become even more infectious.
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Affiliation(s)
- Mahdi Ghorbani
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD 20742, USA
- Laboratory of Computational Biology, National, Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20824, USA
| | - Bernard R. Brooks
- Laboratory of Computational Biology, National, Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20824, USA
| | - Jeffery B. Klauda
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD 20742, USA
- Biophysics Graduate Program, University of Maryland, College Park, MD 20742, USA
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28
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Krämer A, Ghysels A, Wang E, Venable RM, Klauda JB, Brooks BR, Pastor RW. Membrane permeability of small molecules from unbiased molecular dynamics simulations. J Chem Phys 2020; 153:124107. [PMID: 33003739 PMCID: PMC7519415 DOI: 10.1063/5.0013429] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 08/31/2020] [Indexed: 12/26/2022] Open
Abstract
Permeation of many small molecules through lipid bilayers can be directly observed in molecular dynamics simulations on the nano- and microsecond timescale. While unbiased simulations provide an unobstructed view of the permeation process, their feasibility for computing permeability coefficients depends on various factors that differ for each permeant. The present work studies three small molecules for which unbiased simulations of permeation are feasible within less than a microsecond, one hydrophobic (oxygen), one hydrophilic (water), and one amphiphilic (ethanol). Permeabilities are computed using two approaches: counting methods and a maximum-likelihood estimation for the inhomogeneous solubility diffusion (ISD) model. Counting methods yield nearly model-free estimates of the permeability for all three permeants. While the ISD-based approach is reasonable for oxygen, it lacks precision for water due to insufficient sampling and results in misleading estimates for ethanol due to invalid model assumptions. It is also demonstrated that simulations using a Langevin thermostat with collision frequencies of 1/ps and 5/ps yield oxygen permeabilities and diffusion constants that are lower than those using Nosé-Hoover by statistically significant margins. In contrast, permeabilities from trajectories generated with Nosé-Hoover and the microcanonical ensemble do not show statistically significant differences. As molecular simulations become more affordable and accurate, calculation of permeability for an expanding range of molecules will be feasible using unbiased simulations. The present work summarizes theoretical underpinnings, identifies pitfalls, and develops best practices for such simulations.
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Affiliation(s)
- Andreas Krämer
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - An Ghysels
- IBiTech - BioMMeda, Ghent University, Corneel Heymanslaan 10, Block B - Entrance 36, 9000 Gent, Belgium
| | - Eric Wang
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, Maryland 20740, USA
| | - Richard M. Venable
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Jeffery B. Klauda
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, Maryland 20740, USA
| | - Bernard R. Brooks
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Richard W. Pastor
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
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29
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Abstract
The ongoing pandemic caused by coronavirus SARS-COV-2 continues to rage with devastating consequences on human health and global economy. The spike glycoprotein on the surface of coronavirus mediates its entry into host cells and is the target of all current antibody design efforts to neutralize the virus. The glycan shield of the spike helps the virus to evade the human immune response by providing a thick sugar-coated barrier against any antibody. To study the dynamic motion of glycans in the spike protein, we performed microsecond-long MD simulation in two different states that correspond to the receptor binding domain in open or closed conformations. Analysis of this microsecond-long simulation revealed a scissoring motion on the N-terminal domain of neighboring monomers in the spike trimer. Role of multiple glycans in shielding of spike protein in different regions were uncovered by a network analysis, where the high betweenness centrality of glycans at the apex revealed their importance and function in the glycan shield. Microdomains of glycans were identified featuring a high degree of intra-communication in these microdomains. An antibody overlap analysis revealed the glycan microdomains as well as individual glycans that inhibit access to the antibody epitopes on the spike protein. Overall, the results of this study provide detailed understanding of the spike glycan shield, which may be utilized for therapeutic efforts against this crisis.
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30
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Abstract
Self-guided molecular/Langevin dynamics (SGMD/SGLD) simulation methods were developed to enhance conformational sampling through promoting low frequency motion of molecular systems and have been successfully applied in many simulation studies. Quantitative understanding of conformational distribution in SGLD has been achieved by separating microscopic properties according to frequency. However, a missing link between the guiding factors and conformational distributions makes it highly empirical and system dependent when choosing the values of the guiding parameters. Based on the understanding that molecular interactions are the source of energy barriers and diffusion friction, this work reformulates the equation of the low frequency motion to resemble Langevin dynamics. This reformulation leads to new forms of guiding forces and establishes a relation between the guiding factors and conformational distributions. We call simulations with these new guiding forces the generalized self-guided molecular/Langevin dynamics (SGMDg/SGLDg). In addition, we present a new way to calculate low frequency properties and an efficient algorithm to implement SGMDg/SGLDg that minimizes memory usage and inter-processor communication. Through example simulations with a skewed double well system, an argon fluid, and a cryo-EM map flexible fitting case, we demonstrate the guiding effects on conformational distributions and conformational searching.
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Affiliation(s)
- Xiongwu Wu
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), 12 South Dr., Bldg. 12A, Room 3053K, Bethesda, Maryland 20892, USA
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), 12 South Dr., Bldg. 12A, Room 3053K, Bethesda, Maryland 20892, USA
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31
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Roe DR, Brooks BR. A protocol for preparing explicitly solvated systems for stable molecular dynamics simulations. J Chem Phys 2020; 153:054123. [PMID: 32770927 PMCID: PMC7413747 DOI: 10.1063/5.0013849] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 07/19/2020] [Indexed: 11/14/2022] Open
Abstract
Before beginning the production phase of molecular dynamics simulations, i.e., the phase that produces the data to be analyzed, it is often necessary to first perform a series of one or more preparatory minimizations and/or molecular dynamics simulations in order to ensure that subsequent production simulations are stable. This is particularly important for simulations with explicit solvent molecules. Despite the preparatory minimizations and simulations being ubiquitous and essential for stable production simulations, there are currently no general recommended procedures to perform them and very few criteria to decide whether the system is capable of producing a stable simulation trajectory. Here, we propose a simple and well-defined ten step simulation preparation protocol for explicitly solvated biomolecules, which can be applied to a wide variety of system types, as well as a simple test based on the system density for determining whether the simulation is stabilized.
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Affiliation(s)
- Daniel R. Roe
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Bernard R. Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
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32
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Lee J, Brooks BR. Direct global optimization of Onsager-Machlup action using Action-CSA. Chem Phys 2020. [DOI: 10.1016/j.chemphys.2020.110768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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33
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Ghorbani M, Brooks BR, Klauda JB. Critical Sequence Hot-spots for Binding of nCOV-2019 to ACE2 as Evaluated by Molecular Simulations. bioRxiv 2020. [PMID: 32637962 DOI: 10.1101/2020.06.27.175448] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
The novel coronavirus (nCOV-2019) outbreak has put the world on edge, causing millions of cases and hundreds of thousands of deaths all around the world, as of June 2020, let alone the societal and economic impacts of the crisis. The spike protein of nCOV-2019 resides on the virion's surface mediating coronavirus entry into host cells by binding its receptor binding domain (RBD) to the host cell surface receptor protein, angiotensin converter enzyme (ACE2). Our goal is to provide a detailed structural mechanism of how nCOV-2019 recognizes and establishes contacts with ACE2 and its difference with an earlier coronavirus SARS-COV in 2002 via extensive molecular dynamics (MD) simulations. Numerous mutations have been identified in the RBD of nCOV-2019 strains isolated from humans in different parts of the world. In this study, we investigated the effect of these mutations as well as other Ala-scanning mutations on the stability of RBD/ACE2 complex. It is found that most of the naturally-occurring mutations to the RBD either strengthen or have the same binding affinity to ACE2 as the wild-type nCOV-2019. This may have implications for high human-to-human transmission of coronavirus in regions where these mutations have been found as well as any vaccine design endeavors since these mutations could act as antibody escape mutants. Furthermore, in-silico Ala-scanning and long-timescale MD simulations, highlight the crucial role of the residues at the interface of RBD and ACE2 that may be used as potential pharmacophores for any drug development endeavors. From an evolutional perspective, this study also identifies how the virus has evolved from its predecessor SARS-COV and how it could further evolve to become more infectious.
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34
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Smith DGA, Burns LA, Simmonett AC, Parrish RM, Schieber MC, Galvelis R, Kraus P, Kruse H, Di Remigio R, Alenaizan A, James AM, Lehtola S, Misiewicz JP, Scheurer M, Shaw RA, Schriber JB, Xie Y, Glick ZL, Sirianni DA, O’Brien JS, Waldrop JM, Kumar A, Hohenstein EG, Pritchard BP, Brooks BR, Schaefer HF, Sokolov AY, Patkowski K, DePrince AE, Bozkaya U, King RA, Evangelista FA, Turney JM, Crawford TD, Sherrill CD. Psi4 1.4: Open-source software for high-throughput quantum chemistry. J Chem Phys 2020; 152:184108. [PMID: 32414239 PMCID: PMC7228781 DOI: 10.1063/5.0006002] [Citation(s) in RCA: 301] [Impact Index Per Article: 75.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 04/12/2020] [Indexed: 12/13/2022] Open
Abstract
PSI4 is a free and open-source ab initio electronic structure program providing implementations of Hartree-Fock, density functional theory, many-body perturbation theory, configuration interaction, density cumulant theory, symmetry-adapted perturbation theory, and coupled-cluster theory. Most of the methods are quite efficient, thanks to density fitting and multi-core parallelism. The program is a hybrid of C++ and Python, and calculations may be run with very simple text files or using the Python API, facilitating post-processing and complex workflows; method developers also have access to most of PSI4's core functionalities via Python. Job specification may be passed using The Molecular Sciences Software Institute (MolSSI) QCSCHEMA data format, facilitating interoperability. A rewrite of our top-level computation driver, and concomitant adoption of the MolSSI QCARCHIVE INFRASTRUCTURE project, makes the latest version of PSI4 well suited to distributed computation of large numbers of independent tasks. The project has fostered the development of independent software components that may be reused in other quantum chemistry programs.
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Affiliation(s)
| | - Lori A. Burns
- Center for Computational Molecular Science and
Technology, School of Chemistry and Biochemistry, School of Computational Science and
Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0400,
USA
| | - Andrew C. Simmonett
- National Institutes of Health – National Heart,
Lung and Blood Institute, Laboratory of Computational Biology, Bethesda,
Maryland 20892, USA
| | - Robert M. Parrish
- Center for Computational Molecular Science and
Technology, School of Chemistry and Biochemistry, School of Computational Science and
Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0400,
USA
| | - Matthew C. Schieber
- Center for Computational Molecular Science and
Technology, School of Chemistry and Biochemistry, School of Computational Science and
Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0400,
USA
| | | | - Peter Kraus
- School of Molecular and Life Sciences, Curtin
University, Kent St., Bentley, Perth, Western Australia 6102,
Australia
| | - Holger Kruse
- Institute of Biophysics of the Czech Academy of
Sciences, Královopolská 135, 612 65 Brno, Czech
Republic
| | - Roberto Di Remigio
- Department of Chemistry, Centre for Theoretical
and Computational Chemistry, UiT, The Arctic University of Norway, N-9037
Tromsø, Norway
| | - Asem Alenaizan
- Center for Computational Molecular Science and
Technology, School of Chemistry and Biochemistry, School of Computational Science and
Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0400,
USA
| | - Andrew M. James
- Department of Chemistry, Virginia
Tech, Blacksburg, Virginia 24061, USA
| | - Susi Lehtola
- Department of Chemistry, University of
Helsinki, P.O. Box 55 (A. I. Virtasen aukio 1), FI-00014 Helsinki,
Finland
| | - Jonathon P. Misiewicz
- Center for Computational Quantum Chemistry,
University of Georgia, Athens, Georgia 30602, USA
| | - Maximilian Scheurer
- Interdisciplinary Center for Scientific
Computing, Heidelberg University, D-69120 Heidelberg,
Germany
| | - Robert A. Shaw
- ARC Centre of Excellence in Exciton Science,
School of Science, RMIT University, Melbourne, VIC 3000,
Australia
| | - Jeffrey B. Schriber
- Center for Computational Molecular Science and
Technology, School of Chemistry and Biochemistry, School of Computational Science and
Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0400,
USA
| | - Yi Xie
- Center for Computational Molecular Science and
Technology, School of Chemistry and Biochemistry, School of Computational Science and
Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0400,
USA
| | - Zachary L. Glick
- Center for Computational Molecular Science and
Technology, School of Chemistry and Biochemistry, School of Computational Science and
Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0400,
USA
| | - Dominic A. Sirianni
- Center for Computational Molecular Science and
Technology, School of Chemistry and Biochemistry, School of Computational Science and
Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0400,
USA
| | - Joseph Senan O’Brien
- Center for Computational Molecular Science and
Technology, School of Chemistry and Biochemistry, School of Computational Science and
Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0400,
USA
| | - Jonathan M. Waldrop
- Department of Chemistry and Biochemistry, Auburn
University, Auburn, Alabama 36849, USA
| | - Ashutosh Kumar
- Department of Chemistry, Virginia
Tech, Blacksburg, Virginia 24061, USA
| | - Edward G. Hohenstein
- SLAC National Accelerator Laboratory, Stanford
PULSE Institute, Menlo Park, California 94025,
USA
| | | | - Bernard R. Brooks
- National Institutes of Health – National Heart,
Lung and Blood Institute, Laboratory of Computational Biology, Bethesda,
Maryland 20892, USA
| | - Henry F. Schaefer
- Center for Computational Quantum Chemistry,
University of Georgia, Athens, Georgia 30602, USA
| | - Alexander Yu. Sokolov
- Department of Chemistry and Biochemistry, The
Ohio State University, Columbus, Ohio 43210, USA
| | - Konrad Patkowski
- Department of Chemistry and Biochemistry, Auburn
University, Auburn, Alabama 36849, USA
| | - A. Eugene DePrince
- Department of Chemistry and Biochemistry,
Florida State University, Tallahassee, Florida 32306-4390,
USA
| | - Uğur Bozkaya
- Department of Chemistry, Hacettepe
University, Ankara 06800, Turkey
| | - Rollin A. King
- Department of Chemistry, Bethel
University, St. Paul, Minnesota 55112, USA
| | | | - Justin M. Turney
- Center for Computational Quantum Chemistry,
University of Georgia, Athens, Georgia 30602, USA
| | | | - C. David Sherrill
- Center for Computational Molecular Science and
Technology, School of Chemistry and Biochemistry, School of Computational Science and
Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0400,
USA
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35
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Abstract
Water octanol partition coefficient serves as a measure for the lipophilicity of a molecule and is important in the field of drug discovery. A novel method for computational prediction of logarithm of partition coefficient (logP) has been developed using molecular fingerprints and a deep neural network. The machine learning model was trained on a dataset of 12,000 molecules and tested on 2000 molecules. In this article, we present our results for the blind prediction of logP for the SAMPL6 challenge. While the best submission achieved a RMSE of 0.41 logP units, our submission had a RMSE of 0.61 logP units. Overall, we ranked in the top quarter out of the 92 submissions that were made. Our results show that the deep learning model can be used as a fast, accurate and robust method for high throughput prediction of logP of small molecules.
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Affiliation(s)
- Samarjeet Prasad
- Biophysics and Biophysical Chemistry, The Johns Hopkins University, School of Medicine, Baltimore, MD, 21205, USA.
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20814, USA.
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20814, USA
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36
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Patel P, Kuntz DM, Jones MR, Brooks BR, Wilson AK. SAMPL6 logP challenge: machine learning and quantum mechanical approaches. J Comput Aided Mol Des 2020; 34:495-510. [PMID: 32002780 PMCID: PMC10817701 DOI: 10.1007/s10822-020-00287-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 01/08/2020] [Indexed: 10/25/2022]
Abstract
Two different types of approaches: (a) approaches that combine quantitative structure activity relationships, quantum mechanical electronic structure methods, and machine-learning and, (b) electronic structure vertical solvation approaches, were used to predict the logP coefficients of 11 molecules as part of the SAMPL6 logP blind prediction challenge. Using electronic structures optimized with density functional theory (DFT), several molecular descriptors were calculated for each molecule, including van der Waals areas and volumes, HOMO/LUMO energies, dipole moments, polarizabilities, and electrophilic and nucleophilic superdelocalizabilities. A multilinear regression model and a partial least squares model were used to train a set of 97 molecules. As well, descriptors were generated using the molecular operating environment and used to create additional machine learning models. Electronic structure vertical solvation approaches considered include DFT and the domain-based local pair natural orbital methods combined with the solvated variant of the correlation consistent composite approach.
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Affiliation(s)
- Prajay Patel
- Department of Chemistry, Michigan State University, East Lansing, MI, 48824-1322, USA
| | - David M Kuntz
- Department of Chemistry and Center for Advanced Scientific Computing and Modeling (CASCaM), University of North Texas, Denton, TX, 76203-5070, USA
| | - Michael R Jones
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20852-5690, USA
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20852-5690, USA
| | - Angela K Wilson
- Department of Chemistry, Michigan State University, East Lansing, MI, 48824-1322, USA.
- Department of Chemistry and Center for Advanced Scientific Computing and Modeling (CASCaM), University of North Texas, Denton, TX, 76203-5070, USA.
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37
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Krämer A, Hudson PS, Jones MR, Brooks BR. Multi-phase Boltzmann weighting: accounting for local inhomogeneity in molecular simulations of water-octanol partition coefficients in the SAMPL6 challenge. J Comput Aided Mol Des 2020; 34:471-483. [PMID: 32060677 PMCID: PMC8750956 DOI: 10.1007/s10822-020-00285-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 01/08/2020] [Indexed: 02/08/2023]
Abstract
Accurately computing partition coefficients is a pivotal part of drug discovery. Specifically, octanol-water partition coefficients can provide information into hydrophobicity of drug-like molecules, as well as a de facto representation of membrane permeability. However, one challenge facing the computation of partition coefficients is the need to encapsulate various microscopic environments. These include areas of largely bulk solvent (i.e., either water or octanol) or regions where octanol is saturated with water or areas of higher salt concentration. Also, tautomeric effects require consideration. Thus, we present a Boltzmann weighting approach that incorporates transfer free energies across varying microscopic media, as well as varying tautomeric state, to compute partition coefficients in the SAMPL6 challenge.
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Affiliation(s)
- Andreas Krämer
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
| | - Phillip S Hudson
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
- Department of Chemistry, University of South Florida, Tampa, FL, 33620, USA
| | - Michael R Jones
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
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38
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Jones MR, Brooks BR. Quantum chemical predictions of water-octanol partition coefficients applied to the SAMPL6 logP blind challenge. J Comput Aided Mol Des 2020; 34:485-493. [PMID: 32002778 DOI: 10.1007/s10822-020-00286-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 01/08/2020] [Indexed: 11/30/2022]
Abstract
Theoretical approaches for predicting physicochemical properties are valuable tools for accelerating the drug discovery process. In this work, quantum chemical methods are used to predict water-octanol partition coefficients as a part of the SAMPL6 blind challenge. The SMD continuum solvent model was employed with MP2 and eight DFT functionals in conjunction with correlation consistent basis sets to determine the water-octanol transfer free energy. Several tactics towards improving the predictions of the partition coefficient were examined, including increasing the quality of basis sets, considering tautomerization, and accounting for inhomogeneities in the water and n-octanol phases. Evaluation of these various schemes highlights the impact of modeling approaches across different methods. With the inclusion of tautomers and adjustments to the permittivity constants, the best predictions were obtained with smaller basis sets and the O3LYP functional, which yielded an RMSE of 0.79 logP units. The results presented correspond to the SAMPL6 logP submission IDs: DYXBT, O7DJK, and AHMTF.
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Affiliation(s)
- Michael R Jones
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20892-5690, USA.
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20892-5690, USA
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39
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Damjanovic A, Chen AY, Rosenberg RL, Roe DR, Wu X, Brooks BR. Protonation state of the selectivity filter of bacterial voltage‐gated sodium channels is modulated by ions. Proteins 2019; 88:527-539. [DOI: 10.1002/prot.25831] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 09/03/2019] [Accepted: 09/17/2019] [Indexed: 01/28/2023]
Affiliation(s)
- Ana Damjanovic
- Department of BiophysicsJohns Hopkins University Baltimore Maryland
| | - Ada Y. Chen
- Department of PhysicsJohns Hopkins University Baltimore Maryland
| | | | - Daniel R. Roe
- Laboratory of Computational Biology, National Heart, Lung and Blood InstituteNational Institutes of Health Bethesda Maryland
| | - Xiongwu Wu
- Laboratory of Computational Biology, National Heart, Lung and Blood InstituteNational Institutes of Health Bethesda Maryland
| | - Bernard R. Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood InstituteNational Institutes of Health Bethesda Maryland
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40
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Tian R, Zeng Q, Zhu S, Lau J, Chandra S, Ertsey R, Hettie KS, Teraphongphom T, Hu Z, Niu G, Kiesewetter DO, Sun H, Zhang X, Antaris AL, Brooks BR, Chen X. Albumin-chaperoned cyanine dye yields superbright NIR-II fluorophore with enhanced pharmacokinetics. Sci Adv 2019; 5:eaaw0672. [PMID: 31548981 PMCID: PMC6744268 DOI: 10.1126/sciadv.aaw0672] [Citation(s) in RCA: 142] [Impact Index Per Article: 28.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 08/15/2019] [Indexed: 05/22/2023]
Abstract
NIR-II fluorescence imaging greatly reduces scattering coefficients for nearly all tissue types at long wavelengths, benefiting deep tissue imaging. However, most of the NIR-II fluorophores suffer from low quantum yields and/or short circulation time that limit the quality of NIR-II imaging. Here, we engineered a supramolecular assembly of protein complex with lodged cyanine dyes to produce a brilliant NIR-II fluorophore, providing a NIR-II quantum yield of 21.2% with prolonged circulation time. Computational modeling revealed the mechanism for fluorescence enhancement and identified key parameters governing albumin complex for NIR-II fluorophores. Our complex afforded high-resolution microvessel imaging, with a 3-hour imaging window compared to 2 min for free dye alone. Furthermore, the complexation strategy was applied to an antibody-derived assembly, offering high-contrast tumor imaging without affecting the targeting ability of the antibody. This study provides a facile strategy for producing high-performance NIR-II fluorophores by chaperoning cyanine dyes with functional proteins.
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Affiliation(s)
- Rui Tian
- Laboratory of Molecular Imaging and Nanomedicine, National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Institutes of Health, Bethesda, MD 20892, USA
| | - Qiao Zeng
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Shoujun Zhu
- Laboratory of Molecular Imaging and Nanomedicine, National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Institutes of Health, Bethesda, MD 20892, USA
- Corresponding author. (S.Z.); (X.C.); (H.S.)
| | - Joseph Lau
- Laboratory of Molecular Imaging and Nanomedicine, National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Institutes of Health, Bethesda, MD 20892, USA
| | - Swati Chandra
- Laboratory of Molecular Imaging and Nanomedicine, National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Institutes of Health, Bethesda, MD 20892, USA
| | - Robert Ertsey
- Department of Otolaryngology, Stanford University, Stanford, CA 94305, USA
| | - Kenneth S. Hettie
- Department of Otolaryngology, Stanford University, Stanford, CA 94305, USA
| | - Tarn Teraphongphom
- Department of Otolaryngology, Stanford University, Stanford, CA 94305, USA
| | - Zhubin Hu
- State Key Laboratory of Precision Spectroscopy, School of Physics and Materials Science, East China Normal University, Shanghai 200062, P. R. China
| | - Gang Niu
- Laboratory of Molecular Imaging and Nanomedicine, National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Institutes of Health, Bethesda, MD 20892, USA
| | - Dale O. Kiesewetter
- Laboratory of Molecular Imaging and Nanomedicine, National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Institutes of Health, Bethesda, MD 20892, USA
| | - Haitao Sun
- State Key Laboratory of Precision Spectroscopy, School of Physics and Materials Science, East China Normal University, Shanghai 200062, P. R. China
- Corresponding author. (S.Z.); (X.C.); (H.S.)
| | - Xiaodong Zhang
- Department of Physics, School of Science, Tianjin University, Tianjin 300354, P. R. China
| | | | - Bernard R. Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Xiaoyuan Chen
- Laboratory of Molecular Imaging and Nanomedicine, National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Institutes of Health, Bethesda, MD 20892, USA
- Corresponding author. (S.Z.); (X.C.); (H.S.)
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41
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Tian R, Zhu S, Zeng Q, Lang L, Ma Y, Kiesewetter DO, Liu Y, Fu X, Lau J, Zhu G, Jacobson O, Wang Z, Dai Y, Yu G, Brooks BR, Liu G, Niu G, Chen X. An Albumin Sandwich Enhances in Vivo Circulation and Stability of Metabolically Labile Peptides. Bioconjug Chem 2019; 30:1711-1723. [PMID: 31082207 DOI: 10.1021/acs.bioconjchem.9b00258] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The effectiveness of numerous molecular drugs is hampered by their poor pharmacokinetics. Different from previous approaches with limited effectiveness, most recently, emerging high-affinity albumin binding moieties (ABMs) for in vivo hitchhiking of endogenous albumin opens up an avenue to chaperone small molecules for long-acting therapeutics. Although several FDA-approved fatty acids have shown prolonged residence and therapeutic effect, an easily synthesized, water-soluble, and high-efficiency ABM with versatile drug loading ability is urgently needed to improve the therapeutic efficacy of short-lived constructs. We herein identified an ideal bivalent Evans blue derivative, denoted as N(tEB)2, as a smart ABM-delivery platform to chaperone short-lived molecules, through both computational modeling screening and efficient synthetic schemes. The optimal N(tEB)2 could reversibly link two molecules of albumin through its two binding heads with a preferable spacer, resulting in significantly extended circulation half-life of a preloaded cargo and water-soluble. Notably, this in situ dimerization of albumin was able to sandwich peptide therapeutics to protect them from proteolysis. As an application, we conjugated N(tEB)2 with exendin-4 for long-acting glucose control in a diabetic mouse model, and it was superior to both previously tested NtEB-exendin-4 (Abextide) and the newly FDA-approved semaglutide, which has been arguably the best commercial weekly formula so far. Hence, this novel albumin binder has excellent clinical potential for next-generation biomimetic drug delivery systems.
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Affiliation(s)
- Rui Tian
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health , Xiamen University , Xiamen 361102 , China
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Gang Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health , Xiamen University , Xiamen 361102 , China
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42
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Wu X, Brooks BR. The homogeneity condition: A simple way to derive isotropic periodic sum potentials for efficient calculation of long-range interactions in molecular simulation. J Chem Phys 2019; 150:214109. [PMID: 31176325 DOI: 10.1063/1.5097560] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Isotropic periodic sum (IPS) is a method to calculate long-range interactions based on the homogeneity of simulation systems. By using the isotropic periodic images of a local region to represent remote structures, long-range interactions become a function of the local conformation. This function is called the IPS potential, which folds long-ranged interactions into a short-ranged potential and can be calculated as efficiently as a cutoff method. Analytic solutions of IPS potentials have been solved for many interaction types. To further simplify the application of the IPS method, this work presents the homogeneity condition, which requires the sum of interaction energies for any particle to be independent of cutoff distances for a truly homogeneous system. Using the homogeneity condition, one can avoid the complicated mathematic work to solve analytic solutions and can instead use simple functions as IPS potentials. Example simulations are performed for model systems of a series of interaction types. Energies, volumes, and their fluctuations from these simulations demonstrate that simple IPS potentials obtained through the homogeneity condition can satisfactorily describe long-range interactions. The homogeneity condition makes the IPS method a convenient way to handle long-range interactions of any type.
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Affiliation(s)
- Xiongwu Wu
- Laboratory of Computational Biology, NHLBI, NIH, 12 South Dr., Bldg. 12A, Room 3053K, Bethesda, Maryland 20892, USA
| | - Bernard R Brooks
- Laboratory of Computational Biology, NHLBI, NIH, 12 South Dr., Bldg. 12A, Room 3053K, Bethesda, Maryland 20892, USA
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43
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Abstract
Van der Waals (vdw) interaction is an important force between atoms and molecules. Many potential functions have been proposed to model vdw interaction such as the Lennard-Jones (L-J) potential. To overcome certain drawbacks of existing function forms, this work proposes a double exponential (DE) potential that contains a repulsive exponential term and an attractive exponential term. This potential decays faster than the L-J potential and has a soft core. The DE potential is very flexible and its two exponential parameters can be adjusted continuously to mimic many potential functions. Combined with the isotropic periodic sum (IPS) method, the DE potential can efficiently and accurately describe non-bonded interactions and is convenient for alchemical free energy calculation.
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Affiliation(s)
- Xiongwu Wu
- Corresponding author: Xiongwu Wu, Ph.D. Telephone: 301-827-1502. Fax: 301-402-2147.
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44
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Krämer A, Pickard FC, Huang J, Venable RM, Simmonett AC, Reith D, Kirschner KN, Pastor RW, Brooks BR. Interactions of Water and Alkanes: Modifying Additive Force Fields to Account for Polarization Effects. J Chem Theory Comput 2019; 15:3854-3867. [PMID: 31002505 DOI: 10.1021/acs.jctc.9b00016] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Atomistic biomolecular simulations predominantly utilize additive force fields (FF), where the electrostatic potential is modeled by fixed point charges. Among other consequences, the lack of polarizability in these models undermines the balance of hydrophilic/hydrophobic nonbonded interactions. Simulations of water/alkane systems using the TIP3P water model and CHARMM36 parameters reveal a 1 kcal/mol overestimate of the experimental transfer free energy of water to hexadecane; more recent optimized water models (SPC/E, TIP4P/2005, TIP4P-Ew, TIP3P-FB, TIP4P-FB, OPC, TIP4P-D) overestimate this transfer free energy by approximately 2 kcal/mol. In contrast, the polarizable SWM4-NDP and SWM6 water models reproduce experimental values to within statistical error. As an alternative to explicitly modeling polarizability, this paper develops an efficient automated workflow to optimize pair-specific Lennard-Jones parameters within an additive FF. Water/hexadecane is used as a prototype and the free energy of water transfer to hexadecane as a target. The optimized model yields quantitative agreement with the experimental transfer free energy and improves the water/hexadecane interfacial tension by 6%. Simulations of five different lipid bilayers show a strong increase of water permeabilities compared to the unmodified CHARMM36 lipid FF which consistently improves match with experiment: the order-of-magnitude underestimate for monounsaturated bilayers is rectified and the factor of 2.8-4 underestimate for saturated bilayers is turned into a factor of 1.5-3 overestimate. While agreement with experiment is decreased for the diffusion constant of water in hexadecane, alkane transfer free energies, and the bilayers' area per lipid, the method provides a permeant-specific route to achieve a wide range of heterogeneous observables via rapidly optimized pairwise parameters.
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Affiliation(s)
- Andreas Krämer
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute , National Institutes of Health , Bethesda , Maryland 20892 , United States.,Institute of Technology, Resource and Energy-Efficient Engineering , Bonn-Rhein-Sieg University of Applied Sciences , Grantham-Allee 20 , 53757 Sankt Augustin , Germany
| | - Frank C Pickard
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute , National Institutes of Health , Bethesda , Maryland 20892 , United States
| | - Jing Huang
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute , National Institutes of Health , Bethesda , Maryland 20892 , United States.,School of Life Sciences , Westlake University , 18 Shilongshan Road , Hangzhou 310024 , Zhejiang China.,Department of Pharmaceutical Science, School of Pharmacy , University of Maryland , 20 Penn Street , Baltimore , Maryland 21201 , United States
| | - Richard M Venable
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute , National Institutes of Health , Bethesda , Maryland 20892 , United States
| | - Andrew C Simmonett
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute , National Institutes of Health , Bethesda , Maryland 20892 , United States
| | - Dirk Reith
- Institute of Technology, Resource and Energy-Efficient Engineering , Bonn-Rhein-Sieg University of Applied Sciences , Grantham-Allee 20 , 53757 Sankt Augustin , Germany
| | - Karl N Kirschner
- Institute of Technology, Resource and Energy-Efficient Engineering , Bonn-Rhein-Sieg University of Applied Sciences , Grantham-Allee 20 , 53757 Sankt Augustin , Germany
| | - Richard W Pastor
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute , National Institutes of Health , Bethesda , Maryland 20892 , United States
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute , National Institutes of Health , Bethesda , Maryland 20892 , United States
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Lee J, Lee IH, Joung I, Lee J, Brooks BR. Finding Multiple Reaction Pathways via Global Optimization of Action. Biophys J 2019. [DOI: 10.1016/j.bpj.2018.11.1642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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46
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Tywoniuk B, Yuan Y, McCartan S, Szydłowska BM, Tofoleanu F, Brooks BR, Buchete NV. Amyloid Fibril Design: Limiting Structural Polymorphism in Alzheimer's Aβ Protofilaments. J Phys Chem B 2018; 122:11535-11545. [PMID: 30335383 DOI: 10.1021/acs.jpcb.8b07423] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Nanoscale fibrils formed by amyloid peptides have a polymorphic character, adopting several types of molecular structures in similar growth conditions. As shown by experimental (e.g., solid-state NMR) and computational studies, amyloid fibril polymorphism hinders both the structural characterization of Alzheimer's Aβ amyloid protofilaments and fibrils at a molecular level, as well as the possible applications (e.g., development of drugs or biomarkers) that rely on similar, controlled molecular arrangements of the Aβ peptides in amyloid fibril structures. We have explored the use of several contact potentials for the efficient identification of minimal sequence mutations that could enhance the stability of specific fibril structures while simultaneously destabilizing competing topologies, controlling thus the amount of structural polymorphism in a rational way. We found that different types of contact potentials, while having only partial accuracy on their own, lead to similar results regarding ranking the compatibility of wild-type (WT) and mutated amyloid sequences with different fibril morphologies. This approach allows exhaustive screening and assessment of possible mutations and the identification of minimal consensus mutations that could stabilize fibrils with the desired topology at the expense of other topology types, a prediction that is further validated using atomistic molecular dynamics with explicit water molecules. We apply this two-step multiscale (i.e., residue and atomistic-level) approach to predict and validate mutations that could bias either parallel or antiparallel packing in the core Alzheimer's Aβ9-40 amyloid fibril models based on solid-state NMR experiments. Besides shedding new light on the molecular origins of structural polymorphism in WT Aβ fibrils, our study could also lead to efficient tools for assisting future experimental approaches for amyloid fibril determination, and for the development of biomarkers or drugs aimed at interfering with the stability of amyloid fibrils, as well as for the future design of amyloid fibrils with a controlled (e.g., reduced) level of structural polymorphism.
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Affiliation(s)
- Bartłomiej Tywoniuk
- School of Physics , University College Dublin , Dublin D04 V1W8 , Ireland.,Institute for Discovery , University College Dublin , Dublin D04 V1W8 , Ireland
| | - Ye Yuan
- School of Physics , University College Dublin , Dublin D04 V1W8 , Ireland.,Institute for Discovery , University College Dublin , Dublin D04 V1W8 , Ireland
| | - Sarah McCartan
- School of Physics , University College Dublin , Dublin D04 V1W8 , Ireland.,Institute for Discovery , University College Dublin , Dublin D04 V1W8 , Ireland
| | - Beata Maria Szydłowska
- Applied Physical Chemistry , Ruprecht-Karls University Heidelberg , Heidelberg 69120 , Germany.,Institute of Physics, EIT 2 , Universität der Bundeswehr München , Werner-Heisenberg-Weg 39 , 85577 Neubiberg , Germany
| | - Florentina Tofoleanu
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute , National Institutes of Health , Bethesda , Maryland 20892 , United States.,Department of Chemistry , Yale University , New Haven , Connecticut 06520 , United States
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute , National Institutes of Health , Bethesda , Maryland 20892 , United States
| | - Nicolae-Viorel Buchete
- School of Physics , University College Dublin , Dublin D04 V1W8 , Ireland.,Institute for Discovery , University College Dublin , Dublin D04 V1W8 , Ireland
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47
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König G, Pickard FC, Huang J, Thiel W, MacKerell AD, Brooks BR, York DM. A Comparison of QM/MM Simulations with and without the Drude Oscillator Model Based on Hydration Free Energies of Simple Solutes. Molecules 2018; 23:E2695. [PMID: 30347691 PMCID: PMC6222909 DOI: 10.3390/molecules23102695] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2018] [Revised: 10/15/2018] [Accepted: 10/16/2018] [Indexed: 12/01/2022] Open
Abstract
Maintaining a proper balance between specific intermolecular interactions and non-specific solvent interactions is of critical importance in molecular simulations, especially when predicting binding affinities or reaction rates in the condensed phase. The most rigorous metric for characterizing solvent affinity are solvation free energies, which correspond to a transfer from the gas phase into solution. Due to the drastic change of the electrostatic environment during this process, it is also a stringent test of polarization response in the model. Here, we employ both the CHARMM fixed charge and polarizable force fields to predict hydration free energies of twelve simple solutes. The resulting classical ensembles are then reweighted to obtain QM/MM hydration free energies using a variety of QM methods, including MP2, Hartree⁻Fock, density functional methods (BLYP, B3LYP, M06-2X) and semi-empirical methods (OM2 and AM1 ). Our simulations test the compatibility of quantum-mechanical methods with molecular-mechanical water models and solute Lennard⁻Jones parameters. In all cases, the resulting QM/MM hydration free energies were inferior to purely classical results, with the QM/MM Drude force field predictions being only marginally better than the QM/MM fixed charge results. In addition, the QM/MM results for different quantum methods are highly divergent, with almost inverted trends for polarizable and fixed charge water models. While this does not necessarily imply deficiencies in the QM models themselves, it underscores the need to develop consistent and balanced QM/MM interactions. Both the QM and the MM component of a QM/MM simulation have to match, in order to avoid artifacts due to biased solute⁻solvent interactions. Finally, we discuss strategies to improve the convergence and efficiency of multi-scale free energy simulations by automatically adapting the molecular-mechanics force field to the target quantum method.
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Affiliation(s)
- Gerhard König
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA.
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine, Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA.
- Max-Planck-Institut für Kohlenforschung, 45470 Mülheim an der Ruhr, Germany.
| | - Frank C Pickard
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Jing Huang
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA.
- Department of Pharmaceutical Science, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, MD 21201, USA.
- School of Life Sciences, Westlake University, 18 Shilongshan Street, Hangzhou 310024, China.
| | - Walter Thiel
- Max-Planck-Institut für Kohlenforschung, 45470 Mülheim an der Ruhr, Germany.
| | - Alexander D MacKerell
- Department of Pharmaceutical Science, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, MD 21201, USA.
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Darrin M York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine, Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA.
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48
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Hudson PS, Han K, Woodcock HL, Brooks BR. Force matching as a stepping stone to QM/MM CB[8] host/guest binding free energies: a SAMPL6 cautionary tale. J Comput Aided Mol Des 2018; 32:983-999. [PMID: 30276502 PMCID: PMC6867086 DOI: 10.1007/s10822-018-0165-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 09/14/2018] [Indexed: 10/28/2022]
Abstract
Use of quantum mechanical/molecular mechanical (QM/MM) methods in binding free energy calculations, particularly in the SAMPL challenge, often fail to achieve improvement over standard additive (MM) force fields. Frequently, the implementation is through use of reference potentials, or the so-called "indirect approach", and inherently relies on sufficient overlap existing between MM and QM/MM configurational spaces. This overlap is generally poor, particularly for the use of free energy perturbation to perform the MM to QM/MM free energy correction at the end states of interest (e.g., bound and unbound states). However, by utilizing MM parameters that best reproduce forces obtained at the desired QM level of theory, it is possible to lessen the configurational disparity between MM and QM/MM. To this end, we sought to use force matching to generate MM parameters for the SAMPL6 CB[8] host-guest binding challenge, classically compute binding free energies, and apply energetic end state corrections to obtain QM/MM binding free energy differences. For the standard set of 11 molecules and the bonus set (including three additional challenge molecules), error statistics, such as the root mean square deviation (RMSE) were moderately poor (5.5 and 5.4 kcal/mol). Correlation statistics, however, were in the top two for both standard and bonus set submissions ([Formula: see text] of 0.42 and 0.26, [Formula: see text] of 0.64 and 0.47 respectively). High RMSE and moderate correlation strongly indicated the presence of systematic error. Identifiable issues were ameliorated for two of the guest molecules, resulting in a reduction of error and pointing to strong prospects for the future use of this methodology.
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Affiliation(s)
- Phillip S Hudson
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA.
- Department of Chemistry, University of South Florida, Tampa, Florida, 33620, USA.
| | - Kyungreem Han
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA
| | - H Lee Woodcock
- Department of Chemistry, University of South Florida, Tampa, Florida, 33620, USA
| | - Bernard R Brooks
- Department of Chemistry, University of South Florida, Tampa, Florida, 33620, USA
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49
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Prasad S, Huang J, Zeng Q, Brooks BR. An explicit-solvent hybrid QM and MM approach for predicting pKa of small molecules in SAMPL6 challenge. J Comput Aided Mol Des 2018; 32:1191-1201. [PMID: 30276503 PMCID: PMC6342563 DOI: 10.1007/s10822-018-0167-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Accepted: 09/25/2018] [Indexed: 12/30/2022]
Abstract
In this work we have developed a hybrid QM and MM approach to predict pKa of small drug-like molecules in explicit solvent. The gas phase free energy of deprotonation is calculated using the M06-2X density functional theory level with Pople basis sets. The solvation free energy difference of the acid and its conjugate base is calculated at MD level using thermodynamic integration. We applied this method to the 24 drug-like molecules in the SAMPL6 blind pKa prediction challenge. We achieved an overall RMSE of 2.4 pKa units in our prediction. Our results show that further optimization of the protocol needs to be done before this method can be used as an alternative approach to the well established approaches of a full quantum level or empirical pKa prediction methods.
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Affiliation(s)
- Samarjeet Prasad
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20814, USA.
- Biophysics and Biophysical Chemistry, The Johns Hopkins University, School of Medicine, Baltimore, MD, 21205, USA.
| | - Jing Huang
- School of Life Sciences, Westlake University, 18 Shilongshan Street, Xihu District, Hangzhou, Zhejiang, China
| | - Qiao Zeng
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20814, USA
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50
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Narayan B, Herbert C, Yuan Y, Rodriguez BJ, Brooks BR, Buchete NV. Conformational analysis of replica exchange MD: Temperature-dependent Markov networks for FF amyloid peptides. J Chem Phys 2018; 149:072323. [PMID: 30134732 DOI: 10.1063/1.5027580] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Recent molecular modeling methods using Markovian descriptions of conformational states of biomolecular systems have led to powerful analysis frameworks that can accurately describe their complex dynamical behavior. In conjunction with enhanced sampling methods, such as replica exchange molecular dynamics (REMD), these frameworks allow the systematic and accurate extraction of transition probabilities between the corresponding states, in the case of Markov state models, and of statistically-optimized transition rates, in the case of the corresponding coarse master equations. However, applying automatically such methods to large molecular dynamics (MD) simulations, with explicit water molecules, remains limited both by the initial ability to identify good candidates for the underlying Markovian states and by the necessity to do so using good collective variables as reaction coordinates that allow the correct counting of inter-state transitions at various lag times. Here, we show that, in cases when representative molecular conformations can be identified for the corresponding Markovian states, and thus their corresponding collective evolution of atomic positions can be calculated along MD trajectories, one can use them to build a new type of simple collective variable, which can be particularly useful in both the correct state assignment and in the subsequent accurate counting of inter-state transition probabilities. In the case of the ubiquitously used root-mean-square deviation (RMSD) of atomic positions, we introduce the relative RMSD (RelRMSD) measure as a good reaction coordinate candidate. We apply this method to the analysis of REMD trajectories of amyloid-forming diphenylalanine (FF) peptides-a system with important nanotechnology and biomedical applications due to its self-assembling and piezoelectric properties-illustrating the use of RelRMSD in extracting its temperature-dependent intrinsic kinetics, without a priori assumptions on the functional form (e.g., Arrhenius or not) of the underlying conformational transition rates. The RelRMSD analysis enables as well a more objective assessment of the convergence of the REMD simulations. This type of collective variable may be generalized to other observables that could accurately capture conformational differences between the underlying Markov states (e.g., distance RMSD, the fraction of native contacts, etc.).
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Affiliation(s)
- Brajesh Narayan
- School of Physics, University College Dublin, Belfield, Dublin 4, Ireland
| | - Colm Herbert
- School of Physics, University College Dublin, Belfield, Dublin 4, Ireland
| | - Ye Yuan
- School of Physics, University College Dublin, Belfield, Dublin 4, Ireland
| | - Brian J Rodriguez
- School of Physics, University College Dublin, Belfield, Dublin 4, Ireland
| | - Bernard R Brooks
- Laboratory of Computational Biology, NHLBI, National Institutes of Health, Bethesda, Maryland 20892, USA
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