1
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Corrigan RA, Thiel AC, Lynn JR, Casavant TL, Ren P, Ponder JW, Schnieders MJ. A generalized Kirkwood implicit solvent for the polarizable AMOEBA protein model. J Chem Phys 2023; 159:054102. [PMID: 37526158 PMCID: PMC10396400 DOI: 10.1063/5.0158914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 07/17/2023] [Indexed: 08/02/2023] Open
Abstract
Computational simulation of biomolecules can provide important insights into protein design, protein-ligand binding interactions, and ab initio biomolecular folding, among other applications. Accurate treatment of the solvent environment is essential in such applications, but the use of explicit solvents can add considerable cost. Implicit treatment of solvent effects using a dielectric continuum model is an attractive alternative to explicit solvation since it is able to describe solvation effects without the inclusion of solvent degrees of freedom. Previously, we described the development and parameterization of implicit solvent models for small molecules. Here, we extend the parameterization of the generalized Kirkwood (GK) implicit solvent model for use with biomolecules described by the AMOEBA force field via the addition of corrections to the calculation of effective radii that account for interstitial spaces that arise within biomolecules. These include element-specific pairwise descreening scale factors, a short-range neck contribution to describe the solvent-excluded space between pairs of nearby atoms, and finally tanh-based rescaling of the overall descreening integral. We then apply the AMOEBA/GK implicit solvent to a set of ten proteins and achieve an average coordinate root mean square deviation for the experimental structures of 2.0 Å across 500 ns simulations. Overall, the continued development of implicit solvent models will help facilitate the simulation of biomolecules on mechanistically relevant timescales.
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Affiliation(s)
- Rae A. Corrigan
- Roy J. Carver Department of Biomedical Engineering, The University of Iowa, Iowa City, Iowa 52242, USA
| | - Andrew C. Thiel
- Roy J. Carver Department of Biomedical Engineering, The University of Iowa, Iowa City, Iowa 52242, USA
| | - Jack R. Lynn
- Roy J. Carver Department of Biomedical Engineering, The University of Iowa, Iowa City, Iowa 52242, USA
| | - Thomas L. Casavant
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, Iowa 52242, USA
| | - Pengyu Ren
- Department of Biomedical Engineering, The University of Texas in Austin, Austin, Texas 78712, USA
| | - Jay W. Ponder
- Department of Chemistry, Washington University in St. Louis, St. Louis, Missouri 63130, USA
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2
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Wang KW, Lee J, Zhang H, Suh D, Im W. CHARMM-GUI Implicit Solvent Modeler for Various Generalized Born Models in Different Simulation Programs. J Phys Chem B 2022; 126:7354-7364. [PMID: 36117287 DOI: 10.1021/acs.jpcb.2c05294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Implicit solvent models are widely used because they are advantageous to speed up simulations by drastically decreasing the number of solvent degrees of freedom, which allows one to achieve long simulation time scales for large system sizes. CHARMM-GUI, a web-based platform, has been developed to support the setup of complex multicomponent molecular systems and prepare input files. This study describes an Implicit Solvent Modeler (ISM) in CHARMM-GUI for various generalized Born (GB) implicit solvent simulations in different molecular dynamics programs such as AMBER, CHARMM, GENESIS, NAMD, OpenMM, and Tinker. The GB models available in ISM include GB-HCT, GB-OBC, GB-neck, GBMV, and GBSW with the CHARMM and Amber force fields for protein, DNA, RNA, glycan, and ligand systems. Using the system and input files generated by ISM, implicit solvent simulations of protein, DNA, and RNA systems produce similar results for different simulation packages with the same input information. Protein-ligand systems are also considered to further validate the systems and input files generated by ISM. Simple ligand root-mean-square deviation (RMSD) and molecular mechanics generalized Born surface area (MM/GBSA) calculations show that the performance of implicit simulations is better than docking and can be used for early stage ligand screening. These reasonable results indicate that ISM is a useful and reliable tool to provide various implicit solvent simulation applications.
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Affiliation(s)
- Kye Won Wang
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Jumin Lee
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Han Zhang
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Donghyuk Suh
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Wonpil Im
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
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3
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Henderson JA, Liu R, Harris JA, Huang Y, de Oliveira VM, Shen J. A Guide to the Continuous Constant pH Molecular Dynamics Methods in Amber and CHARMM [Article v1.0]. LIVING JOURNAL OF COMPUTATIONAL MOLECULAR SCIENCE 2022; 4:1563. [PMID: 36776714 PMCID: PMC9910290 DOI: 10.33011/livecoms.4.1.1563] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Like temperature and pressure, solution pH is an important environmental variable in biomolecular simulations. Virtually all proteins depend on pH to maintain their structure and function. In conventional molecular dynamics (MD) simulations of proteins, pH is implicitly accounted for by assigning and fixing protonation states of titratable sidechains. This is a significant limitation, as the assigned protonation states may be wrong and they may change during dynamics. In this tutorial, we guide the reader in learning and using the various continuous constant pH MD methods in Amber and CHARMM packages, which have been applied to predict pK a values and elucidate proton-coupled conformational dynamics of a variety of proteins including enzymes and membrane transporters.
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Affiliation(s)
| | - Ruibin Liu
- University of Maryland School of Pharmacy, Baltimore, MD
| | | | - Yandong Huang
- University of Maryland School of Pharmacy, Baltimore, MD
| | | | - Jana Shen
- University of Maryland School of Pharmacy, Baltimore, MD
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4
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Thorat SA, Lee Y, Jung A, Ann J, Ahn S, Baek J, Zuo D, Do N, Jeong JJ, Blumberg PM, Esch TE, Turcios NA, Pearce LV, Ha HJ, Yoo YD, Hong S, Choi S, Lee J. Discovery of Benzopyridone-Based Transient Receptor Potential Vanilloid 1 Agonists and Antagonists and the Structural Elucidation of Their Activity Shift. J Med Chem 2021; 64:370-384. [PMID: 33385210 DOI: 10.1021/acs.jmedchem.0c00982] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Among a series of benzopyridone-based scaffolds investigated as human transient receptor potential vanilloid 1 (TRPV1) ligands, two isomeric benzopyridone scaffolds demonstrated a consistent and distinctive functional profile in which 2-oxo-1,2-dihydroquinolin-5-yl analogues (e.g., 2) displayed high affinity and potent antagonism, whereas 1-oxo-1,2-dihydroisoquinolin-5-yl analogues (e.g., 3) showed full agonism with high potency. Our computational models provide insight into the agonist-antagonist boundary of the analogues suggesting that the Arg557 residue in the S4-S5 linker might be important for sensing the agonist binding and transmitting signals. These results provide structural insights into the TRPV1 and the protein-ligand interactions at a molecular level.
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Affiliation(s)
- Shivaji A Thorat
- Laboratory of Medicinal Chemistry, College of Pharmacy, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Yoonji Lee
- Laboratory of Molecular Modeling & Drug Design, College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Republic of Korea.,College of Pharmacy, Chung-Ang University, Seoul 06974, Korea
| | - Aeran Jung
- Laboratory of Medicinal Chemistry, College of Pharmacy, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Jihyae Ann
- Laboratory of Medicinal Chemistry, College of Pharmacy, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Songyeon Ahn
- Laboratory of Medicinal Chemistry, College of Pharmacy, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Jisoo Baek
- Laboratory of Medicinal Chemistry, College of Pharmacy, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Dongxu Zuo
- Laboratory of Medicinal Chemistry, College of Pharmacy, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Nayeon Do
- Laboratory of Medicinal Chemistry, College of Pharmacy, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Jin Ju Jeong
- Laboratory of Medicinal Chemistry, College of Pharmacy, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Peter M Blumberg
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland 20892-4255, United States
| | - Timothy E Esch
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland 20892-4255, United States
| | - Noe A Turcios
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland 20892-4255, United States
| | - Larry V Pearce
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland 20892-4255, United States
| | - Hee-Jin Ha
- Medifron DBT, Sandanro 349, Danwon-gu, Ansan-si, Gyeonggi-do 15426, Republic of Korea
| | - Young Dong Yoo
- Medifron DBT, Sandanro 349, Danwon-gu, Ansan-si, Gyeonggi-do 15426, Republic of Korea
| | - Sunhye Hong
- Laboratory of Molecular Modeling & Drug Design, College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Sun Choi
- Laboratory of Molecular Modeling & Drug Design, College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Jeewoo Lee
- Laboratory of Medicinal Chemistry, College of Pharmacy, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
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5
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Horvath D, Marcou G, Varnek A. "Big Data" Fast Chemoinformatics Model to Predict Generalized Born Radius and Solvent Accessibility as a Function of Geometry. J Chem Inf Model 2020; 60:2951-2965. [PMID: 32374171 DOI: 10.1021/acs.jcim.9b01172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The Generalized Born (GB) solvent model is offering the best accuracy/computing effort ratio yet requires drastic simplifications to estimate of the Effective Born Radii (EBR) in bypassing a too expensive volume integration step. EBRs are a measure of the degree of burial of an atom and not very sensitive to small changes of geometry: in molecular dynamics, the costly EBR update procedure is not mandatory at every step. This work however aims at implementing a GB model into the Sampler for Multiple Protein-Ligand Entities (S4MPLE) evolutionary algorithm with mandatory EBR updates at each step triggering arbitrarily large geometric changes. Therefore, a quantitative structure-property relationship has been developed in order to express the EBRs as a linear function of both the topological neighborhood and geometric occupancy of the space around atoms. A training set of 810 molecular systems, starting from fragment-like to drug-like compounds, proteins, host-guest systems, and ligand-protein complexes, has been compiled. For each species, S4MPLE generated several hundreds of random conformers. For each atom in each geometry of each species, its "standard" EBR was calculated by numeric integration and associated to topological and geometric descriptors of the atom neighborhood. This training set (EBR, atom descriptors) involving >5 M entries was subjected to a boot-strapping multilinear regression process with descriptor selection. In parallel, the strategy was repurposed to also learn atomic solvent-accessible areas (SA) based on the same descriptors. Resulting linear equations were challenged to predict EBR and SA values for a similarly compiled external set of >2000 new molecular systems. Solvation energies calculated with estimated EBR and SA match "standard" energies within the typical error of a force-field-based approach (a few kilocalories per mole). Given the extreme diversity of molecular systems covered by the model, this simple EBR/SA estimator covers a vast applicability domain.
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Affiliation(s)
- Dragos Horvath
- Laboratory of Chemoinformatics, UMR 7140 University of Strasbourg/CNRS, 4 rue Blaise Pascal, 67000 Strasbourg, France
| | - Gilles Marcou
- Laboratory of Chemoinformatics, UMR 7140 University of Strasbourg/CNRS, 4 rue Blaise Pascal, 67000 Strasbourg, France
| | - Alexandre Varnek
- Laboratory of Chemoinformatics, UMR 7140 University of Strasbourg/CNRS, 4 rue Blaise Pascal, 67000 Strasbourg, France
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6
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Loschwitz J, Olubiyi OO, Hub JS, Strodel B, Poojari CS. Computer simulations of protein-membrane systems. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2020; 170:273-403. [PMID: 32145948 PMCID: PMC7109768 DOI: 10.1016/bs.pmbts.2020.01.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The interactions between proteins and membranes play critical roles in signal transduction, cell motility, and transport, and they are involved in many types of diseases. Molecular dynamics (MD) simulations have greatly contributed to our understanding of protein-membrane interactions, promoted by a dramatic development of MD-related software, increasingly accurate force fields, and available computer power. In this chapter, we present available methods for studying protein-membrane systems with MD simulations, including an overview about the various all-atom and coarse-grained force fields for lipids, and useful software for membrane simulation setup and analysis. A large set of case studies is discussed.
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Affiliation(s)
- Jennifer Loschwitz
- Institute of Theoretical and Computational Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, Jülich, Germany
| | - Olujide O Olubiyi
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, Jülich, Germany; Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Obafemi Awolowo University, Ile-Ife, Nigeria
| | - Jochen S Hub
- Theoretical Physics and Center for Biophysics, Saarland University, Saarbrücken, Germany
| | - Birgit Strodel
- Institute of Theoretical and Computational Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, Jülich, Germany
| | - Chetan S Poojari
- Theoretical Physics and Center for Biophysics, Saarland University, Saarbrücken, Germany.
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7
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Gong X, Chiricotto M, Liu X, Nordquist E, Feig M, Brooks CL, Chen J. Accelerating the Generalized Born with Molecular Volume and Solvent Accessible Surface Area Implicit Solvent Model Using Graphics Processing Units. J Comput Chem 2019; 41:830-838. [PMID: 31875339 DOI: 10.1002/jcc.26133] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 11/21/2019] [Accepted: 12/07/2019] [Indexed: 12/15/2022]
Abstract
The generalized Born with molecular volume and solvent accessible surface area (GBMV2/SA) implicit solvent model provides an accurate description of molecular volume and has the potential to accurately describe the conformational equilibria of structured and disordered proteins. However, its broader application has been limited by the computational cost and poor scaling in parallel computing. Here, we report an efficient implementation of both the electrostatic and nonpolar components of GBMV2/SA on graphics processing unit (GPU) within the CHARMM/OpenMM module. The GPU-GBMV2/SA is numerically equivalent to the original CPU-GBMV2/SA. The GPU acceleration offers ~60- to 70-fold speedup on a single NVIDIA TITAN X (Pascal) graphics card for molecular dynamic simulations of both folded and unstructured proteins of various sizes. The current implementation can be further optimized to achieve even greater acceleration with minimal reduction on the numerical accuracy. The successful development of GPU-GBMV2/SA greatly facilitates its application to biomolecular simulations and paves the way for further development of the implicit solvent methodology. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- Xiping Gong
- Department of Chemistry, University of Massachusetts, Amherst, Amherst, Massachusetts, 01003
| | - Mara Chiricotto
- Department of Chemistry, University of Massachusetts, Amherst, Amherst, Massachusetts, 01003
| | - Xiaorong Liu
- Department of Chemistry, University of Massachusetts, Amherst, Amherst, Massachusetts, 01003
| | - Erik Nordquist
- Department of Chemistry, University of Massachusetts, Amherst, Amherst, Massachusetts, 01003
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, 48824
| | - Charles L Brooks
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan, 48109
| | - Jianhan Chen
- Department of Chemistry, University of Massachusetts, Amherst, Amherst, Massachusetts, 01003
- Department of Biochemistry and Molecular Biology, University of Massachusetts, Amherst, Amherst, Massachusetts, 01003
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8
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Park SJ, Lee J, Qi Y, Kern NR, Lee HS, Jo S, Joung I, Joo K, Lee J, Im W. CHARMM-GUI Glycan Modeler for modeling and simulation of carbohydrates and glycoconjugates. Glycobiology 2019; 29:320-331. [PMID: 30689864 DOI: 10.1093/glycob/cwz003] [Citation(s) in RCA: 191] [Impact Index Per Article: 38.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 01/20/2019] [Accepted: 01/22/2019] [Indexed: 12/15/2022] Open
Abstract
Characterizing glycans and glycoconjugates in the context of three-dimensional structures is important in understanding their biological roles and developing efficient therapeutic agents. Computational modeling and molecular simulation have become an essential tool complementary to experimental methods. Here, we present a computational tool, Glycan Modeler for in silico N-/O-glycosylation of the target protein and generation of carbohydrate-only systems. In our previous study, we developed Glycan Reader, a web-based tool for detecting carbohydrate molecules from a PDB structure and generation of simulation system and input files. As integrated into Glycan Reader in CHARMM-GUI, Glycan Modeler (Glycan Reader & Modeler) enables to generate the structures of glycans and glycoconjugates for given glycan sequences and glycosylation sites using PDB glycan template structures from Glycan Fragment Database (http://glycanstructure.org/fragment-db). Our benchmark tests demonstrate the universal applicability of Glycan Reader & Modeler to various glycan sequences and target proteins. We also investigated the structural properties of modeled glycan structures by running 2-μs molecular dynamics simulations of HIV envelope protein. The simulations show that the modeled glycan structures built by Glycan Reader & Modeler have the similar structural features compared to the ones solved by X-ray crystallography. We also describe the representative examples of glycoconjugate modeling with video demos to illustrate the practical applications of Glycan Reader & Modeler. Glycan Reader & Modeler is freely available at http://charmm-gui.org/input/glycan.
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Affiliation(s)
- Sang-Jun Park
- Departments of Biological Sciences and Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Jumin Lee
- Departments of Biological Sciences and Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Yifei Qi
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, China
| | - Nathan R Kern
- Departments of Biological Sciences and Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Hui Sun Lee
- Departments of Biological Sciences and Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Sunhwan Jo
- Leadership Computing Facility, Argonne National Laboratory, Argonne, IL, USA
| | - InSuk Joung
- Center for Advanced Computation, Korea Institute for Advanced Study, Republic of Korea
| | - Keehyung Joo
- Center for Advanced Computation, Korea Institute for Advanced Study, Republic of Korea
| | - Jooyoung Lee
- Center for Advanced Computation, Korea Institute for Advanced Study, Republic of Korea
| | - Wonpil Im
- Departments of Biological Sciences and Bioengineering, Lehigh University, Bethlehem, PA, USA
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9
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Molecular Docking and Molecular Dynamics (MD) Simulation of Human Anti-Complement Factor H (CFH) Antibody Ab42 and CFH Polypeptide. Int J Mol Sci 2019; 20:ijms20102568. [PMID: 31130605 PMCID: PMC6566401 DOI: 10.3390/ijms20102568] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Revised: 05/17/2019] [Accepted: 05/22/2019] [Indexed: 02/07/2023] Open
Abstract
An understanding of the interaction between the antibody and its targeted antigen and knowing of the epitopes are critical for the development of monoclonal antibody drugs. Complement factor H (CFH) is implied to play a role in tumor growth and metastasis. An autoantibody to CHF is associated with anti-tumor cell activity. The interaction of a human monoclonal antibody Ab42 that was isolated from a cancer patient with CFH polypeptide (pCFH) antigen was analyzed by molecular docking, molecular dynamics (MD) simulation, free energy calculation, and computational alanine scanning (CAS). Experimental alanine scanning (EAS) was then carried out to verify the results of the theoretical calculation. Our results demonstrated that the Ab42 antibody interacts with pCFH by hydrogen bonds through the Tyr315, Ser100, Gly33, and Tyr53 residues on the complementarity-determining regions (CDRs), respectively, with the amino acid residues of Pro441, Ile442, Asp443, Asn444, Ile447, and Thr448 on the pCFH antigen. In conclusion, this study has explored the mechanism of interaction between Ab42 antibody and its targeted antigen by both theoretical and experimental analysis. Our results have important theoretical significance for the design and development of relevant antibody drugs.
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10
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Abstract
It would often be useful in computer simulations to use an implicit description of solvation effects, instead of explicitly representing the individual solvent molecules. Continuum dielectric models often work well in describing the thermodynamic aspects of aqueous solvation and can be very efficient compared to the explicit treatment of the solvent. Here, we review a particular class of so-called fast implicit solvent models, generalized Born (GB) models, which are widely used for molecular dynamics (MD) simulations of proteins and nucleic acids. These approaches model hydration effects and provide solvent-dependent forces with efficiencies comparable to molecular-mechanics calculations on the solute alone; as such, they can be incorporated into MD or other conformational searching strategies in a straightforward manner. The foundations of the GB model are reviewed, followed by examples of newer, emerging models and examples of important applications. We discuss their strengths and weaknesses, both for fidelity to the underlying continuum model and for the ability to replace explicit consideration of solvent molecules in macromolecular simulations.
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Affiliation(s)
- Alexey V Onufriev
- Departments of Computer Science and Physics, Center for Soft Matter and Biological Physics, Virginia Tech, Blacksburg, Virginia 24060, USA;
| | - David A Case
- Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA;
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11
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Lee J, Patel DS, Ståhle J, Park SJ, Kern NR, Kim S, Lee J, Cheng X, Valvano MA, Holst O, Knirel YA, Qi Y, Jo S, Klauda JB, Widmalm G, Im W. CHARMM-GUI Membrane Builder for Complex Biological Membrane Simulations with Glycolipids and Lipoglycans. J Chem Theory Comput 2018; 15:775-786. [PMID: 30525595 DOI: 10.1021/acs.jctc.8b01066] [Citation(s) in RCA: 346] [Impact Index Per Article: 57.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Glycolipids (such as glycoglycerolipids, glycosphingolipids, and glycosylphosphatidylinositol) and lipoglycans (such as lipopolysaccharides (LPS), lipooligosaccharides (LOS), mycobacterial lipoarabinomannan, and mycoplasma lipoglycans) are typically found on the surface of cell membranes and play crucial roles in various cellular functions. Characterizing their structure and dynamics at the molecular level is essential to understand their biological roles, but systematic generation of glycolipid and lipoglycan structures is challenging because of great variations in lipid structures and glycan sequences (i.e., carbohydrate types and their linkages). To facilitate the generation of all-atom glycolipid/LPS/LOS structures, we have developed Glycolipid Modeler and LPS Modeler in CHARMM-GUI ( http://www.charmm-gui.org ), a web-based interface that simplifies building of complex biological simulation systems. In addition, we have incorporated these modules into Membrane Builder so that users can readily build a complex symmetric or asymmetric biological membrane system with various glycolipids and LPS/LOS. These tools are expected to be useful in innovative and novel glycolipid/LPS/LOS modeling and simulation research by easing tedious and intricate steps in modeling complex biological systems and shall provide insight into structures, dynamics, and underlying mechanisms of complex glycolipid-/LPS-/LOS-containing biological membrane systems.
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Affiliation(s)
- Jumin Lee
- Departments of Biological Sciences and Bioengineering , Lehigh University , Bethlehem , Pennsylvania 18015 , United States
| | - Dhilon S Patel
- Departments of Biological Sciences and Bioengineering , Lehigh University , Bethlehem , Pennsylvania 18015 , United States
| | - Jonas Ståhle
- Department of Organic Chemistry, Arrhenius Laboratory , Stockholm University , SE-106 91 Stockholm , Sweden
| | - Sang-Jun Park
- Departments of Biological Sciences and Bioengineering , Lehigh University , Bethlehem , Pennsylvania 18015 , United States
| | - Nathan R Kern
- Departments of Biological Sciences and Bioengineering , Lehigh University , Bethlehem , Pennsylvania 18015 , United States
| | - Seonghoon Kim
- Departments of Biological Sciences and Bioengineering , Lehigh University , Bethlehem , Pennsylvania 18015 , United States
| | - Joonseong Lee
- Departments of Biological Sciences and Bioengineering , Lehigh University , Bethlehem , Pennsylvania 18015 , United States
| | - Xi Cheng
- State Key Laboratory of Drug Research , Shanghai Institute of Materia Medica, Chinese Academy of Sciences , 555 Zuchongzhi Road , Shanghai 201203 , China
| | - Miguel A Valvano
- Wellcome-Wolfson Institute for Experimental Medicine , Queen's University Belfast BT9 7BL , United Kingdom
| | - Otto Holst
- Division of Structural Biochemistry, Research Center Borstel , Airway Research Center North, Member of the German Center for Lung Research (DZL) , D-23845 Borstel , Germany
| | - Yuriy A Knirel
- N. D. Zelinsky Institute of Organic Chemistry , Russian Academy of Sciences , 119991 Moscow , Russia
| | - Yifei Qi
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering , East China Normal University , Shanghai 200062 , China
| | - Sunhwan Jo
- Leadership Computing Facility , Argonne National Laboratory , Argonne , Illinois 60439 , United States
| | - Jeffery B Klauda
- Department of Chemical and Biomolecular Engineering and the Biophysics Graduate Program , University of Maryland , College Park , Maryland 20742 , United States
| | - Göran Widmalm
- Department of Organic Chemistry, Arrhenius Laboratory , Stockholm University , SE-106 91 Stockholm , Sweden
| | - Wonpil Im
- Departments of Biological Sciences and Bioengineering , Lehigh University , Bethlehem , Pennsylvania 18015 , United States
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12
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Steinbach PJ. Peptide and Protein Structure Prediction with a Simplified Continuum Solvent Model. J Phys Chem B 2018; 122:11355-11362. [PMID: 30230838 DOI: 10.1021/acs.jpcb.8b07264] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
A continuum solvent model based on screened Coulomb potentials has been simplified and parametrized to sample native-like structures in replica-exchange simulations of each of six different peptides and miniproteins. Low-energy, native, and non-native structures were used to iteratively refine 11 parameter values. The centroid of the largest cluster of structures sampled in simulations initiated from an extended conformation represents the predicted structure. The main-chain rms deviation of this prediction from the experimental structure was 0.47 Å for the 12-residue Trp-zip2, 0.86 Å for the 14-residue MBH12, 2.53 Å for the 17-residue U(1-17)T9D, 2.03 Å for the 20-residue BS1, 1.08 Å for the 20-residue Trp-cage, and 3.64 Å for the 35-residue villin headpiece subdomain HP35. The centroid of the sixth largest cluster sampled for HP35 deviated by 0.91 Å. The CHARMM22/CMAP force field was used, with an additional ψ torsion term for residues other than glycine and proline. Six parameters govern the dielectric response of the continuum solvent, and four values of surface tension approximate nonpolar effects. An atom's self-energy and interaction energies are screened independently, each depending on whether the atom is part of a charged group, a neutral hydrogen-bonding main-chain group, or any other neutral group. The parameters inferred result in strong main-chain hydrogen bonds, consistent with the view that protein folding is dominated by the formation of these bonds. (1,2) Conformations of MBH12 and BS1 were excluded from the energy-function refinement, suggesting the parameters, referred to as SCP18, are transferable. An efficient estimate of solvent-accessible surface area is also described.
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Affiliation(s)
- Peter J Steinbach
- Center for Molecular Modeling, Center for Information Technology , National Institutes of Health , Bethesda , Maryland 20892 , United States
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13
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Sahu TK, Pradhan D, Rao AR, Jena L. In silico site-directed mutagenesis of neutralizing mAb 4C4 and analysis of its interaction with G-H loop of VP1 to explore its therapeutic applications against FMD. J Biomol Struct Dyn 2018; 37:2641-2651. [PMID: 30051760 DOI: 10.1080/07391102.2018.1494631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Investigating the behaviour of bio-molecules through computational mutagenesis is gaining interest to facilitate the development of new therapeutic solutions for infectious diseases. The antigenetically variant genotypes of foot and mouth disease virus (FMDV) and their subsequent infections are challenging to tackle with traditional vaccination. In such scenario, neutralizing antibodies might provide an alternate solution to manage the FMDV infection. Thus, we have analysed the interaction of the mAb 4C4 with a synthetic G-H loop of FMDV-VP1 through in silico mutagenesis and molecular modelling. Initially, a set of 25,434 mutants were designed and the mutants having better energetic stability than 4C4 were clustered based on sequence identity. The best mutant representing each cluster was selected and evaluated for its binding affinity with the antigen in terms of docking scores, interaction energy and binding energy. Six mutants have confirmed better binding affinities towards the antigen than 4C4. Further, interaction of these mutants with the natural G-H loop that is bound to mAb SD6 was also evaluated. One 4C4 variant having mutations at the positions 2034(N→L), 2096(N→C), 2098(D→Y), 2532(T→K) and 2599(A→G) has revealed better binding affinities towards both the synthetic and natural G-H loops than 4C4 and SD6, respectively. A molecular dynamic simulation for 50 ns was conducted for mutant and wild-type antibody structures which supported the pre-simulation results. Therefore, these mutations on mAb 4C4 are believed to provide a better antibody-based therapeutic option for FMD. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Tanmaya Kumar Sahu
- a Centre for Agricultural Bioinformatics , ICAR-Indian Agricultural Statistics Research Institute , New Delhi , Delhi , India
| | - Dibyabhaba Pradhan
- b Biomedical Informatics Centre , ICMR-National Institute of Pathology , New Delhi , Delhi , India.,c ICMR-Computational Genomics Centre , Indian Council of Medical Research , New Delhi , Delhi , India
| | - Atmakuri Ramakrishna Rao
- a Centre for Agricultural Bioinformatics , ICAR-Indian Agricultural Statistics Research Institute , New Delhi , Delhi , India
| | - Lingaraj Jena
- d Bioinformatics Centre , Mahatma Gandhi Institute of Medical Sciences , Sevagram , Maharashtra , India
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14
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Park SJ, Lee J, Patel DS, Ma H, Lee HS, Jo S, Im W. Glycan Reader is improved to recognize most sugar types and chemical modifications in the Protein Data Bank. Bioinformatics 2018; 33:3051-3057. [PMID: 28582506 DOI: 10.1093/bioinformatics/btx358] [Citation(s) in RCA: 82] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Accepted: 05/31/2017] [Indexed: 11/12/2022] Open
Abstract
Motivation Glycans play a central role in many essential biological processes. Glycan Reader was originally developed to simplify the reading of Protein Data Bank (PDB) files containing glycans through the automatic detection and annotation of sugars and glycosidic linkages between sugar units and to proteins, all based on atomic coordinates and connectivity information. Carbohydrates can have various chemical modifications at different positions, making their chemical space much diverse. Unfortunately, current PDB files do not provide exact annotations for most carbohydrate derivatives and more than 50% of PDB glycan chains have at least one carbohydrate derivative that could not be correctly recognized by the original Glycan Reader. Results Glycan Reader has been improved and now identifies most sugar types and chemical modifications (including various glycolipids) in the PDB, and both PDB and PDBx/mmCIF formats are supported. CHARMM-GUI Glycan Reader is updated to generate the simulation system and input of various glycoconjugates with most sugar types and chemical modifications. It also offers a new functionality to edit the glycan structures through addition/deletion/modification of glycosylation types, sugar types, chemical modifications, glycosidic linkages, and anomeric states. The simulation system and input files can be used for CHARMM, NAMD, GROMACS, AMBER, GENESIS, LAMMPS, Desmond, OpenMM, and CHARMM/OpenMM. Glycan Fragment Database in GlycanStructure.Org is also updated to provide an intuitive glycan sequence search tool for complex glycan structures with various chemical modifications in the PDB. Availability and implementation http://www.charmm-gui.org/input/glycan and http://www.glycanstructure.org. Contact wonpil@lehigh.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sang-Jun Park
- Department of Biological Sciences and Bioengineering Program, Lehigh University, Bethlehem, PA, USA
| | - Jumin Lee
- Department of Biological Sciences and Bioengineering Program, Lehigh University, Bethlehem, PA, USA
| | - Dhilon S Patel
- Department of Biological Sciences and Bioengineering Program, Lehigh University, Bethlehem, PA, USA
| | - Hongjing Ma
- Department of Biological Sciences and Bioengineering Program, Lehigh University, Bethlehem, PA, USA
| | - Hui Sun Lee
- Department of Biological Sciences and Bioengineering Program, Lehigh University, Bethlehem, PA, USA
| | - Sunhwan Jo
- Leadership Computing Facility, Argonne National Laboratory, Argonne, IL, USA
| | - Wonpil Im
- Department of Biological Sciences and Bioengineering Program, Lehigh University, Bethlehem, PA, USA
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15
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Kar P, Feig M. Hybrid All-Atom/Coarse-Grained Simulations of Proteins by Direct Coupling of CHARMM and PRIMO Force Fields. J Chem Theory Comput 2017; 13:5753-5765. [PMID: 28992696 DOI: 10.1021/acs.jctc.7b00840] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Hybrid all-atom/coarse-grained (AA/CG) simulations of proteins offer a computationally efficient compromise where atomistic details are only applied to biologically relevant regions while benefiting from the speedup of treating the remaining parts of a given system at the CG level. The recently developed CG model, PRIMO, allows a direct coupling with an atomistic force field with no additional modifications or coupling terms and the ability to carry out dynamic simulations without any restraints on secondary or tertiary structures. A hybrid AA/CG scheme based on combining all-atom CHARMM and coarse-grained PRIMO representations was validated via molecular dynamics and replica exchange simulations of soluble and membrane proteins. The AA/CG scheme was also tested in the calculation of the free energy profile for the transition from the closed to the open state of adenylate kinase via umbrella sampling molecular dynamics method. The overall finding is that the AA/CG scheme generates dynamics and energetics that are qualitatively and quantitatively comparable to AA simulations while offering the computational advantages of coarse-graining. This model opens the door to challenging applications where high accuracy is required only in parts of large biomolecular complexes.
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Affiliation(s)
- Parimal Kar
- Department of Biochemistry and Molecular Biology, Michigan State University , East Lansing, Michigan 48824, United States
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University , East Lansing, Michigan 48824, United States
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16
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Kamgar-Parsi K, Hong L, Naito A, Brooks CL, Ramamoorthy A. Growth-incompetent monomers of human calcitonin lead to a noncanonical direct relationship between peptide concentration and aggregation lag time. J Biol Chem 2017; 292:14963-14976. [PMID: 28739873 PMCID: PMC5592673 DOI: 10.1074/jbc.m117.791236] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2017] [Revised: 07/19/2017] [Indexed: 11/06/2022] Open
Abstract
The role of the peptide hormone calcitonin in skeletal protection has led to its use as a therapeutic for osteoporosis. However, calcitonin aggregation into amyloid fibrils limits its therapeutic efficacy, necessitating a modification of calcitonin's aggregation kinetics. Here, we report a direct relationship between human calcitonin (hCT) concentration and aggregation lag time. This kinetic trend was contrary to the conventional understanding of amyloid aggregation and persisted over a range of aggregation conditions, as confirmed by thioflavin-T kinetics assays, CD spectroscopy, and transmission EM. Dynamic light scattering, 1H NMR experiments, and seeded thioflavin-T assay results indicated that differences in initial peptide species contribute to this trend more than variations in the primary nucleus formation rate. On the basis of kinetics modeling results, we propose a mechanism whereby a structural conversion of hCT monomers is needed before incorporation into the fibril. Our kinetic mechanism recapitulates the experimentally observed relationship between peptide concentration and lag time and represents a novel mechanism in amyloid aggregation. Interestingly, hCT at low pH and salmon calcitonin (sCT) exhibited the canonical inverse relationship between concentration and lag time. Comparative studies of hCT and sCT with molecular dynamics simulations and CD indicated an increased α-helical structure in sCT and low-pH hCT monomers compared with neutral-pH hCT, suggesting that α-helical monomers represent a growth-competent species, whereas unstructured random coil monomers represent a growth-incompetent species. Our finding that initial monomer concentration is positively correlated with lag time in hCT aggregation could help inform future efforts for improving therapeutic applications of CT.
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Affiliation(s)
- Kian Kamgar-Parsi
- From the Applied Physics Program, University of Michigan, Ann Arbor, Michigan 48109
| | - Liu Hong
- Zhou Pei-Yuan Center for Applied Mathematics, Tsinghua University, Beijing 100084, China
| | - Akira Naito
- Graduate School of Engineering, Yokohama National University, 79-5 Tokiwadai, Hodogaya-ku, Yokohama 240-8501, Japan, and
| | - Charles L Brooks
- Department of Chemistry and Biophysics Program, University of Michigan, Ann Arbor, Michigan 48109-1055
| | - Ayyalusamy Ramamoorthy
- Department of Chemistry and Biophysics Program, University of Michigan, Ann Arbor, Michigan 48109-1055
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17
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Jo S, Cheng X, Lee J, Kim S, Park SJ, Patel DS, Beaven AH, Lee KI, Rui H, Park S, Lee HS, Roux B, MacKerell AD, Klauda JB, Qi Y, Im W. CHARMM-GUI 10 years for biomolecular modeling and simulation. J Comput Chem 2017; 38:1114-1124. [PMID: 27862047 PMCID: PMC5403596 DOI: 10.1002/jcc.24660] [Citation(s) in RCA: 179] [Impact Index Per Article: 25.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2016] [Revised: 10/04/2016] [Accepted: 10/18/2016] [Indexed: 12/16/2022]
Abstract
CHARMM-GUI, http://www.charmm-gui.org, is a web-based graphical user interface that prepares complex biomolecular systems for molecular simulations. CHARMM-GUI creates input files for a number of programs including CHARMM, NAMD, GROMACS, AMBER, GENESIS, LAMMPS, Desmond, OpenMM, and CHARMM/OpenMM. Since its original development in 2006, CHARMM-GUI has been widely adopted for various purposes and now contains a number of different modules designed to set up a broad range of simulations: (1) PDB Reader & Manipulator, Glycan Reader, and Ligand Reader & Modeler for reading and modifying molecules; (2) Quick MD Simulator, Membrane Builder, Nanodisc Builder, HMMM Builder, Monolayer Builder, Micelle Builder, and Hex Phase Builder for building all-atom simulation systems in various environments; (3) PACE CG Builder and Martini Maker for building coarse-grained simulation systems; (4) DEER Facilitator and MDFF/xMDFF Utilizer for experimentally guided simulations; (5) Implicit Solvent Modeler, PBEQ-Solver, and GCMC/BD Ion Simulator for implicit solvent related calculations; (6) Ligand Binder for ligand solvation and binding free energy simulations; and (7) Drude Prepper for preparation of simulations with the CHARMM Drude polarizable force field. Recently, new modules have been integrated into CHARMM-GUI, such as Glycolipid Modeler for generation of various glycolipid structures, and LPS Modeler for generation of lipopolysaccharide structures from various Gram-negative bacteria. These new features together with existing modules are expected to facilitate advanced molecular modeling and simulation thereby leading to an improved understanding of the structure and dynamics of complex biomolecular systems. Here, we briefly review these capabilities and discuss potential future directions in the CHARMM-GUI development project. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Sunhwan Jo
- Leadership Computing Facility, Argonne National Laboratory, 9700 Cass Ave, Argonne, Illinois
| | - Xi Cheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, 555 Zuchongzhi Road, Pudong, Shanghai, 201203, China
| | - Jumin Lee
- Department of Biological Sciences and Bioengineering Program, Lehigh University, Pennsylvania
| | - Seonghoon Kim
- Department of Biological Sciences and Bioengineering Program, Lehigh University, Pennsylvania
| | - Sang-Jun Park
- Department of Biological Sciences and Bioengineering Program, Lehigh University, Pennsylvania
| | - Dhilon S Patel
- Department of Biological Sciences and Bioengineering Program, Lehigh University, Pennsylvania
| | - Andrew H Beaven
- Department of Chemistry, The University of Kansas, Lawrence, Kansas
| | - Kyu Il Lee
- Department of Biological Sciences and Bioengineering Program, Lehigh University, Pennsylvania
| | - Huan Rui
- Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, Illinois
| | - Soohyung Park
- Department of Biological Sciences and Bioengineering Program, Lehigh University, Pennsylvania
| | - Hui Sun Lee
- Department of Biological Sciences and Bioengineering Program, Lehigh University, Pennsylvania
| | - Benoît Roux
- Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, Illinois
| | - Alexander D MacKerell
- Department of Pharmaceutical Sciences School of Pharmacy, University of Maryland, Baltimore, Maryland
| | - Jeffrey B Klauda
- Department of Chemical and Biomolecular Engineering and the Biophysics Program, University of Maryland College Park, Maryland
| | - Yifei Qi
- Department of Biological Sciences and Bioengineering Program, Lehigh University, Pennsylvania
| | - Wonpil Im
- Department of Biological Sciences and Bioengineering Program, Lehigh University, Pennsylvania
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18
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Zhang B, Kilburg D, Eastman P, Pande VS, Gallicchio E. Efficient gaussian density formulation of volume and surface areas of macromolecules on graphical processing units. J Comput Chem 2017; 38:740-752. [PMID: 28160511 DOI: 10.1002/jcc.24745] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2016] [Revised: 01/05/2017] [Accepted: 01/08/2017] [Indexed: 11/07/2022]
Abstract
We present an algorithm to efficiently compute accurate volumes and surface areas of macromolecules on graphical processing unit (GPU) devices using an analytic model which represents atomic volumes by continuous Gaussian densities. The volume of the molecule is expressed by means of the inclusion-exclusion formula, which is based on the summation of overlap integrals among multiple atomic densities. The surface area of the molecule is obtained by differentiation of the molecular volume with respect to atomic radii. The many-body nature of the model makes a port to GPU devices challenging. To our knowledge, this is the first reported full implementation of this model on GPU hardware. To accomplish this, we have used recursive strategies to construct the tree of overlaps and to accumulate volumes and their gradients on the tree data structures so as to minimize memory contention. The algorithm is used in the formulation of a surface area-based non-polar implicit solvent model implemented as an open source plug-in (named GaussVol) for the popular OpenMM library for molecular mechanics modeling. GaussVol is 50 to 100 times faster than our best optimized implementation for the CPUs, achieving speeds in excess of 100 ns/day with 1 fs time-step for protein-sized systems on commodity GPUs. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Baofeng Zhang
- Department of Chemistry, Brooklyn College of the City University of New York, 2900 Bedford Avenue, Brooklyn, New York, 11210
| | - Denise Kilburg
- Department of Chemistry, Brooklyn College of the City University of New York, 2900 Bedford Avenue, Brooklyn, New York, 11210.,Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, New York, 10016
| | - Peter Eastman
- Department of Bioengineering, Stanford University, Stanford, California, 94035
| | - Vijay S Pande
- Department of Chemistry, Stanford University, Stanford, California, 94035
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York, 2900 Bedford Avenue, Brooklyn, New York, 11210.,Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, New York, 10016
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19
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Olson MA, Lee MS, Yeh IC. Membrane insertion of fusion peptides from Ebola and Marburg viruses studied by replica-exchange molecular dynamics simulations. J Comput Chem 2017; 38:1342-1352. [PMID: 28130780 DOI: 10.1002/jcc.24717] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Revised: 12/05/2016] [Accepted: 12/06/2016] [Indexed: 12/24/2022]
Abstract
This work presents replica-exchange molecular dynamics simulations of inserting a 16-residue Ebola virus fusion peptide into a membrane bilayer. A computational approach is applied for modeling the peptide at the explicit all-atom level and the membrane-aqueous bilayer by a generalized Born continuum model with a smoothed switching function (GBSW). We provide an assessment of the model calculations in terms of three metrics: (1) the ability to reproduce the NMR structure of the peptide determined in the presence of SDS micelles and comparable structural data on other fusion peptides; (2) determination of the effects of the mutation Trp-8 to Ala and sequence discrimination of the homologous Marburg virus; and (3) calculation of potentials of mean force for estimating the partitioning free energy and their comparison to predictions from the Wimley-White interfacial hydrophobicity scale. We found the GBSW implicit membrane model to produce results of limited accuracy in conformational properties of the peptide when compared to the NMR structure, yet the model resolution is sufficient to determine the effect of sequence differentiation on peptide-membrane integration. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Mark A Olson
- Department of Cell Biology and Biochemistry, Molecular and Translational Sciences, USAMRIID, Fredrick, Maryland
| | - Michael S Lee
- Computational Sciences Division, U.S. Army Research Laboratory, Aberdeen Proving Ground, Maryland
| | - In-Chul Yeh
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, U.S. Army Medical Research and Materiel Command, Fredrick, Maryland
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20
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Sanyal T, Shell MS. Coarse-grained models using local-density potentials optimized with the relative entropy: Application to implicit solvation. J Chem Phys 2017; 145:034109. [PMID: 27448876 DOI: 10.1063/1.4958629] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Bottom-up multiscale techniques are frequently used to develop coarse-grained (CG) models for simulations at extended length and time scales but are often limited by a compromise between computational efficiency and accuracy. The conventional approach to CG nonbonded interactions uses pair potentials which, while computationally efficient, can neglect the inherently multibody contributions of the local environment of a site to its energy, due to degrees of freedom that were coarse-grained out. This effect often causes the CG potential to depend strongly on the overall system density, composition, or other properties, which limits its transferability to states other than the one at which it was parameterized. Here, we propose to incorporate multibody effects into CG potentials through additional nonbonded terms, beyond pair interactions, that depend in a mean-field manner on local densities of different atomic species. This approach is analogous to embedded atom and bond-order models that seek to capture multibody electronic effects in metallic systems. We show that the relative entropy coarse-graining framework offers a systematic route to parameterizing such local density potentials. We then characterize this approach in the development of implicit solvation strategies for interactions between model hydrophobes in an aqueous environment.
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Affiliation(s)
- Tanmoy Sanyal
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California 93106, USA
| | - M Scott Shell
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California 93106, USA
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21
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Katkova EV, Onufriev AV, Aguilar B, Sulimov VB. Accuracy comparison of several common implicit solvent models and their implementations in the context of protein-ligand binding. J Mol Graph Model 2016; 72:70-80. [PMID: 28064081 DOI: 10.1016/j.jmgm.2016.12.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 12/07/2016] [Accepted: 12/15/2016] [Indexed: 11/18/2022]
Abstract
In this study several commonly used implicit solvent models are compared with respect to their accuracy of estimating solvation energies of small molecules and proteins, as well as desolvation penalty in protein-ligand binding. The test set consists of 19 small proteins, 104 small molecules, and 15 protein-ligand complexes. We compared predicted hydration energies of small molecules with their experimental values; the results of the solvation and desolvation energy calculations for small molecules, proteins and protein-ligand complexes in water were also compared with Thermodynamic Integration calculations based on TIP3P water model and Amber12 force field. The following implicit solvent (water) models considered here are: PCM (Polarized Continuum Model implemented in DISOLV and MCBHSOLV programs), GB (Generalized Born method implemented in DISOLV program, S-GB, and GBNSR6 stand-alone version), COSMO (COnductor-like Screening Model implemented in the DISOLV program and the MOPAC package) and the Poisson-Boltzmann model (implemented in the APBS program). Different parameterizations of the molecules were examined: we compared MMFF94 force field, Amber12 force field and the quantum-chemical semi-empirical PM7 method implemented in the MOPAC package. For small molecules, all of the implicit solvent models tested here yield high correlation coefficients (0.87-0.93) between the calculated solvation energies and the experimental values of hydration energies. For small molecules high correlation (0.82-0.97) with the explicit solvent energies is seen as well. On the other hand, estimated protein solvation energies and protein-ligand binding desolvation energies show substantial discrepancy (up to 10kcal/mol) with the explicit solvent reference. The correlation of polar protein solvation energies and protein-ligand desolvation energies with the corresponding explicit solvent results is 0.65-0.99 and 0.76-0.96 respectively, though this difference in correlations is caused more by different parameterization and less by methods and indicates the need for further improvement of implicit solvent models parameterization. Within the same parameterization, various implicit methods give practically the same correlation with results obtained in explicit solvent model for ligands and proteins: e.g. correlation values of polar ligand solvation energies and the corresponding energies in the frame of explicit solvent were 0.953-0.966 for the APBS program, the GBNSR6 program and all models used in the DISOLV program. The DISOLV program proved to be on a par with the other used programs in the case of proteins and ligands solvation energy calculation. However, the solution of the Poisson-Boltzmann equation (APBS program) and Generalized Born method (implemented in the GBNSR6 program) proved to be the most accurate in calculating the desolvation energies of complexes.
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Affiliation(s)
- E V Katkova
- Dimonta, Ltd., Nagornaya Street 15, Bldg 8, Moscow, 117186, Russia; Research Computer Center, Lomonosov Moscow State University, Leninskie Gory 1,Bldg 4, Moscow, 119992, Russia.
| | - A V Onufriev
- Departments of Computer Science and Physics, Center for Soft Matter and Biological Physics, Virginia Tech, Blacksburg, VA, USA
| | - B Aguilar
- Institute for Systems Biology, Seattle, WA, USA
| | - V B Sulimov
- Dimonta, Ltd., Nagornaya Street 15, Bldg 8, Moscow, 117186, Russia; Research Computer Center, Lomonosov Moscow State University, Leninskie Gory 1,Bldg 4, Moscow, 119992, Russia
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22
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Robinson MK, Monroe JI, Shell MS. Are AMBER Force Fields and Implicit Solvation Models Additive? A Folding Study with a Balanced Peptide Test Set. J Chem Theory Comput 2016; 12:5631-5642. [DOI: 10.1021/acs.jctc.6b00788] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Melina K. Robinson
- Department
of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California 93106, United States
| | - Jacob I. Monroe
- Department
of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California 93106, United States
| | - M. Scott Shell
- Department
of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California 93106, United States
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23
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Arthur EJ, Brooks CL. Efficient implementation of constant pH molecular dynamics on modern graphics processors. J Comput Chem 2016; 37:2171-80. [PMID: 27405884 DOI: 10.1002/jcc.24435] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Revised: 06/02/2016] [Accepted: 06/03/2016] [Indexed: 12/15/2022]
Abstract
The treatment of pH sensitive ionization states for titratable residues in proteins is often omitted from molecular dynamics (MD) simulations. While static charge models can answer many questions regarding protein conformational equilibrium and protein-ligand interactions, pH-sensitive phenomena such as acid-activated chaperones and amyloidogenic protein aggregation are inaccessible to such models. Constant pH molecular dynamics (CPHMD) coupled with the Generalized Born with a Simple sWitching function (GBSW) implicit solvent model provide an accurate framework for simulating pH sensitive processes in biological systems. Although this combination has demonstrated success in predicting pKa values of protein structures, and in exploring dynamics of ionizable side-chains, its speed has been an impediment to routine application. The recent availability of low-cost graphics processing unit (GPU) chipsets with thousands of processing cores, together with the implementation of the accurate GBSW implicit solvent model on those chipsets (Arthur and Brooks, J. Comput. Chem. 2016, 37, 927), provide an opportunity to improve the speed of CPHMD and ionization modeling greatly. Here, we present a first implementation of GPU-enabled CPHMD within the CHARMM-OpenMM simulation package interface. Depending on the system size and nonbonded force cutoff parameters, we find speed increases of between one and three orders of magnitude. Additionally, the algorithm scales better with system size than the CPU-based algorithm, thus allowing for larger systems to be modeled in a cost effective manner. We anticipate that the improved performance of this methodology will open the door for broad-spread application of CPHMD in its modeling pH-mediated biological processes. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Evan J Arthur
- Department of Chemistry, University of Michigan, 930 N. University Ave, Ann Arbor, Michigan, 48109
| | - Charles L Brooks
- Department of Chemistry and Biophysics Program, University of Michigan, 930 N. University Ave, Ann Arbor, Michigan, 48109
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