85 results on '"Boresch S."'
Search Results
2. Studying the dielectric properties of a protein solution by computer simulation
- Author
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Boresch, S., Hochtl, P., and Steinhauser, O.
- Subjects
Molecular dynamics -- Research ,Proteins -- Electric properties ,Chemicals, plastics and rubber industries - Abstract
The static and frequency-dependent dielectric properties of a 9 mmol/L ubiquitin solution based on the analysis of a 5 ns molecular dynamics (MD) simulation is reported. It is demonstrated that an MD-based approach can qualitatively reproduce measured dielectric properties of protein solutions and aid in the interpretation of the experimental data. The findings reveal that protein and water self-contributions could be obtained with reasonable accuracy, while some problems were detected for the protein-water cross-term.
- Published
- 2000
3. Simulation studies of the protein-water interface. I. Properties at the molecular resolution.
- Author
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Schröder, C., Rudas, T., Boresch, S., and Steinhauser, O.
- Subjects
INTERFACES (Physical sciences) ,SIMULATION methods & models ,MOLECULAR dynamics ,PROTEINS ,UBIQUITIN ,PHOSPHOLIPASES - Abstract
We report molecular dynamics simulations of three globular proteins: ubiquitin, apo-calbindin D
9K , and the C-terminal SH2 domain of phospholipase C-γ1 in explicit water. The proteins differ in their overall charge and fold type and were chosen to represent to some degree the structural variability found in medium-sized proteins. The length of each simulation was at least 15 ns, and larger than usual solvent boxes were used. We computed radial distribution functions, as well as orientational correlation functions about the surface residues. Two solvent shells could be clearly discerned about charged and polar amino acids. Near apolar amino acids the water density near such residues was almost devoid of structure. The mean residence time of water molecules was determined for water shells about the full protein, as well as for water layers about individual amino acids. In the dynamic properties, two solvent shells could be characterized as well. However, by comparison to simulations of pure water it could be shown that the influence of the protein reaches beyond 6 Å, i.e., beyond the first two shells. In the first shell (r≤=3.5 Å), the structural and dynamical properties of solvent waters varied considerably and depended primarily on the physicochemical properties of the closest amino acid side chain, with which the waters interact. By contrast, the solvent properties seem not to depend on the specifics of the protein studied (such as the net charge) or on the secondary structure element in which an amino acid is located. While differing considerably from the neat liquid, the properties of waters in the second solvation shell (3.5≤r≤=6 Å) are rather uniform; a direct influence from surface amino acids are already mostly shielded. [ABSTRACT FROM AUTHOR]- Published
- 2006
- Full Text
- View/download PDF
4. Simulation studies of the protein-water interface. II. Properties at the mesoscopic resolution.
- Author
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Rudas, T., Schröder, C., Boresch, S., and Steinhauser, O.
- Subjects
INTERFACES (Physical sciences) ,MESOSCOPIC phenomena (Physics) ,SIMULATION methods & models ,MOLECULAR dynamics ,PROTEINS ,DIELECTRICS - Abstract
We report molecular dynamics (MD) simulations of three protein-water systems (ubiquitin, apo-calbindin D
9K , and the C-terminal SH2 domain of phospholipase C-γ1), from which we compute the dielectric properties of the solutions. Since two of the proteins studied have a net charge, we develop the necessary theory to account for the presence of charged species in a form suitable for computer simulations. In order to ensure convergence of the time correlation functions needed for the analysis, the minimum length of the MD simulations was 20 ns. The system sizes (box length, number of waters) were chosen so that the resulting protein concentrations are comparable to experimental conditions. A dielectric component analysis was carried out to analyze the contributions from protein and water to the frequency-dependent dielectric susceptibility χ(ω) of the solutions. Additionally, an even finer decomposition into protein, two solvation shells, and the remaining water (bulk water) was carried out. The results of these dielectric decompositions were used to study protein solvation at mesoscopic resolution, i.e., in terms of protein, first and second solvation layers, and bulk water. This study, therefore, complements the structural and dynamical analyses at molecular resolution that are presented in the companion paper. The dielectric component contributions from the second shell and bulk water are very similar in all three systems. We find that the proteins influence the dielectric properties of water even beyond the second solvation shell, in agreement with what was observed for the mean residence times of water molecules in protein solutions. By contrast, the protein contributions, as well as the contributions of the first solvation shell, are system specific. Most importantly, the protein and the first water shell around ubiquitin and apo-calbindin are anticorrelated, whereas the first water shell around the SH2 domain is positively correlated. [ABSTRACT FROM AUTHOR]- Published
- 2006
- Full Text
- View/download PDF
5. The effect of density variation on the structure of liquid hydrogen chloride. A Monte Carlo study.
- Author
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Steinhauser, O., Boresch, S., and Bertagnolli, H.
- Subjects
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HYDROGEN chloride , *MONTE Carlo method , *SOLID state chemistry , *PARTIAL differential equations - Abstract
An intermolecular potential for liquid hydrogen chloride is derived from ab initio calculations and is further refined by using solid state properties. The potential function includes a two-center Lennard-Jones term, a six-center point charge model and many-body polarization forces. Monte Carlo calculations are performed for two densities (ρ=0.85 g/cm3, 0.50 g/cm3) at two temperatures (T=25 °C, 100 °C). The relative importance of the various contributions to the intermolecular potential are elucidated by comparison to neutron diffraction experiments. [ABSTRACT FROM AUTHOR]
- Published
- 1990
- Full Text
- View/download PDF
6. Comments on 'Anomalous dielectric relaxation of aqueous protein solutions' by Nilashis Nandi and Biman Bagchi
- Author
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Boresch, S. and Steinhauser, O.
- Subjects
Dielectric relaxation -- Analysis ,Chemicals, plastics and rubber industries - Abstract
The shortcomings present in the Nandi and Bagchi's (NB's) approach to explain the dielectric relaxation spectra of an aqueous protein solution via the first unified, microscopic theory is discussed. It is stated that the alleged rationalization of dielectric spectra of protein solutions provided by NB relies on a wrong starting equation combined with an incorrect definition of the Kirkwood g-factor.
- Published
- 2001
7. CHARMM: the biomolecular simulation program
- Author
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Brooks, B. R., Brooks III, C. L., Mackerell Jr., A. D., Nilsson, L., Petrella, R. J., Roux, B., Won, Y., Archontis, Georgios Z., Bartels, C., Boresch, S., Caflisch, A., Caves, L., Cui, Q., Dinner, A. R., Feig, M., Fischer, S., Gao, J., Hodoscek, M., Im, W., Kuczera, K., Lazaridis, T., Ma, J., Ovchinnikov, V., Paci, E., Pastor, R. W., Post, C. B., Pu, J. Z., Schaefer, M., Tidor, B., Venable, R. M., Woodcock, H. L., Wu, X., Yang, W., York, D. M., Karplus, M., Brooks B.R., Brooks III C.L., Mackerell Jr. A.D., Nilsson L., Petrella R.J., Roux B., Won Y., Archontis G., Bartels C., Boresch S., Caflisch A., Caves L., Cui Q., Dinner A.R., Feig M., Fischer S., Gao J., Hodoscek M., Im W., Kuczera K., Lazaridis T., Ma J., Ovchinnikov V., Paci E., Pastor R.W., Post C.B., Pu J.Z., Schaefer M., Tidor B., Venable R.M., Woodcock H.L., Wu X., Yang W., York D.M., Karplus M., Archontis, Georgios Z. [0000-0002-7750-8641], and University of Zurich
- Subjects
Models, Molecular ,Potential energy functions ,Molecular dynamic ,Molecular model ,chemical model ,Energy functions ,Molecular dynamics ,Molecular mechanicals ,Computational chemistry ,Nucleic Acids ,computer program ,CHARMM program ,Many-particle systems ,Amines ,Explicit solvents ,Chemistry ,biology ,Small molecules ,article ,Potential energy ,Lipids ,peptide ,Dynamics ,Nucleic acids ,Computational Mathematics ,Molecular mechanic ,symbols ,Membrane models ,Molecular simulations ,Harvard ,Biomolecular simulation ,2605 Computational Mathematics ,Biophysical computation ,Carbohydrates ,Molecular modeling ,1600 General Chemistry ,chemistry ,Article ,Computational science ,symbols.namesake ,Parallel architectures ,lipid ,Simulators ,10019 Department of Biochemistry ,computer simulation ,Computer Simulation ,Molecular mechanics ,Analysis techniques ,Computational tools ,Energy function ,Computational Biology ,Proteins ,Quantum mechanical ,General Chemistry ,Mechanical force ,quantum theory ,nucleic acid ,Implicit solvents ,Models, Chemical ,Path sampling method ,carbohydrate ,chemical structure ,Quantum Theory ,570 Life sciences ,Drude particle ,protein ,Peptides ,Software - Abstract
CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and widely used molecular simulation program. It has been developed over the last three decades with a primary focus on molecules of biological interest, including proteins, peptides, lipids, nucleic acids, carbohydrates and small molecule ligands, as they occur in solution, crystals, and membrane environments. For the study of such systems, the program provides a large suite of computational tools that include numerous conformational and path sampling methods, free energy estimators, molecular minimization, dynamics, and analysis techniques, and model-building capabilities. In addition, the CHARMM program is applicable to problems involving a much broader class of many-particle systems. Calculations with CHARMM can be performed using a number of different energy functions and models, from mixed quantum mechanical-molecular mechanical force fields, to all-atom classical potential energy functions with explicit solvent and various boundary conditions, to implicit solvent and membrane models. The program has been ported to numerous platforms in both serial and parallel architectures. This paper provides an overview of the program as it exists today with an emphasis on developments since the publication of the original CHARMM paper in 1983.
- Published
- 2009
- Full Text
- View/download PDF
8. CHARMM: The biomolecular simulation program
- Author
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Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109 ; Department of Biophysics, University of Michigan, Ann Arbor, Michigan 48109 ; Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892 ; Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, Maryland 21201 ; Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, Department of Biosciences and Nutrition, Karolinska Institutet, SE-141 57, Huddinge, Sweden ; Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138 ; Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115 ; Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, Department of Biochemistry and Molecular Biology, University of Chicago, Gordon Center for Integrative Science, Chicago, Illinois 60637 ; Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, Department of Chemistry, Hanyang University, Seoul 133-792, Korea ; Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138 ; Laboratoire de Chimie Biophysique, ISIS, Universit?? de Strasbourg, 67000 Strasbourg, France ; Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, Brooks, B. R., Brooks, C. L., Mackerell, A. D., Nilsson, L., Petrella, R. J., Roux, B., Won, Y., Archontis, G., Bartels, C., Boresch, S., Caflisch, A., Caves, L., Cui, Q., Dinner, A. R., Feig, M., Fischer, S., Gao, J., Hodoscek, M., Im, W., Kuczera, K., Lazaridis, T., Ma, J., Ovchinnikov, V., Paci, E., Pastor, R. W., Post, C. B., Pu, J. Z., Schaefer, M., Tidor, B., Venable, R. M., Woodcock, H. L., Wu, X., Yang, W., York, D. M., Karplus, M., Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109 ; Department of Biophysics, University of Michigan, Ann Arbor, Michigan 48109 ; Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892 ; Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, Maryland 21201 ; Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, Department of Biosciences and Nutrition, Karolinska Institutet, SE-141 57, Huddinge, Sweden ; Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138 ; Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115 ; Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, Department of Biochemistry and Molecular Biology, University of Chicago, Gordon Center for Integrative Science, Chicago, Illinois 60637 ; Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, Department of Chemistry, Hanyang University, Seoul 133-792, Korea ; Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138 ; Laboratoire de Chimie Biophysique, ISIS, Universit?? de Strasbourg, 67000 Strasbourg, France ; Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, Brooks, B. R., Brooks, C. L., Mackerell, A. D., Nilsson, L., Petrella, R. J., Roux, B., Won, Y., Archontis, G., Bartels, C., Boresch, S., Caflisch, A., Caves, L., Cui, Q., Dinner, A. R., Feig, M., Fischer, S., Gao, J., Hodoscek, M., Im, W., Kuczera, K., Lazaridis, T., Ma, J., Ovchinnikov, V., Paci, E., Pastor, R. W., Post, C. B., Pu, J. Z., Schaefer, M., Tidor, B., Venable, R. M., Woodcock, H. L., Wu, X., Yang, W., York, D. M., and Karplus, M.
- Abstract
CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and widely used molecular simulation program. It has been developed over the last three decades with a primary focus on molecules of biological interest, including proteins, peptides, lipids, nucleic acids, carbohydrates, and small molecule ligands, as they occur in solution, crystals, and membrane environments. For the study of such systems, the program provides a large suite of computational tools that include numerous conformational and path sampling methods, free energy estimators, molecular minimization, dynamics, and analysis techniques, and model-building capabilities. The CHARMM program is applicable to problems involving a much broader class of many-particle systems. Calculations with CHARMM can be performed using a number of different energy functions and models, from mixed quantum mechanical-molecular mechanical force fields, to all-atom classical potential energy functions with explicit solvent and various boundary conditions, to implicit solvent and membrane models. The program has been ported to numerous platforms in both serial and parallel architectures. This article provides an overview of the program as it exists today with an emphasis on developments since the publication of the original CHARMM article in 1983. ?? 2009 Wiley Periodicals, Inc.J Comput Chem, 2009.
- Published
- 2009
9. Free energy simulations: The meaning of the individual contributions from a component analysis
- Author
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Boresch, S., Archontis, Georgios Z., Karplus, M., and Archontis, Georgios Z. [0000-0002-7750-8641]
- Subjects
Bromides ,chloride ,hemodynamic integration ,Thermodynamic integration ,Ionic bonding ,Ligands ,Biochemistry ,Support, U.S. Gov't, Non-P.H.S ,thermodynamics ,Component analysis ,Chlorides ,Structural Biology ,Computational chemistry ,protein folding ,Computer Simulation ,Statistical physics ,Poisson Distribution ,Support, Non-U.S. Gov't ,Molecular Biology ,RISM theory ,bromide ,alchemical path ,decomposition ,Continuum (measurement) ,Chemistry ,Solvation ,article ,Proteins ,simulation ,Integral equation ,Solutions ,priority journal ,protein stability ,Models, Chemical ,Mutation ,Thermodynamics ,Polar ,Path dependence - Abstract
A theoretical analysis is made of the decomposition into contributions from individual interactions of the free energy calculated by thermodynamic integration. It is demonstrated that such a decomposition, often referred to as “component analysis,” is meaningful, even though it is a function of the integration path. Moreover, it is shown that the path dependence can be used to determine the relation of the contribution of a given interaction to the state of the system. To illustrate these conclusions, a simple transformation(Cl− to Br− in aqueous solution) is analyzed by use of the Reference Interaction Site Model‐Hypernetted Chain Closure integral equation approach it avoids the calculational difficulties of macromolecular simulation while retaining their conceptual complexity. The difference in the solvation free energy between chloride and bromide is calculated, and the contributions of the Lennard‐Jones and electrostatic terms in the potential function are analyzed by the use of suitably chosen integration paths. The model is also used to examine the path dependence of individual contributions to the double free energy differences (ΔΔG or ΔΔA) that are often employed in free energy simulations of biological systems. The alchemical path, as contrasted with the experimental path, is shown to be appropriate for interpreting the effects of mutations on ligand binding and protein stability. The formulation is used to obtain a better understanding of the success of the Poisson‐Boltzmann continuum approach for determining the solvation properties of polar and ionic systems. © 1994 Wiley‐Liss, Inc. Copyright © 1994 Wiley‐Liss, Inc. 20 1 25 33 Cited By :104
- Published
- 1994
10. CHARMM: The biomolecular simulation program
- Author
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Brooks, B. R., primary, Brooks, C. L., additional, Mackerell, A. D., additional, Nilsson, L., additional, Petrella, R. J., additional, Roux, B., additional, Won, Y., additional, Archontis, G., additional, Bartels, C., additional, Boresch, S., additional, Caflisch, A., additional, Caves, L., additional, Cui, Q., additional, Dinner, A. R., additional, Feig, M., additional, Fischer, S., additional, Gao, J., additional, Hodoscek, M., additional, Im, W., additional, Kuczera, K., additional, Lazaridis, T., additional, Ma, J., additional, Ovchinnikov, V., additional, Paci, E., additional, Pastor, R. W., additional, Post, C. B., additional, Pu, J. Z., additional, Schaefer, M., additional, Tidor, B., additional, Venable, R. M., additional, Woodcock, H. L., additional, Wu, X., additional, Yang, W., additional, York, D. M., additional, and Karplus, M., additional
- Published
- 2009
- Full Text
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11. Comparative modeling of GABAA receptors: limits, insights, future developments
- Author
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Ernst, M, primary, Brauchart, D, additional, Boresch, S, additional, and Sieghart, W, additional
- Published
- 2003
- Full Text
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12. Dielectric properties of glucose and maltose solutions
- Author
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Höchtl, P., primary, Boresch, S., additional, and Steinhauser, O., additional
- Published
- 2000
- Full Text
- View/download PDF
13. The Meaning of Component Analysis: Decomposition of the Free Energy in Terms of Specific Interactions
- Author
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Boresch, S., primary and Karplus, M., additional
- Published
- 1995
- Full Text
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14. Nonadditive Monte Carlo Simulation of Liquid Hydrogen Chloride Difference Algorithm and Parallel Implementation
- Author
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Steinhauser, O., primary, Boresch, S., additional, and Bertagnolli, H., additional
- Published
- 1991
- Full Text
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15. Towards a better description and understanding of biomolecular solvation
- Author
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Boresch, S., Ringhofer, S., Hochtl, P., and Steinhauser, O.
- Published
- 1999
- Full Text
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16. The Role of Bonded Terms in Free Energy Simulations. 2. Calculation of Their Influence on Free Energy Differences of Solvation
- Author
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Boresch, S. and Karplus, M.
- Abstract
Calculations of the free energy difference of solvation are used to study the contributions arising from alchemical changes of bond stretching and angle bending energy terms in the force field. The results illustrate the theoretical analysis of such terms given in the companion paper (Boresch, S.; Karplus, M. The Role of Bonded Terms in Free Energy Simulations: 1. Theoretical Analysis. J. Phys. Chem. A
1998 , 103, 10310). Three model systems are investigated: (a) two one-dimensional harmonic oscillators interacting with a third particle that represents the solvent, (b) the aqueous solvation of two diatomic molecules, and (c) the aqueous solvation of ethane and methanol. In each case, the computations are carried out with both a single topology and a dual topology methodology. A comparison of free energy components of the single and double free energy differences obtained in the calculations makes it possible to identify the three contributions that the theoretical analysis showed were involved, i.e., vibrational, pmf-type, and Jacobian factor terms.The verification of the theoretical analysis by illustrative examples provides the basis for addressing the question of whether the so-called self-terms can make significant contributions to double free energy differences. This is accomplished by identifying the effect of coupling of the three contributions from bonded energy terms on a double free energy difference. For the model systems studied, coupling and, hence, self-terms are found to be of little importance. The analysis resolves the ambiguities concerning this issue in the literature.- Published
- 1999
17. The Role of Bonded Terms in Free Energy Simulations: 1. Theoretical Analysis
- Author
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Boresch, S. and Karplus, M.
- Abstract
The role of the bonded (bond stretching and bond angle) force-field terms in free energy simulations is examined. It is shown that the proper treatment of such terms depends on the choice of the free energy methodology (single or dual topology). Furthermore, while there are no problems in describing changes in bonded terms, care has to be used in creating or destroying them in a molecular dynamics simulation. An approach that avoids the singularity caused by a bond with a zero force constant is outlined. Changes in bond stretching or bond angle terms are shown to give rise to vibrational, Jacobian factor, and potential-of-mean-force-type (pmf) contributions. The meaning of bond stretching and bond angle bending free energy components obtained in single and dual topology simulations and their connection to these three contributions is investigated. Due to the different end states used in single and dual topology simulations, the pmf contribution is projected on different free energy components. In certain dual topology methods, vibrational and Jacobian factor contributions are not included in the free energy difference. Therefore, single free energy differences (e.g., the free energy difference between two molecules in the gas phase and in solution) often cannot be compared directly between single and dual topology methods. However, identical double free energy differences (e.g., free energy differences of solvation) are obtained in all cases. The present analysis emphasizes the importance of the details of the simulation methodology in interpreting the results for bonded terms and reconciles apparently contradictory findings in the literature.
- Published
- 1999
18. Dielectric properties of glucose and maltose solutions.
- Author
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Ho¨chtl, P., Boresch, S., and Steinhauser, O.
- Subjects
- *
GLUCOSE , *MALTOSE , *MOLECULAR dynamics - Abstract
We report molecular dynamics (MD) simulations of aqueous solutions of glucose and maltose. For each sugar, two concentrations were studied. The static and frequency-dependent dielectric properties of the solutions were calculated from MD trajectories of at least 5 ns length and compared to those of pure water. The contributions from the solute, the solvent, and the solute-solvent cross term were analyzed. In addition, for the more dilute glucose and maltose solutions a Voronoi analysis was carried out to distinguish between contributions from the first water shell and from unbound bulk water. The results of the glucose simulations were compared to available experimental data. While the static dielectric constant of the four solutions was found to be very similar to that of pure water, a number of differences could be discerned in the dielectric spectra. These findings for the overall frequency-dependent dielectric susceptibilities were rationalized by a dielectric component analysis. The importance of contributions from cross terms and from the solute depended on solute type (glucose or maltose) and concentration. In particular, we observed a linear correlation between the contribution of the solute-solvent cross term and the total number of hydroxyl groups of the solute (i.e., the number of solute molecules times the number of hydroxyl groups in a glucose or maltose molecule, respectively). The dielectric properties of water in the solutions could be rationalized as the superposition of two contributions, one originating from the bulklike free waters, the other from the waters in the first hydration shell of the saccharides. © 2000 American Institute of Physics. [ABSTRACT FROM AUTHOR]
- Published
- 2000
- Full Text
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19. Comments on Anomalous Dielectric Relaxation of Aqueous Protein Solutions by Nilashis Nandi and Biman Bagchi (J. Phys. Chem. A 1998, 102, 8217)
- Author
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Boresch, S. and Steinhauser, O.
- Published
- 2001
20. Comparative modeling of GABAA receptors: limits, insights, future developments
- Author
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Ernst, M., Brauchart, D., Boresch, S., and Sieghart, W.
- Subjects
- *
GABA receptors , *CHLORIDE channels - Abstract
GABAA receptors are chloride ion channels that mediate fast synaptic transmission and belong to a superfamily of pentameric ligand-gated ion channels. The recently published crystal structure of the acetylcholine binding protein can be used as a template for comparative modeling of the extracellular domain of GABAA receptors. In this commentary, difficulties with comparative modeling at low sequence identity are discussed, the degree of structural conservation to be expected within the superfamily is analyzed and numerical estimates of model uncertainties in functional regions are provided. Topography of the binding sites at subunit-interfaces is examined and possible targets for rational mutagenesis studies are suggested. Allosteric motions are considered and a mechanism for mediation of positive cooperativity at the benzodiazepine site is proposed. [Copyright &y& Elsevier]
- Published
- 2003
- Full Text
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21. Common Hits Approach: Combining Pharmacophore Modeling and Molecular Dynamics Simulations
- Author
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Thomas Seidel, Thierry Langer, Arthur Garon, Marcus Wieder, Ugo Perricone, Anna Maria Almerico, Stefan Boresch, Wieder, M, Garon, A, Perricone, U, Boresch, S, Seidel, T, Almerico, AM, and Langer, T
- Subjects
0301 basic medicine ,General Chemical Engineering ,Drug Evaluation, Preclinical ,Library and Information Sciences ,Molecular Dynamics Simulation ,computer.software_genre ,Ligands ,LigandScout ,Common Hits Approach (CHA) ,03 medical and health sciences ,Molecular dynamics ,User-Computer Interface ,Computational chemistry ,Pharmacophore Modeling ,Flexibility (engineering) ,Virtual screening ,Chemistry ,Frame (networking) ,Proteins ,General Chemistry ,Into-structure ,Settore CHIM/08 - Chimica Farmaceutica ,Computer Science Applications ,030104 developmental biology ,Data mining ,Pharmacophore ,computer - Abstract
We present a new approach that incorporates flexibility based on extensive MD simulations of protein-ligand complexes into structure-based pharmacophore modeling and virtual screening. The approach uses the multiple coordinate sets saved during the MD simulations and generates for each frame a pharmacophore model. Pharmacophore models with the same pharmacophore features are pooled. In this way the high number of pharmacophore models that results from the MD simulation is reduced to only a few hundred representative pharmacophore models. Virtual screening runs are performed with every representative pharmacophore model; the screening results are combined and rescored to generate a single hit-list. The score for a particular molecule is calculated based on the number of representative pharmacophore models which classified it as active. Hence, the method is called common hits approach (CHA). The steps between the MD simulation and the final hit-list are performed automatically and without user interaction. We test the performance of CHA for virtual screening using screening databases with active and inactive compounds for 40 protein-ligand systems. The results of the CHA are compared to the (i) median screening performance of all representative pharmacophore models of protein-ligand systems, as well as to the virtual screening performance of (ii) a random classifier, (iii) the pharmacophore model derived from the experimental structure in the PDB, and (iv) the representative pharmacophore model appearing most frequently during the MD simulation. For the 34 (out of 40) protein-ligand complexes, for which at least one of the approaches was able to perform better than a random classifier, the highest enrichment was achieved using CHA in 68% of the cases, compared to 12% for the PDB pharmacophore model and 20% for the representative pharmacophore model appearing most frequently. The availabilithy of diverse sets of different pharmacophore models is utilized to analyze some additional questions of interest in 3D pharmacophore-based virtual screening.
- Published
- 2017
22. Evaluating the stability of pharmacophore features using molecular dynamics simulations
- Author
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Stefan Boresch, Ugo Perricone, Marcus Wieder, Thierry Langer, Thomas Seidel, Wieder, M., Perricone, U., Boresch, S., Seidel, T., and Langer, T.
- Subjects
0301 basic medicine ,Protein Flexibility ,Protein Conformation ,Biophysics ,Stability (learning theory) ,Molecular Dynamics Simulation ,Ligands ,01 natural sciences ,Biochemistry ,LigandScout ,Set (abstract data type) ,03 medical and health sciences ,Molecular dynamics ,Computational chemistry ,Feature (machine learning) ,Pharmacophore Modeling ,Sensitivity (control systems) ,Molecular Biology ,Binding Sites ,010405 organic chemistry ,Chemistry ,Structure-based Pharmacophore Modeling ,Molecular Dynamic ,Proteins ,Hydrogen Bonding ,Cell Biology ,0104 chemical sciences ,030104 developmental biology ,Ranking ,Models, Chemical ,Drug Design ,Pharmacophore ,Biological system ,Protein Binding - Abstract
Molecular dynamics simulations of twelve protein—ligand systems were used to derive a single, structure based pharmacophore model for each system. These merged models combine the information from the initial experimental structure and from all snapshots saved during the simulation. We compared the merged pharmacophore models with the corresponding PDB pharmacophore models, i.e., the static models generated from an experimental structure in the usual manner. The frequency of individual features, of feature types and the occurrence of features not present in the static model derived from the experimental structure were analyzed. We observed both pharmacophore features not visible in the traditional approach, as well as features which disappeared rapidly during the molecular dynamics simulations and which may well be artifacts of the initial PDB structure-derived pharmacophore model. Our approach helps mitigate the sensitivity of structure based pharmacophore models to the single set of coordinates present in the experimental structure. Further, the frequency with which specific features occur during the MD simulation may aid in ranking the importance of individual features.
- Published
- 2016
23. CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed.
- Author
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Hwang W, Austin SL, Blondel A, Boittier ED, Boresch S, Buck M, Buckner J, Caflisch A, Chang HT, Cheng X, Choi YK, Chu JW, Crowley MF, Cui Q, Damjanovic A, Deng Y, Devereux M, Ding X, Feig MF, Gao J, Glowacki DR, Gonzales JE 2nd, Hamaneh MB, Harder ED, Hayes RL, Huang J, Huang Y, Hudson PS, Im W, Islam SM, Jiang W, Jones MR, Käser S, Kearns FL, Kern NR, Klauda JB, Lazaridis T, Lee J, Lemkul JA, Liu X, Luo Y, MacKerell AD Jr, Major DT, Meuwly M, Nam K, Nilsson L, Ovchinnikov V, Paci E, Park S, Pastor RW, Pittman AR, Post CB, Prasad S, Pu J, Qi Y, Rathinavelan T, Roe DR, Roux B, Rowley CN, Shen J, Simmonett AC, Sodt AJ, Töpfer K, Upadhyay M, van der Vaart A, Vazquez-Salazar LI, Venable RM, Warrensford LC, Woodcock HL, Wu Y, Brooks CL 3rd, Brooks BR, and Karplus M
- Abstract
Since its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and advances in computer hardware, the range of methods and applicability of CHARMM have also grown. This review summarizes major developments that occurred after 2009 when the last review of CHARMM was published. They include the following: new faster simulation engines, accessible user interfaces for convenient workflows, and a vast array of simulation and analysis methods that encompass quantum mechanical, atomistic, and coarse-grained levels, as well as extensive coverage of force fields. In addition to providing the current snapshot of the CHARMM development, this review may serve as a starting point for exploring relevant theories and computational methods for tackling contemporary and emerging problems in biomolecular systems. CHARMM is freely available for academic and nonprofit research at https://academiccharmm.org/program.
- Published
- 2024
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24. Insights and Challenges in Correcting Force Field Based Solvation Free Energies Using a Neural Network Potential.
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Karwounopoulos J, Wu Z, Tkaczyk S, Wang S, Baskerville A, Ranasinghe K, Langer T, Wood GPF, Wieder M, and Boresch S
- Abstract
We present a comprehensive study investigating the potential gain in accuracy for calculating absolute solvation free energies (ASFE) using a neural network potential to describe the intramolecular energy of the solute. We calculated the ASFE for most compounds from the FreeSolv database using the Open Force Field (OpenFF) and compared them to earlier results obtained with the CHARMM General Force Field (CGenFF). By applying a nonequilibrium (NEQ) switching approach between the molecular mechanics (MM) description (either OpenFF or CGenFF) and the neural net potential (NNP)/MM level of theory (using ANI-2x as the NNP potential), we attempted to improve the accuracy of the calculated ASFEs. The predictive performance of the results did not change when this approach was applied to all 589 small molecules in the FreeSolv database that ANI-2x can describe. When selecting a subset of 156 molecules, focusing on compounds where the force fields performed poorly, we saw a slight improvement in the root-mean-square error (RMSE) and mean absolute error (MAE). The majority of our calculations utilized unidirectional NEQ protocols based on Jarzynski's equation. Additionally, we conducted bidirectional NEQ switching for a subset of 156 solutes. Notably, only a small fraction (10 out of 156) exhibited statistically significant discrepancies between unidirectional and bidirectional NEQ switching free energy estimates.
- Published
- 2024
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25. On Analytical Corrections for Restraints in Absolute Binding Free Energy Calculations.
- Author
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Boresch S
- Subjects
- Ligands, Protein Binding, Proteins chemistry, Proteins metabolism, Molecular Dynamics Simulation, Models, Molecular, Thermodynamics
- Abstract
Double decoupling absolute binding free energy simulations require an intermediate state at which the ligand is held solely by restraints in a position and orientation resembling the bound state. One possible choice consists of one distance, two angle, and three dihedral angle restraints. Here, I demonstrate that in practically all cases the analytical correction derived under the rigid rotator harmonic oscillator approximation is sufficient to account for the free energy of the restraints.
- Published
- 2024
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26. Reweighting from Molecular Mechanics Force Fields to the ANI-2x Neural Network Potential.
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Tkaczyk S, Karwounopoulos J, Schöller A, Woodcock HL, Langer T, Boresch S, and Wieder M
- Abstract
To achieve chemical accuracy in free energy calculations, it is necessary to accurately describe the system's potential energy surface and efficiently sample configurations from its Boltzmann distribution. While neural network potentials (NNPs) have shown significantly higher accuracy than classical molecular mechanics (MM) force fields, they have a limited range of applicability and are considerably slower than MM potentials, often by orders of magnitude. To address this challenge, Rufa et al. [Rufa et al. bioRxiv 2020, 10.1101/2020.07.29.227959.] suggested a two-stage approach that uses a fast and established MM alchemical energy protocol, followed by reweighting the results using NNPs, known as endstate correction or indirect free energy calculation. This study systematically investigates the accuracy and robustness of reweighting from an MM reference to a neural network target potential (ANI-2x) for an established data set in vacuum, using single-step free-energy perturbation (FEP) and nonequilibrium (NEQ) switching simulation. We assess the influence of longer switching lengths and the impact of slow degrees of freedom on outliers in the work distribution and compare the results to those of multistate equilibrium free energy simulations. Our results demonstrate that free energy calculations between NNPs and MM potentials should be preferably performed using NEQ switching simulations to obtain accurate free energy estimates. NEQ switching simulations between the MM potentials and NNPs are efficient, robust, and trivial to implement.
- Published
- 2024
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27. Calculations of Absolute Solvation Free Energies with Transformato─Application to the FreeSolv Database Using the CGenFF Force Field.
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Karwounopoulos J, Kaupang Å, Wieder M, and Boresch S
- Abstract
We recently introduced transformato, an open-source Python package for the automated setup of large-scale calculations of relative solvation and binding free energy differences. Here, we extend the capabilities of transformato to the calculation of absolute solvation free energy differences. After careful validation against the literature results and reference calculations with the PERT module of CHARMM, we used transformato to compute absolute solvation free energies for most molecules in the FreeSolv database (621 out of 642). The force field parameters were obtained with the program cgenff (v2.5.1), which derives missing parameters from the CHARMM general force field (CGenFF v4.6). A long-range correction for the Lennard-Jones interactions was added to all computed solvation free energies. The mean absolute error compared to the experimental data is 1.12 kcal/mol. Our results allow a detailed comparison between the AMBER and CHARMM general force fields and provide a more in-depth understanding of the capabilities and limitations of the CGenFF small molecule parameters.
- Published
- 2023
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28. Exploring Routes to Enhance the Calculation of Free Energy Differences via Non-Equilibrium Work SQM/MM Switching Simulations Using Hybrid Charge Intermediates between MM and SQM Levels of Theory or Non-Linear Switching Schemes.
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Schöller A, Woodcock HL, and Boresch S
- Abstract
Non-equilibrium work switching simulations and Jarzynski's equation are a reliable method for computing free energy differences, ΔAlow→high, between two levels of theory, such as a pure molecular mechanical (MM) and a quantum mechanical/molecular mechanical (QM/MM) description of a system of interest. Despite the inherent parallelism, the computational cost of this approach can quickly become very high. This is particularly true for systems where the core region, the part of the system to be described at different levels of theory, is embedded in an environment such as explicit solvent water. We find that even for relatively simple solute-water systems, switching lengths of at least 5 ps are necessary to compute ΔAlow→high reliably. In this study, we investigate two approaches towards an affordable protocol, with an emphasis on keeping the switching length well below 5 ps. Inserting a hybrid charge intermediate state with modified partial charges, which resembles the charge distribution of the desired high level, makes it possible to obtain reliable calculations with 2 ps switches. Attempts using step-wise linear switching paths, on the other hand, did not lead to improvement, i.e., a faster convergence for all systems. To understand these findings, we analyzed the solutes' properties as a function of the partial charges used and the number of water molecules in direct contact with the solute, and studied the time needed for water molecules to reorient themselves upon a change in the solute's charge distribution.
- Published
- 2023
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29. Relative binding free energy calculations with transformato: A molecular dynamics engine-independent tool.
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Karwounopoulos J, Wieder M, and Boresch S
- Abstract
We present the software package transformato for the setup of large-scale relative binding free energy calculations. Transformato is written in Python as an open source project (https://github.com/wiederm/transformato); in contrast to comparable tools, it is not closely tied to a particular molecular dynamics engine to carry out the underlying simulations. Instead of alchemically transforming a ligand L
1 directly into another L2 , the two ligands are mutated to a common core. Thus, while dummy atoms are required at intermediate states, in particular at the common core state, none are present at the physical endstates. To validate the method, we calculated 76 relative binding free energy differences Δ Δ G L 1 → L 2 b i n d for five protein-ligand systems. The overall root mean squared error to experimental binding free energies is 1.17 kcal/mol with a Pearson correlation coefficient of 0.73. For selected cases, we checked that the relative binding free energy differences between pairs of ligands do not depend on the choice of the intermediate common core structure. Additionally, we report results with and without hydrogen mass reweighting. The code currently supports OpenMM, CHARMM, and CHARMM/OpenMM directly. Since the program logic to choose and construct alchemical transformation paths is separated from the generation of input and topology/parameter files, extending transformato to support additional molecular dynamics engines is straightforward., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Karwounopoulos, Wieder and Boresch.)- Published
- 2022
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30. Alchemical free energy simulations without speed limits. A generic framework to calculate free energy differences independent of the underlying molecular dynamics program.
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Wieder M, Fleck M, Braunsfeld B, and Boresch S
- Subjects
- Entropy, Thermodynamics, Molecular Dynamics Simulation
- Abstract
We describe the theory of the so-called common-core/serial-atom-insertion (CC/SAI) approach to compute alchemical free energy differences and its practical implementation in a Python package called Transformato. CC/SAI is not tied to a specific biomolecular simulation program and does not rely on special purpose code for alchemical transformations. To calculate the alchemical free energy difference between several small molecules, the physical end-states are mutated into a suitable common core. Since this only requires turning off interactions, the setup of intermediate states is straightforward to automate. Transformato currently supports CHARMM and OpenMM as back ends to carry out the necessary molecular dynamics simulations, as well as post-processing calculations. We validate the method by computing a series of relative solvation free energy differences., (© 2022 The Authors. Journal of Computational Chemistry published by Wiley Periodicals LLC.)
- Published
- 2022
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31. Optimizing the Calculation of Free Energy Differences in Nonequilibrium Work SQM/MM Switching Simulations.
- Author
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Schöller A, Kearns F, Woodcock HL, and Boresch S
- Subjects
- Entropy, Molecular Conformation, Thermodynamics, Quantum Theory
- Abstract
A key step during indirect alchemical free energy simulations using quantum mechanical/molecular mechanical (QM/MM) hybrid potential energy functions is the calculation of the free energy difference Δ A between the low level (e.g., pure MM) and the high level of theory (QM/MM). A reliable approach uses nonequilibrium work (NEW) switching simulations in combination with Jarzynski's equation; however, it is computationally expensive. In this study, we investigate whether it is more efficient to use more shorter switches or fewer but longer switches. We compare results obtained with various protocols to reference free energy differences calculated with Crooks' equation. The central finding is that fewer longer switches give better converged results. As few as 200 sufficiently long switches lead to Δ
low→high between the low level (e.g., pure MM) and the high level of theory (QM/MM). A reliable approach uses nonequilibrium work (NEW) switching simulations in combination with Jarzynski's equation; however, it is computationally expensive. In this study, we investigate whether it is more efficient to use more shorter switches or fewer but longer switches. We compare results obtained with various protocols to reference free energy differences calculated with Crooks' equation. The central finding is that fewer longer switches give better converged results. As few as 200 sufficiently long switches lead to Δ Alow→high values in good agreement with the reference results. This optimized protocol reduces the computational cost by a factor of 40 compared to earlier work. We also describe two tools/ways of analyzing the raw data to detect sources of poor convergence. Specifically, we find it helpful to analyze the raw data (work values from the NEW switching simulations) in a quasi-time series-like manner. Principal component analysis helps to detect cases where one or more conformational degrees of freedom are different at the low and high level of theory.- Published
- 2022
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32. Dummy Atoms in Alchemical Free Energy Calculations.
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Fleck M, Wieder M, and Boresch S
- Abstract
In calculations of relative free energy differences, the number of atoms of the initial and final states is rarely the same. This necessitates the introduction of dummy atoms. These placeholders interact with the physical system only by bonded energy terms. We investigate the conditions necessary so that the presence of dummy atoms does not influence the result of a relative free energy calculation. On the one hand, one has to ensure that dummy atoms only give a multiplicative contribution to the partition function so that their contribution cancels from double-free energy differences. On the other hand, the bonded terms used to attach a dummy atom (or group of dummy atoms) to the physical system have to maintain it in a well-defined position and orientation relative to the physical system. A detailed theoretical analysis of both aspects is provided, illustrated by 24 calculations of relative solvation free energy differences, for which all four legs of the underlying thermodynamic cycle were computed. Cycle closure (or lack thereof) was used as a sensitive indicator to probing the effects of dummy atom treatment on the resulting free energy differences. We find that a naive (but often practiced) treatment of dummy atoms results in errors of up to k
BT when calculating the relative solvation free energy difference between two small solutes, such as methane and ammonia. While our analysis focuses on the so-called single topology approach to set up alchemical transformations, similar considerations apply to dual topology, at least many widely used variants thereof.- Published
- 2021
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33. Polarizable molecular dynamics simulations of ionic liquids: Influence of temperature control.
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Heid E, Boresch S, and Schröder C
- Abstract
Ionic liquids are an interesting class of soft matter with viscosities of one or two orders of magnitude higher than that of water. Unfortunately, classical, non-polarizable molecular dynamics (MD) simulations of ionic liquids result in too slow dynamics and demonstrate the need for explicit inclusion of polarizability. The inclusion of polarizability, here via the Drude oscillator model, requires amendments to the employed thermostat, where we consider a dual Nosé-Hoover thermostat, as well as a dual Langevin thermostat. We investigate the effects of the choice of a thermostat and the underlying parameters such as the masses and force constants of the Drude particles on static and dynamic properties of ionic liquids. Here, we show that Langevin thermostats are not suitable for investigating the dynamics of ionic liquids. Since polarizable MD simulations are associated with high computational costs, we employed a self-developed graphics processing unit enhanced code within the MD program CHARMM to keep the overall computational effort reasonable.
- Published
- 2020
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34. Use of Interaction Energies in QM/MM Free Energy Simulations.
- Author
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Hudson PS, Woodcock HL, and Boresch S
- Abstract
The use of the most accurate (i.e., QM or QM/MM) levels of theory for free energy simulations (FES) is typically not possible. Primarily, this is because the computational cost associated with the extensive configurational sampling needed for converging FES is prohibitive. To ensure the feasibility of QM-based FES, the "indirect" approach is generally taken, necessitating a free energy calculation between the MM and QM/MM potential energy surfaces. Ideally, this step is performed with standard free energy perturbation (Zwanzig's equation) as it only requires simulations be carried out at the low level of theory; however, work from several groups over the past few years has conclusively shown that Zwanzig's equation is ill-suited to this task. As such, many approximations have arisen to mitigate difficulties with Zwanzig's equation. One particularly popular notion is that the convergence of Zwanzig's equation can be improved by using interaction energy differences instead of total energy differences. Although problematic numerical fluctuations (a major problem when using Zwanzig's equation) are indeed reduced, our results and analysis demonstrate that this "interaction energy approximation" (IEA) is theoretically incorrect, and the implicit approximation invoked is spurious at best. Herein, we demonstrate this via solvation free energy calculations using IEA from two different low levels of theory to the same target high level. Results from this proof-of-concept consistently yield the wrong results, deviating by ∼1.5 kcal/mol from the rigorously obtained value.
- Published
- 2019
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35. The Good, the Bad, and the Ugly: "HiPen", a New Dataset for Validating (S)QM/MM Free Energy Simulations.
- Author
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Kearns FL, Warrensford L, Boresch S, and Woodcock HL
- Subjects
- Entropy, Quantum Theory, Energy Metabolism, Proteins metabolism, Thermodynamics, Water metabolism
- Abstract
Indirect (S)QM/MM free energy simulations (FES) are vital to efficiently incorporating sufficient sampling and accurate (QM) energetic evaluations when estimating free energies of practical/experimental interest. Connecting between levels of theory, i.e., calculating Δ A l o w → h i g h , remains to be the most challenging step within an indirect FES protocol. To improve calculations of Δ A l o w → h i g h , we must: (1) compare the performance of all FES methods currently available; and (2) compile and maintain datasets of Δ A l o w → h i g h calculated for a wide-variety of molecules so that future practitioners may replicate or improve upon the current state-of-the-art. Towards these two aims, we introduce a new dataset, "HiPen", which tabulates Δ A g a s M M → 3 o b (the free energy associated with switching from an M M to an S C C - D F T B molecular description using the 3 ob parameter set in gas phase), calculated for 22 drug-like small molecules. We compare the calculation of this value using free energy perturbation, Bennett's acceptance ratio, Jarzynski's equation, and Crooks' equation. We also predict the reliability of each calculated Δ A g a s M M → 3 o b by evaluating several convergence criteria including sample size hysteresis, overlap statistics, and bias metric ( Π ). Within the total dataset, three distinct categories of molecules emerge: the "good" molecules, for which we can obtain converged Δ A g a s M M → 3 o b using Jarzynski's equation; "bad" molecules which require Crooks' equation to obtain a converged Δ A g a s M M → 3 o b ; and "ugly" molecules for which we cannot obtain reliably converged Δ A g a s M M → 3 o b with either Jarzynski's or Crooks' equations. We discuss, in depth, results from several example molecules in each of these categories and describe how dihedral discrepancies between levels of theory cause convergence failures even for these gas phase free energy simulations.
- Published
- 2019
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36. Accelerating QM/MM Free Energy Computations via Intramolecular Force Matching.
- Author
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Hudson PS, Boresch S, Rogers DM, and Woodcock HL
- Abstract
The calculation of free energy differences between levels of theory has numerous potential pitfalls. Chief among them is the lack of overlap, i.e., ensembles generated at one level of theory (e.g., "low") not being good approximations of ensembles at the other (e.g., "high"). Numerous strategies have been devised to mitigate this issue. However, the most straightforward approach is to ensure that the "low" level ensemble more closely resembles that of the "high". Ideally, this is done without increasing computational cost. Herein, we demonstrate that by reparametrizing classical intramolecular potentials to reproduce high level forces (i.e., force matching) configurational overlap between a "low" (i.e., classical) and "high" (i.e., quantum) level can be significantly improved. This procedure is validated on two test cases and results in vastly improved convergence of free energy simulations.
- Published
- 2018
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37. SAR-Guided Scoring Function and Mutational Validation Reveal the Binding Mode of CGS-8216 at the α1+/γ2- Benzodiazepine Site.
- Author
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Siebert DCB, Wieder M, Schlener L, Scholze P, Boresch S, Langer T, Schnürch M, Mihovilovic MD, Richter L, Ernst M, and Ecker GF
- Subjects
- Benzodiazepines metabolism, Binding Sites, Humans, Ligands, Molecular Docking Simulation, Protein Subunits chemistry, Protein Subunits metabolism, Pyrazoles chemistry, Receptors, GABA-A chemistry, Software, Structure-Activity Relationship, Pyrazoles pharmacology, Receptors, GABA-A metabolism
- Abstract
The structural resolution of a bound ligand-receptor complex is a key asset to efficiently drive lead optimization in drug design. However, structural resolution of many drug targets still remains a challenging endeavor. In the absence of structural knowledge, scientists resort to structure-activity relationships (SARs) to promote compound development. In this study, we incorporated ligand-based knowledge to formulate a docking scoring function that evaluates binding poses for their agreement with a known SAR. We showcased this protocol by identifying the binding mode of the pyrazoloquinolinone (PQ) CGS-8216 at the benzodiazepine binding site of the GABA
A receptor. Further evaluation of the final pose by molecular dynamics and free energy simulations revealed a close proximity between the pendent phenyl ring of the PQ and γ2D56, congruent with the low potency of carboxyphenyl analogues. Ultimately, we introduced the γ2D56A mutation and in fact observed a 10-fold potency increase in the carboxyphenyl analogue, providing experimental evidence in favor of our binding hypothesis.- Published
- 2018
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38. Computing converged free energy differences between levels of theory via nonequilibrium work methods: Challenges and opportunities.
- Author
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Kearns FL, Hudson PS, Woodcock HL, and Boresch S
- Abstract
We demonstrate that Jarzynski's equation can be used to reliably compute free energy differences between low and high level representations of systems. The need for such a calculation arises when employing the so-called "indirect" approach to free energy simulations with mixed quantum mechanical/molecular mechanical (QM/MM) Hamiltonians; a popular technique for circumventing extensive simulations involving quantum chemical computations. We have applied this methodology to several small and medium sized organic molecules, both in the gas phase and explicit solvent. Test cases include several systems for which the standard approach; that is, free energy perturbation between low and high level description, fails to converge. Finally, we identify three major areas in which the difference between low and high level representations make the calculation of ΔAlow→high difficult: bond stretching and angle bending, different preferred conformations, and the response of the MM region to the charge distribution of the QM region. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)
- Published
- 2017
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39. Common Hits Approach: Combining Pharmacophore Modeling and Molecular Dynamics Simulations.
- Author
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Wieder M, Garon A, Perricone U, Boresch S, Seidel T, Almerico AM, and Langer T
- Subjects
- Ligands, Proteins chemistry, Proteins metabolism, User-Computer Interface, Drug Evaluation, Preclinical methods, Molecular Dynamics Simulation
- Abstract
We present a new approach that incorporates flexibility based on extensive MD simulations of protein-ligand complexes into structure-based pharmacophore modeling and virtual screening. The approach uses the multiple coordinate sets saved during the MD simulations and generates for each frame a pharmacophore model. Pharmacophore models with the same pharmacophore features are pooled. In this way the high number of pharmacophore models that results from the MD simulation is reduced to only a few hundred representative pharmacophore models. Virtual screening runs are performed with every representative pharmacophore model; the screening results are combined and rescored to generate a single hit-list. The score for a particular molecule is calculated based on the number of representative pharmacophore models which classified it as active. Hence, the method is called common hits approach (CHA). The steps between the MD simulation and the final hit-list are performed automatically and without user interaction. We test the performance of CHA for virtual screening using screening databases with active and inactive compounds for 40 protein-ligand systems. The results of the CHA are compared to the (i) median screening performance of all representative pharmacophore models of protein-ligand systems, as well as to the virtual screening performance of (ii) a random classifier, (iii) the pharmacophore model derived from the experimental structure in the PDB, and (iv) the representative pharmacophore model appearing most frequently during the MD simulation. For the 34 (out of 40) protein-ligand complexes, for which at least one of the approaches was able to perform better than a random classifier, the highest enrichment was achieved using CHA in 68% of the cases, compared to 12% for the PDB pharmacophore model and 20% for the representative pharmacophore model appearing most frequently. The availabilithy of diverse sets of different pharmacophore models is utilized to analyze some additional questions of interest in 3D pharmacophore-based virtual screening.
- Published
- 2017
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40. Evaluating the stability of pharmacophore features using molecular dynamics simulations.
- Author
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Wieder M, Perricone U, Boresch S, Seidel T, and Langer T
- Subjects
- Binding Sites, Hydrogen Bonding, Ligands, Protein Binding, Protein Conformation, Drug Design, Models, Chemical, Molecular Dynamics Simulation, Proteins chemistry, Proteins ultrastructure
- Abstract
Molecular dynamics simulations of twelve protein-ligand systems were used to derive a single, structure based pharmacophore model for each system. These merged models combine the information from the initial experimental structure and from all snapshots saved during the simulation. We compared the merged pharmacophore models with the corresponding PDB pharmacophore models, i.e., the static models generated from an experimental structure in the usual manner. The frequency of individual features, of feature types and the occurrence of features not present in the static model derived from the experimental structure were analyzed. We observed both pharmacophore features not visible in the traditional approach, as well as features which disappeared rapidly during the molecular dynamics simulations and which may well be artifacts of the initial PDB structure-derived pharmacophore model. Our approach helps mitigate the sensitivity of structure based pharmacophore models to the single set of coordinates present in the experimental structure. Further, the frequency with which specific features occur during the MD simulation may aid in ranking the importance of individual features., (Copyright © 2016 Elsevier Inc. All rights reserved.)
- Published
- 2016
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41. Comparing pharmacophore models derived from crystal structures and from molecular dynamics simulations.
- Author
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Wieder M, Perricone U, Seidel T, Boresch S, and Langer T
- Abstract
Abstract: Pharmacophore modeling is a widely used technique in computer-aided drug discovery. Structure-based pharmacophore models of a ligand in complex with a protein have proven to be useful for supporting in silico hit discovery, hit to lead expansion, and lead optimization. As a structure-based approach it depends on the correct interpretation of ligand-protein interactions. There are legitimate concerns about the fidelity of the bound ligand and about non-physiological contacts with parts of the crystal and the solvent effects that influence the protein structure. A possible way to refine the structure of a protein-ligand system is to use the final structure of a given MD simulation. In this study we compare pharmacophore models built using the initial protein-ligand structure obtained from the protein data bank (PDB) with pharmacophore models built with the final structure of a molecular dynamics simulation. We show that the pharmacophore models differ in feature number and feature type and that the pharmacophore models built from the last structure of a MD simulation shows in some cases better ability to distinguish between active and decoy ligand structures.
- Published
- 2016
- Full Text
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42. Methods for Efficiently and Accurately Computing Quantum Mechanical Free Energies for Enzyme Catalysis.
- Author
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Kearns FL, Hudson PS, Boresch S, and Woodcock HL
- Subjects
- Animals, Enzymes chemistry, Humans, Biocatalysis, Computer Simulation, Enzymes metabolism, Models, Chemical, Quantum Theory, Thermodynamics
- Abstract
Enzyme activity is inherently linked to free energies of transition states, ligand binding, protonation/deprotonation, etc.; these free energies, and thus enzyme function, can be affected by residue mutations, allosterically induced conformational changes, and much more. Therefore, being able to predict free energies associated with enzymatic processes is critical to understanding and predicting their function. Free energy simulation (FES) has historically been a computational challenge as it requires both the accurate description of inter- and intramolecular interactions and adequate sampling of all relevant conformational degrees of freedom. The hybrid quantum mechanical molecular mechanical (QM/MM) framework is the current tool of choice when accurate computations of macromolecular systems are essential. Unfortunately, robust and efficient approaches that employ the high levels of computational theory needed to accurately describe many reactive processes (ie, ab initio, DFT), while also including explicit solvation effects and accounting for extensive conformational sampling are essentially nonexistent. In this chapter, we will give a brief overview of two recently developed methods that mitigate several major challenges associated with QM/MM FES: the QM non-Boltzmann Bennett's acceptance ratio method and the QM nonequilibrium work method. We will also describe usage of these methods to calculate free energies associated with (1) relative properties and (2) along reaction paths, using simple test cases with relevance to enzymes examples., (© 2016 Elsevier Inc. All rights reserved.)
- Published
- 2016
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43. Use of Nonequilibrium Work Methods to Compute Free Energy Differences Between Molecular Mechanical and Quantum Mechanical Representations of Molecular Systems.
- Author
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Hudson PS, Woodcock HL, and Boresch S
- Abstract
Carrying out free energy simulations (FES) using quantum mechanical (QM) Hamiltonians remains an attractive, albeit elusive goal. Renewed efforts in this area have focused on using "indirect" thermodynamic cycles to connect "low level" simulation results to "high level" free energies. The main obstacle to computing converged free energy results between molecular mechanical (MM) and QM (ΔA(MM→QM)), as recently demonstrated by us and others, is differences in the so-called "stiff" degrees of freedom (e.g., bond stretching) between the respective energy surfaces. Herein, we demonstrate that this problem can be efficiently circumvented using nonequilibrium work (NEW) techniques, i.e., Jarzynski's and Crooks' equations. Initial applications of computing ΔA(NEW)(MM→QM), for blocked amino acids alanine and serine as well as to generate butane's potentials of mean force via the indirect QM/MM FES method, showed marked improvement over traditional FES approaches.
- Published
- 2015
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44. Efficiently computing pathway free energies: New approaches based on chain-of-replica and Non-Boltzmann Bennett reweighting schemes.
- Author
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Hudson PS, White JK, Kearns FL, Hodoscek M, Boresch S, and Lee Woodcock H
- Subjects
- Butanes chemistry, Carbohydrate Conformation, Energy Transfer, Maltose chemistry, Molecular Structure, Reproducibility of Results, Rotation, Solvents chemistry, Torsion, Mechanical, Algorithms, Molecular Dynamics Simulation
- Abstract
Background: Accurately modeling condensed phase processes is one of computation's most difficult challenges. Include the possibility that conformational dynamics may be coupled to chemical reactions, where multiscale (i.e., QM/MM) methods are needed, and this task becomes even more daunting., Methods: Free energy simulations (i.e., molecular dynamics), multiscale modeling, and reweighting schemes., Results: Herein, we present two new approaches for mitigating the aforementioned challenges. The first is a new chain-of-replica method (off-path simulations, OPS) for computing potentials of mean force (PMFs) along an easily defined reaction coordinate. This development is coupled with a new distributed, highly-parallel replica framework (REPDstr) within the CHARMM package. Validation of these new schemes is carried out on two processes that undergo conformational changes. First is the simple torsional rotation of butane, while a much more challenging glycosidic rotation (in vacuo and solvated) is the second. Additionally, a new approach that greatly improves (i.e., possibly an order of magnitude) the efficiency of computing QM/MM PMFs is introduced and compared to standard schemes. Our efforts are grounded in the recently developed method for efficiently computing QM-based free energies (i.e., QM-Non-Boltzmann Bennett, QM-NBB). Again, we validate this new technique by computing the QM/MM PMF of butane's torsional rotation., Conclusions: The OPS-REPDstr method is a promising new approach that overcomes many limitations of standard pathway simulations in CHARMM. The combination of QM-NBB with pathway techniques is very promising as it offers significant advantages over current procedures., General Significance: Efficiently computing potentials of mean force is a major, unresolved, area of interest. This article is part of a Special Issue entitled Recent developments of molecular dynamics., (Copyright © 2014. Published by Elsevier B.V.)
- Published
- 2015
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45. Web-based computational chemistry education with CHARMMing I: Lessons and tutorial.
- Author
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Miller BT, Singh RP, Schalk V, Pevzner Y, Sun J, Miller CS, Boresch S, Ichiye T, Brooks BR, and Woodcock HL 3rd
- Subjects
- Software, Computational Biology education, Computer Simulation, Computer-Assisted Instruction methods, Databases, Protein, Internet, Models, Molecular
- Abstract
This article describes the development, implementation, and use of web-based "lessons" to introduce students and other newcomers to computer simulations of biological macromolecules. These lessons, i.e., interactive step-by-step instructions for performing common molecular simulation tasks, are integrated into the collaboratively developed CHARMM INterface and Graphics (CHARMMing) web user interface (http://www.charmming.org). Several lessons have already been developed with new ones easily added via a provided Python script. In addition to CHARMMing's new lessons functionality, web-based graphical capabilities have been overhauled and are fully compatible with modern mobile web browsers (e.g., phones and tablets), allowing easy integration of these advanced simulation techniques into coursework. Finally, one of the primary objections to web-based systems like CHARMMing has been that "point and click" simulation set-up does little to teach the user about the underlying physics, biology, and computational methods being applied. In response to this criticism, we have developed a freely available tutorial to bridge the gap between graphical simulation setup and the technical knowledge necessary to perform simulations without user interface assistance.
- Published
- 2014
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46. Multiscale Free Energy Simulations: An Efficient Method for Connecting Classical MD Simulations to QM or QM/MM Free Energies Using Non-Boltzmann Bennett Reweighting Schemes.
- Author
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König G, Hudson PS, Boresch S, and Woodcock HL
- Abstract
THE RELIABILITY OF FREE ENERGY SIMULATIONS (FES) IS LIMITED BY TWO FACTORS: (a) the need for correct sampling and (b) the accuracy of the computational method employed. Classical methods (e.g., force fields) are typically used for FES and present a myriad of challenges, with parametrization being a principle one. On the other hand, parameter-free quantum mechanical (QM) methods tend to be too computationally expensive for adequate sampling. One widely used approach is a combination of methods, where the free energy difference between the two end states is computed by, e.g., molecular mechanics (MM), and the end states are corrected by more accurate methods, such as QM or hybrid QM/MM techniques. Here we report two new approaches that significantly improve the aforementioned scheme; with a focus on how to compute corrections between, e.g., the MM and the more accurate QM calculations. First, a molecular dynamics trajectory that properly samples relevant conformational degrees of freedom is generated. Next, potential energies of each trajectory frame are generated with a QM or QM/MM Hamiltonian. Free energy differences are then calculated based on the QM or QM/MM energies using either a non-Boltzmann Bennett approach (QM-NBB) or non-Boltzmann free energy perturbation (NB-FEP). Both approaches are applied to calculate relative and absolute solvation free energies in explicit and implicit solvent environments. Solvation free energy differences (relative and absolute) between ethane and methanol in explicit solvent are used as the initial test case for QM-NBB. Next, implicit solvent methods are employed in conjunction with both QM-NBB and NB-FEP to compute absolute solvation free energies for 21 compounds. These compounds range from small molecules such as ethane and methanol to fairly large, flexible solutes, such as triacetyl glycerol. Several technical aspects were investigated. Ultimately some best practices are suggested for improving methods that seek to connect MM to QM (or QM/MM) levels of theory in FES.
- Published
- 2014
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47. Transport and dielectric properties of water and the influence of coarse-graining: comparing BMW, SPC/E, and TIP3P models.
- Author
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Braun D, Boresch S, and Steinhauser O
- Subjects
- Computer Simulation, Diffusion, Electricity, Models, Chemical, Viscosity, Molecular Dynamics Simulation, Water chemistry
- Abstract
Long-term molecular dynamics simulations are used to compare the single particle dipole reorientation time, the diffusion constant, the viscosity, and the frequency-dependent dielectric constant of the coarse-grained big multipole water (BMW) model to two common atomistic three-point water models, SPC/E and TIP3P. In particular, the agreement between the calculated viscosity of BMW and the experimental viscosity of water is satisfactory. We also discuss contradictory values for the static dielectric properties reported in the literature. Employing molecular hydrodynamics, we show that the viscosity can be computed from single particle dynamics, circumventing the slow convergence of the standard approaches. Furthermore, our data indicate that the Kivelson relation connecting single particle and collective reorientation time holds true for all systems investigated. Since simulations with coarse-grained force fields often employ extremely large time steps, we also investigate the influence of time step on dynamical properties. We observe a systematic acceleration of system dynamics when increasing the time step. Carefully monitoring energy/temperature conservation is found to be a sufficient criterion for the reliable calculation of dynamical properties. By contrast, recommended criteria based on the ratio of fluctuations of total vs. kinetic energy are not sensitive enough.
- Published
- 2014
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48. Comparison of thermodynamic integration and Bennett acceptance ratio for calculating relative protein-ligand binding free energies.
- Author
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de Ruiter A, Boresch S, and Oostenbrink C
- Subjects
- Benzamidines chemistry, Protein Binding, Trypsin Inhibitors chemistry, Models, Chemical, Molecular Dynamics Simulation, Thermodynamics
- Abstract
The performances of Bennett's acceptance ratio method and thermodynamic integration (TI) for the calculation of free energy differences in protein simulations are compared. For the latter, the standard trapezoidal rule, Simpson's rule, and Clenshaw-Curtis integration are used as numerical integration methods. We evaluate the influence of the number and definition of intermediate states on the precision, accuracy, and efficiency of the free energy calculations. Our results show that non-equidistantly spaced intermediate states are in some cases beneficial for the TI methods. Using several combinations of softness parameters and the λ power dependence, it is shown that these benefits are strongly dependent on the shape of the integrand. Although TI is more user-friendly due to its simplicity, it was found that Bennett's acceptance ratio method is the more efficient method. It is also the least dependent on the choice of the intermediate states, making it more robust than TI., (Copyright © 2013 Wiley Periodicals, Inc.)
- Published
- 2013
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49. Absolute hydration free energies of blocked amino acids: implications for protein solvation and stability.
- Author
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König G, Bruckner S, and Boresch S
- Subjects
- Animals, Cattle, Computer Simulation, Protein Denaturation, Protein Stability, Rats, Thermodynamics, Amino Acids chemistry, Proteins chemistry, Solvents chemistry, Water chemistry
- Abstract
Most proteins perform their function in aqueous solution. The interactions with water determine the stability of proteins and the desolvation costs of ligand binding or membrane insertion. However, because of experimental restrictions, absolute solvation free energies of proteins or amino acids are not available. Instead, solvation free energies are estimated based on side chain analog data. This approach implies that the contributions to free energy differences are additive, and it has often been employed for estimating folding or binding free energies. However, it is not clear how much the additivity assumption affects the reliability of the resulting data. Here, we use molecular dynamics-based free energy simulations to calculate absolute hydration free energies for 15 N-acetyl-methylamide amino acids with neutral side chains. By comparing our results with solvation free energies for side chain analogs, we demonstrate that estimates of solvation free energies of full amino acids based on group-additive methods are systematically too negative and completely overestimate the hydrophobicity of glycine. The largest deviation of additive protocols using side chain analog data was 6.7 kcal/mol; on average, the deviation was 4 kcal/mol. We briefly discuss a simple way to alleviate the errors incurred by using side chain analog data and point out the implications of our findings for the field of biophysics and implicit solvent models. To support our results and conclusions, we calculate relative protein stabilities for selected point mutations, yielding a root-mean-square deviation from experimental results of 0.8 kcal/mol., (Copyright © 2013 Biophysical Society. Published by Elsevier Inc. All rights reserved.)
- Published
- 2013
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50. Enhanced Sampling in Free Energy Calculations: Combining SGLD with the Bennett's Acceptance Ratio and Enveloping Distribution Sampling Methods.
- Author
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König G, Miller BT, Boresch S, Wu X, and Brooks BR
- Abstract
One of the key requirements for the accurate calculation of free energy differences is proper sampling of conformational space. Especially in biological applications, molecular dynamics simulations are often confronted with rugged energy surfaces and high energy barriers, leading to insufficient sampling and, in turn, poor convergence of the free energy results. In this work, we address this problem by employing enhanced sampling methods. We explore the possibility of using self-guided Langevin dynamics (SGLD) to speed up the exploration process in free energy simulations. To obtain improved free energy differences from such simulations, it is necessary to account for the effects of the bias due to the guiding forces. We demonstrate how this can be accomplished for the Bennett's acceptance ratio (BAR) and the enveloping distribution sampling (EDS) methods. While BAR is considered among the most efficient methods available for free energy calculations, the EDS method developed by Christ and van Gunsteren is a promising development that reduces the computational costs of free energy calculations by simulating a single reference state. To evaluate the accuracy of both approaches in connection with enhanced sampling, EDS was implemented in CHARMM. For testing, we employ benchmark systems with analytical reference results and the mutation of alanine to serine. We find that SGLD with reweighting can provide accurate results for BAR and EDS where conventional molecular dynamics simulations fail. In addition, we compare the performance of EDS with other free energy methods. We briefly discuss the implications of our results and provide practical guidelines for conducting free energy simulations with SGLD.
- Published
- 2012
- Full Text
- View/download PDF
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