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292 results on '"Brooks, B R"'

1. How accurate are polymer models in the analysis of Forster resonance energy transfer experiments on proteins?

Author

O'Brien, E. P., Morrison, G., Brooks, B. R., and Thirumalai, D.

Subjects
Quantitative Biology - Biomolecules
Abstract

Single molecule Forster resonance energy transfer (FRET) experiments are used to infer the properties of the denatured state ensemble (DSE) of proteins. From the measured average FRET efficiency, , the distance distribution P(R) is inferred by assuming that the DSE can be described as a polymer. The single parameter in the appropriate polymer model (Gaussian chain, Wormlike chain, or Self-avoiding walk) for P(R) is determined by equating the calculated and measured . In order to assess the accuracy of this "standard procedure," we consider the generalized Rouse model (GRM), whose properties [ and P(R)] can be analytically computed, and the Molecular Transfer Model for protein L for which accurate simulations can be carried out as a function of guanadinium hydrochloride (GdmCl) concentration. Using the precisely computed for the GRM and protein L, we infer P(R) using the standard procedure. We find that the mean end-to-end distance can be accurately inferred (less than 10% relative error) using and polymer models for P(R). However, the value extracted for the radius of gyration (Rg) and the persistence length (lp) are less accurate. The relative error in the inferred R-g and lp, with respect to the exact values, can be as large as 25% at the highest GdmCl concentration. We propose a self-consistency test, requiring measurements of by attaching dyes to different residues in the protein, to assess the validity of describing DSE using the Gaussian model. Application of the self-consistency test to the GRM shows that even for this simple model the Gaussian P(R) is inadequate. Analysis of experimental data of FRET efficiencies for the cold shock protein shows that at there are significant deviations in the DSE P(R) from the Gaussian model., Comment: 31 pages, 9 figures

Published
2009
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7. Normal modes for large molecules with arbitrary link constraints in the mobile block Hessian approach.

Author

Ghysels, A., Van Neck, D., Brooks, B. R., Van Speybroeck, V., and Waroquier, M.

Subjects
MOLECULAR structure, CHEMICAL structure, ATOMS, MACROMOLECULES, PHYSICAL & theoretical chemistry
Abstract

In a previous paper [Ghysels et al., J. Chem. Phys. 126, 224102 (2007)] the mobile block Hessian (MBH) approach was presented. The method was designed to accurately compute vibrational modes of partially optimized molecular structures. The key concept was the introduction of several blocks of atoms, which can move as rigid bodies with respect to a local, fully optimized subsystem. The choice of the blocks was restricted in the sense that none of them could be connected, and also linear blocks were not taken into consideration. In this paper an extended version of the MBH method is presented that is generally applicable and allows blocks to be adjoined by one or two common atoms. This extension to all possible block partitions of the molecule provides a structural flexibility varying from very rigid to extremely relaxed. The general MBH method is very well suited to study selected normal modes of large macromolecules (such as proteins and polymers) because the number of degrees of freedom can be greatly reduced while still keeping the essential motions of the molecular system. The reduction in the number of degrees of freedom due to the block linkages is imposed here directly using a constraint method, in contrast to restraint methods where stiff harmonic couplings are introduced to restrain the relative motion of the blocks. The computational cost of this constraint method is less than that of an implementation using a restraint method. This is illustrated for the α-helix conformation of an alanine-20-polypeptide. [ABSTRACT FROM AUTHOR]

Published
2009
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8. Theoretical study of the ground-state vibrations of the linear polyenes.

Author

Hemley, R. J., Brooks, B. R., and Karplus, M.

Subjects
*VIBRATION (Mechanics), *POLYENES, *NUCLEAR chemistry, *BUTADIENE
Abstract

Vibrational force fields for the ground states of 1,3-butadiene, 1,3,5-hexatriene, and 1,3,5,7-octatetraene are calculated from extended Pariser–Parr–Pople CI theory. For the ground state of butadiene, the principal π-system force constants are compared to those determined by empirical, other semiempirical, and ab initio methods. The calculated vibrational frequency shifts for a series of deuterium substituted butadienes are found to be in good agreement with experiment. Trends in the principal π-system force constants of the polyenes for the ground states of ethylene through octatetraene are described, including the effects of introducing cis linkages in the polyene chain and of using single- vs single-plus-double-excitation CI. Correlations between the force constants and the calculated vibrational frequencies are discussed, and current assignments of the vibrational spectra of the polyenes are examined in view of the theoretical results. [ABSTRACT FROM AUTHOR]

Published
1986
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11. CHARMM: the biomolecular simulation program

Author

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
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12. CHARMM: The biomolecular simulation program

Author

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

13. Quality improvement in neurology: Amyotrophic lateral sclerosis quality measures: Report of the Quality Measurement and Reporting Subcommittee of the American Academy of Neurology

Author

Miller, R. G., primary, Brooks, B. R., additional, Swain-Eng, R. J., additional, Basner, R. C., additional, Carter, G. T., additional, Casey, P., additional, Cohen, A. B., additional, Dubinsky, R., additional, Forshew, D., additional, Jackson, C. E., additional, Kasarskis, E., additional, Procaccini, N. J., additional, Sanjak, M., additional, and Tolin, F. P., additional

Published
2013
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14. 48th ENMC international workshop: Drug trials and clinical research in ALS 12-14 January 1997, Naarden, the Netherlands

Author

Bensimon, G., Borasio, G., Bossuyt, P. M.M., Brooks, B. R., Camu, W., Chio, A., Conradi, S., Dengler, R., Doble, A., Emery, A. E.H., Hardiman, O., Illa, I., De Jong, J. M.B.V., Kievid, A., Ludolph, A., Markland, C., Meininger, V., Mellinik, M. P., Miller, R. G., Mora, J., Ochs, G., Pouget, J., Robberecht, W., De Sales Luis, M., Shaw, P., Silani, V., Swash, M., Wokke, J. H.J., Epidemiology and Data Science, APH - Methodology, and APH - Personalized Medicine

Subjects
medicine.medical_specialty, Veterinary medicine, Clinical research, Drug trial, Neurology, business.industry, Family medicine, Pediatrics, Perinatology and Child Health, Alternative medicine, medicine, Neurology (clinical), business, Genetics (clinical)
Published
1997

17. CHARMM: The biomolecular simulation program

Author

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
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23. X-linked Dominant ALS

Author

Siddique, T., primary, Hong, S., additional, Brooks, B. R., additional, Hung, W-Y., additional, Siddique, N. A., additional, Rimmler, J., additional, Kaplan, J. P., additional, Haines, J. L., additional, Brown, R. H., additional, and Pericak Vance, M. A., additional

Published
1998
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35. Erratum

Author

Raghunathan, G., primary, Seetharamula, P., additional, Brooks, B. R., additional, and Gua, H. R., additional

Published
1991
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38. Dissociation between mechanical and myoelectrical manifestation of muscle fatigue in amyotrophic lateral sclerosis.

Author

Sanjak, M., Konopacki, R., Capasso, R., Roelke, K. A., Peper, S. M., Houdek, A. M., Waclawik, A., and Brooks, B. R.

Subjects
FATIGUE (Physiology), ELECTROMYOGRAPHY, AMYOTROPHIC lateral sclerosis, NEUROMUSCULAR diseases, MUSCLES, ELECTRODIAGNOSIS
Abstract

Motor fatigue, during 30 seconds of maximum voluntary isometric contraction (MVIC) was simultaneously evaluated by the decline in mechanical force output, and from the compression in the power spectrum obtained from surface electromyogram (sEMG). Measurements were performed in patients diagnosed with amyotrophic lateral sclerosis (ALS) and normal control (NC) in two muscle groups, elbow flexors (EF) and ankle dorsiflexors (DF). The decline in force output, as a manifestation of mechanical fatigue, was digitally calculated online by partitioning the force versus time curve to determine the percent of MVIC reduction over a 30 sec period and was expressed as force fatigue index (FFI). The compression in the sEMG power spectrum, as a manifestation of myoelectrical fatigue, was tracked by calculating the median frequency shift (MFS) from the first 5 sec to the last 5 sec of the 30 sec MVIC using digital Fast Fourier Transformation. In ALS patients, the significantly higher reduction in mechanical force output during the 30 sec MVIC (higher FFI) was accompanied with significantly less compression in the sEMG power spectrum (less MFS) as compared to NC ( P ≤0.005) in the two muscle groups. This dissociation between the mechanical and myoelectrical manifestation of muscle fatigue in ALS indicates that a reduction in muscle fiber conduction velocity (MFCV) may be a contributing peripheral factor in the pathogenesis of muscle fatigue in ALS. Alterations in motor unit functionality, especially in type II fast motor unit muscle fibers, and structural damage in denervated muscle fibers may contribute to the lower MFCV during motor fatigue in ALS patients. [ABSTRACT FROM AUTHOR]

Published
2004
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39. Mosaic chemotherapy strategies for developing ALS/MND therapeutic approaches: Beta-2 adrenergic agonists.

Author

Brooks, B R, Juhasz-Poscine, K, Waclawik, A, Sanjak, M, Belden, D, Roelke, K, Parnell, J, and Weasler, C

Subjects
*AMYOTROPHIC lateral sclerosis treatment, *ADRENERGIC beta agonists
Abstract

Explores strategies to develop mosaic chemotherapeutic approaches for amyotrophic lateral sclerosis/motor neurone disease (ALS/MND). Inclusion of beta-2 adrenergic agonists for ALS/MND treatment; Pathogenesis-based and symptom-directed therapies in ALS/MND; Effect of beta-2 adrenergic agonists in the central nervous system and muscular system; Side-effects of beta-2 adrenergic agonists.

Published
2000
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46. Interaction of DNA with Bifunctional Aldehydes.

Author

Brooks, B. R. and Klamerth, O. L.

Subjects
*DNA, *ALDEHYDES, *ORGANIC compounds, *SUGARS, *ENZYMES, *RADIATION chemistry
Abstract

The bifunctional aldehydes glyoxal and malondialdehyde, known to be toxic products of the radiolysis of sugar and other food constituents, interact with DNA in vitro and in vivo as demonstrated by thermal denaturation profiles, chromatographic behavior, and incomplete degradation of the reaction product by deoxyribonuclease. After irradiation of DNA in diluted radioactive glucose solution followed by incubation at slightly acid pH, the radioactivity is found in the core of the enzymic digest only. Attempts to locate the binding site proved that the radiolytic compound is associated preferentially with the guanine and cytidine moiety of the DNA core. [ABSTRACT FROM AUTHOR]

Published
1968
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47. Reaction Mechanism of Chorismate Mutase Studied by the Combined Potentials of Quantum Mechanics and Molecular Mechanics

Author

Lee, Y. S., Worthington, S. E., Krauss, M., and Brooks, B. R.

Abstract

The reaction path for the rearrangement of chorismate to prephenate in B.subtilis has been determined in a QM/MM study including the entire protein environment while treating the reaction with ab initio quantum chemistry. In addition to the reactant, chorismate, the side-chains of glu78 and arg90 are included in the quantum region to explore whether the strong ionic hydrogen bonding of the side chains to the substrate has a catalytic effect. The hydrogen bonds from glu78 and arg90 induce electronic effects that activate the substrate. The energetic residue analysis finds that the binding from arg7, arg63, and arg90 are all catalytic due to a differential stabilization along the reaction path of the transition state with respect to the reactant by the local environment. A global QM/MM optimization including the entire protein environment shows only slight changes in the protein environment around the active site along the reaction path. The rearrangement reaction occurs with almost a complete break in the C−O ether bond in chorismate before the C−C bond forms to create prephenate. In this study, the reacting complex forms a hydrogen bond to arg63 that stabilizes the region near the protein surface where the substrate may enter the active site.

Published
2002
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50. Theoretically determined three-dimensional structure for the repeating tetrapeptide unit of the circumsporozoite coat protein of the malaria parasite Plasmodium falciparum.

Author

Brooks, B R, Pastor, R W, and Carson, F W

Abstract

A model for the three-dimensional structure of the repeating Asn-Pro-Asn-Ala tetrapeptide of the immunodominant circumsporozoite protein of Plasmodium falciparum has been developed. A trial structure in the form of a type I beta turn with asparagine side chains hydrogen-bonded to the backbone peptide linkages was used as a starting point. A repeating oligomer of this trial structure was modeled using energy minimization and molecular dynamics computer simulations in conjunction with image boundary conditions. The most stable structure generated is a right-handed 12(38) helix, which is unlike any previously identified protein secondary structure. The helix has 12 residues per turn, corresponding to an angle of twist of 120 degrees per tetrapeptide unit, and a pitch of 4.95 A, corresponding to a rise of 1.65 A per tetrapeptide unit. It is highly stabilized by extensive hydrogen bonding, with each tetrapeptide unit acting as an acceptor for five hydrogen bonds and as a donor for five hydrogen bonds to residues in adjacent turns as well as having four weak internal hydrogen bonds. A number of nearly isoenergetic variations on the most stable structure that still retained the basic 12(38) helical motif were also discovered. The implications of these structures for vaccine development are discussed.

Published
1987
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