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David A. Case

Bio: David A. Case is an academic researcher from Rutgers University. The author has contributed to research in topics: Solvation & Molecular dynamics. The author has an hindex of 102, co-authored 364 publications receiving 74066 citations. Previous affiliations of David A. Case include University of Utah & Scripps Health.


Papers
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Journal ArticleDOI
TL;DR: A general Amber force field for organic molecules is described, designed to be compatible with existing Amber force fields for proteins and nucleic acids, and has parameters for most organic and pharmaceutical molecules that are composed of H, C, N, O, S, P, and halogens.
Abstract: We describe here a general Amber force field (GAFF) for organic molecules. GAFF is designed to be compatible with existing Amber force fields for proteins and nucleic acids, and has parameters for most organic and pharmaceutical molecules that are composed of H, C, N, O, S, P, and halogens. It uses a simple functional form and a limited number of atom types, but incorporates both empirical and heuristic models to estimate force constants and partial atomic charges. The performance of GAFF in test cases is encouraging. In test I, 74 crystallographic structures were compared to GAFF minimized structures, with a root-mean-square displacement of 0.26 A, which is comparable to that of the Tripos 5.2 force field (0.25 A) and better than those of MMFF 94 and CHARMm (0.47 and 0.44 A, respectively). In test II, gas phase minimizations were performed on 22 nucleic acid base pairs, and the minimized structures and intermolecular energies were compared to MP2/6-31G* results. The RMS of displacements and relative energies were 0.25 A and 1.2 kcal/mol, respectively. These data are comparable to results from Parm99/RESP (0.16 A and 1.18 kcal/mol, respectively), which were parameterized to these base pairs. Test III looked at the relative energies of 71 conformational pairs that were used in development of the Parm99 force field. The RMS error in relative energies (compared to experiment) is about 0.5 kcal/mol. GAFF can be applied to wide range of molecules in an automatic fashion, making it suitable for rational drug design and database searching.

13,615 citations

Journal ArticleDOI
TL;DR: The development, current features, and some directions for future development of the Amber package of computer programs, which contains a group of programs embodying a number of powerful tools of modern computational chemistry, focused on molecular dynamics and free energy calculations of proteins, nucleic acids, and carbohydrates.
Abstract: We describe the development, current features, and some directions for future development of the Amber package of computer programs. This package evolved from a program that was constructed in the late 1970s to do Assisted Model Building with Energy Refinement, and now contains a group of programs embodying a number of powerful tools of modern computational chemistry, focused on molecular dynamics and free energy calculations of proteins, nucleic acids, and carbohydrates.

7,672 citations

Journal ArticleDOI
TL;DR: In this paper, a force field for simulation of nucleic acids and proteins is presented, which is based on the ECEPP, UNECEPP, and EPEN energy refinement software.
Abstract: We present the development of a force field for simulation of nucleic acids and proteins. Our approach began by obtaining equilibrium bond lengths and angles from microwave, neutron diffraction, and prior molecular mechanical calculations, torsional constants from microwave, NMR, and molecular mechanical studies, nonbonded parameters from crystal packing calculations, and atomic charges from the fit of a partial charge model to electrostatic potentials calculated by ab initio quantum mechanical theory. The parameters were then refined with molecular mechanical studies on the structures and energies of model compounds. For nucleic acids, we focused on methyl ethyl ether, tetrahydrofuran, deoxyadenosine, dimethyl phosphate, 9-methylguanine-l-methylcytosine hydrogen-bonded complex, 9-methyladenine-l-methylthymine hydrogen-bonded complex, and 1,3-dimethyluracil base-stacked dimer. Bond, angle, torsional, nonbonded, and hydrogen-bond parameters were varied to optimize the agreement between calculated and experimental values for sugar pucker energies and structures, vibrational frequencies of dimethyl phosphate and tetrahydrofuran, and energies for base pairing and base stacking. For proteins, we focused on 4>,'lt maps of glycyl and alanyl dipeptides, hydrogen-bonding interactions involving the various protein polar groups, and energy refinement calculations on insulin. Unlike the models for hydrogen bonding involving nitrogen and oxygen electron donors, an adequate description of sulfur hydrogen bonding required explicit inclusion of lone pairs. There are two fundamental problems in simulating the struc­ tural and energetic properties of molecules: the first is how to choose an analytical been placed E(R) which correctly describes the energy of the system in terms of its 3N degrees of freedom. The second is how the simulation can search or span conforma­ tional space (R) in order to answer questions posed by the scientist interested in the properties of the system. For complex systems, solution to the first problem are an es­ sential first step in attacking the second problem, and thus, considerable effort has been placed in developing analytical functions that are simple enough to allow one to simulate the properties of complex molecules yet accurate enough to obtain meaningful estimates for structures and energies. In the case of the structures and thermodynamic stabilities of saturated hydrocarbons in inert solvents or the gas phase, the first problem has been essentially solved by molecular mechanics ap­ proaches of Allinger, I Ermer and Lifson,2 and their co-workers. However, for polar and ionic molecules in condensed phases, unsolved questions remain as to the best form of the analytical function E(R). In the area of proteins and peptides, seminal work has come from the Scheraga 3 and Lifson 4 schools. The Scheraga group has used both crystal packing (intermolecular) and con­ formational properties of peptides to arrive at force fields ECEPP, UNECEPP, and EPEN for modeling structural and thermodynamic properties of peptides and proteins. Levitt, using the energy refinement software developed in the Lifson group, has proposed a force field for proteins based on calculations on lysozyme,S and Gelin and Karplus have adapted this software along with many parameters from the Scheraga studies to do molecular dynamics

4,340 citations

Journal ArticleDOI
TL;DR: Anautomatic algorithm of perceiving atom types that are defined in a description table, and an automatic algorithm of assigning bond types just based on atomic connectivity are presented.
Abstract: In molecular mechanics (MM) studies, atom types and/or bond types of molecules are needed to determine prior to energy calculations. We present here an automatic algorithm of perceiving atom types that are defined in a description table, and an automatic algorithm of assigning bond types just based on atomic connectivity. The algorithms have been implemented in a new module of the AMBER packages. This auxiliary module, antechamber (roughly meaning "before AMBER"), can be applied to generate necessary inputs of leap-the AMBER program to generate topologies for minimization, molecular dynamics, etc., for most organic molecules. The algorithms behind the manipulations may be useful for other molecular mechanical packages as well as applications that need to designate atom types and bond types.

4,124 citations

Journal ArticleDOI
TL;DR: A historical perspective on the application of molecular dynamics to biological macromolecules is presented and recent developments combining state-of-the-art force fields with continuum solvation calculations have allowed for the fourth era of MD applications in which one can often derive both accurate structure and accurate relative free energies from molecular dynamics trajectories.
Abstract: A historical perspective on the application of molecular dynamics (MD) to biological macromolecules is presented. Recent developments combining state-of-the-art force fields with continuum solvation calculations have allowed us to reach the fourth era of MD applications in which one can often derive both accurate structure and accurate relative free energies from molecular dynamics trajectories. We illustrate such applications on nucleic acid duplexes, RNA hairpins, protein folding trajectories, and protein−ligand, protein−protein, and protein−nucleic acid interactions.

3,965 citations


Cited by
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01 May 1993
TL;DR: Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems.
Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

29,323 citations

Journal ArticleDOI
TL;DR: An N⋅log(N) method for evaluating electrostatic energies and forces of large periodic systems is presented based on interpolation of the reciprocal space Ewald sums and evaluation of the resulting convolutions using fast Fourier transforms.
Abstract: An N⋅log(N) method for evaluating electrostatic energies and forces of large periodic systems is presented. The method is based on interpolation of the reciprocal space Ewald sums and evaluation of the resulting convolutions using fast Fourier transforms. Timings and accuracies are presented for three large crystalline ionic systems.

24,332 citations

28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal ArticleDOI
TL;DR: The CHARMM (Chemistry at Harvard Macromolecular Mechanics) as discussed by the authors is a computer program that uses empirical energy functions to model macromolescular systems, and it can read or model build structures, energy minimize them by first- or second-derivative techniques, perform a normal mode or molecular dynamics simulation, and analyze the structural, equilibrium, and dynamic properties determined in these calculations.
Abstract: CHARMM (Chemistry at HARvard Macromolecular Mechanics) is a highly flexible computer program which uses empirical energy functions to model macromolecular systems. The program can read or model build structures, energy minimize them by first- or second-derivative techniques, perform a normal mode or molecular dynamics simulation, and analyze the structural, equilibrium, and dynamic properties determined in these calculations. The operations that CHARMM can perform are described, and some implementation details are given. A set of parameters for the empirical energy function and a sample run are included.

14,725 citations

Journal ArticleDOI
TL;DR: NAMD as discussed by the authors is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems that scales to hundreds of processors on high-end parallel platforms, as well as tens of processors in low-cost commodity clusters, and also runs on individual desktop and laptop computers.
Abstract: NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high-end parallel platforms, as well as tens of processors on low-cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This article, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical molecular dynamics force field, equations of motion, and integration methods along with the efficient electrostatics evaluation algorithms employed and temperature and pressure controls used. Features for steering the simulation across barriers and for calculating both alchemical and conformational free energy differences are presented. The motivations for and a roadmap to the internal design of NAMD, implemented in C++ and based on Charm++ parallel objects, are outlined. The factors affecting the serial and parallel performance of a simulation are discussed. Finally, typical NAMD use is illustrated with representative applications to a small, a medium, and a large biomolecular system, highlighting particular features of NAMD, for example, the Tcl scripting language. The article also provides a list of the key features of NAMD and discusses the benefits of combining NAMD with the molecular graphics/sequence analysis software VMD and the grid computing/collaboratory software BioCoRE. NAMD is distributed free of charge with source code at www.ks.uiuc.edu.

14,558 citations