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Lu B.,CAS Academy of Mathematics and Systems Science | Cheng X.,Oak Ridge National Laboratory | Huang J.,University of North Carolina at Chapel Hill | McCammon J.A.,Center for Theoretical Biological Physics
Computer Physics Communications | Year: 2010

A Fortran program package is introduced for rapid evaluation of the electrostatic potentials and forces in biomolecular systems modeled by the linearized Poisson-Boltzmann equation. The numerical solver utilizes a well-conditioned boundary integral equation (BIE) formulation, a node-patch discretization scheme, a Krylov subspace iterative solver package with reverse communication protocols, and an adaptive new version of fast multipole method in which the exponential expansions are used to diagonalize the multipole-to-local translations. The program and its full description, as well as several closely related libraries and utility tools are available at http://lsec.cc.ac.cn/~lubz/afmpb.html and a mirror site at http://mccammon.ucsd.edu/. This paper is a brief summary of the program: the algorithms, the implementation and the usage. Program summary: Program title: AFMPB: Adaptive fast multipole Poisson-Boltzmann solver. Catalogue identifier: AEGB_v1_0. Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEGB_v1_0.html. Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland. Licensing provisions: GPL 2.0. No. of lines in distributed program, including test data, etc.: 453 649. No. of bytes in distributed program, including test data, etc.: 8 764 754. Distribution format: tar.gz. Programming language: Fortran. Computer: Any. Operating system: Any. RAM: Depends on the size of the discretized biomolecular system. Classification: 3. External routines: Pre- and post-processing tools are required for generating the boundary elements and for visualization. Users can use MSMS (http://www.scripps.edu/~sanner/html/msms_home.html) for pre-processing, and VMD (http://www.ks.uiuc.edu/Research/vmd/) for visualization. Sub-programs included: An iterative Krylov subspace solvers package from SPARSKIT by Yousef Saad (http://www-users.cs.umn.edu/~saad/software/SPARSKIT/sparskit.html), and the fast multipole methods subroutines from FMMSuite (http://www.fastmultipole.org/). Nature of problem: Numerical solution of the linearized Poisson-Boltzmann equation that describes electrostatic interactions of molecular systems in ionic solutions. Solution method: A novel node-patch scheme is used to discretize the well-conditioned boundary integral equation formulation of the linearized Poisson-Boltzmann equation. Various Krylov subspace solvers can be subsequently applied to solve the resulting linear system, with a bounded number of iterations independent of the number of discretized unknowns. The matrix-vector multiplication at each iteration is accelerated by the adaptive new versions of fast multipole methods. The AFMPB solver requires other stand-alone pre-processing tools for boundary mesh generation, post-processing tools for data analysis and visualization, and can be conveniently coupled with different time stepping methods for dynamics simulation. Restrictions: Only three or six significant digits options are provided in this version. Unusual features: Most of the codes are in Fortran77 style. Memory allocation functions from Fortran90 and above are used in a few subroutines. Additional comments: The current version of the codes is designed and written for single core/processor desktop machines. Check http://lsec.cc.ac.cn/~lubz/afmpb.html and http://mccammon.ucsd.edu/ for updates and changes. Running time: The running time varies with the number of discretized elements (N) in the system and their distributions. In most cases, it scales linearly as a function of N. © 2010 Elsevier B.V. Source

Plunkett P.,University of California at Santa Barbara | Camley B.A.,University of California at San Diego | Camley B.A.,Center for Theoretical Biological Physics | Weirich K.L.,University of California at Santa Barbara | And 2 more authors.
Soft Matter | Year: 2013

We investigate the kinetics of supported lipid bilayer formation by the adsorption and rupture of uncharged phosphatidylcholine lipid vesicles on to a solid substrate. We model the adsorption process taking into account the distinct vesicle rupture events and growth processes. This includes (i) the initial adhesion and vesicle rupture that nucleates bilayer islands, (ii) the growth and merger of bilayer islands, (iii) enhanced adhesion of vesicles to the bilayer edge, and (iv) the final desorption of excess vesicles from the substrate. These simulation studies give insight into prior experimental observations of adsorption in which an overloading of lipid on the solid substrate occurs before formation of the final supported lipid bilayer. Our model provides an explanation for the features of the interesting universal master curve that was observed for the surface fluorescence intensity in the experimental investigations of Weirich et al. © 2013 The Royal Society of Chemistry. Source

Timson D.J.,Queens University of Belfast | Lindert S.,University of California at San Diego | Lindert S.,Center for Theoretical Biological Physics
Gene | Year: 2013

UDP-galactose 4'-epimerase (GALE) catalyzes the interconversion of UDP-galactose and UDP-glucose, an important step in galactose catabolism. Type III galactosemia, an inherited metabolic disease, is associated with mutations in human GALE. The V94M mutation has been associated with a very severe form of type III galactosemia. While a variety of structural and biochemical studies have been reported that elucidate differences between the wildtype and this mutant form of human GALE, little is known about the dynamics of the protein and how mutations influence structure and function. We performed molecular dynamics simulations on the wildtype and V94M enzyme in different states of substrate and cofactor binding. In the mutant, the average distance between the substrate and both a key catalytic residue (Tyr157) and the enzyme-bound NAD+ cofactor and the active site dynamics are altered making substrate binding slightly less stable. However, overall stability or dynamics of the protein is not altered. This is consistent with experimental findings that the impact is largely on the turnover number (kcat), with less substantial effects on Km. Active site fluctuations were found to be correlated in enzyme with substrate bound to just one of the subunits in the homodimer suggesting inter-subunit communication. Greater active site loop mobility in human GALE compared to the equivalent loop in Escherichia coli GALE explains why the former can catalyze the interconversion of UDP-N-acetylgalactosamine and UDP-N-acetylglucosamine while the bacterial enzyme cannot. This work illuminates molecular mechanisms of disease and may inform the design of small molecule therapies for type III galactosemia. •We present molecular dynamics simulations of HsGALE and the V94M variant.•We find substrate binding to be slightly less stable in the mutant.•The overall stability or dynamics of the protein is not altered by the mutation.•Results may inform design of small molecule therapies for type III galactosemia. © 2013 Elsevier B.V. Source

Lindert S.,University of California at San Diego | Lindert S.,Center for Theoretical Biological Physics | Meiler J.,Vanderbilt University | McCammon J.A.,University of California at San Diego | And 2 more authors.
Journal of Chemical Theory and Computation | Year: 2013

Rosetta is one of the prime tools for high resolution protein structure refinement. While its scoring function can distinguish native-like from non-native-like conformations in many cases, the method is limited by conformational sampling for larger proteins, that is, leaving a local energy minimum in which the search algorithm may get stuck. Here, we test the hypothesis that iteration of Rosetta with an orthogonal sampling and scoring strategy might facilitate exploration of conformational space. Specifically, we run short molecular dynamics (MD) simulations on models created by de novo folding of large proteins into cryoEM density maps to enable sampling of conformational space not directly accessible to Rosetta and thus provide an escape route from the conformational traps. We present a combined MD-Rosetta protein structure refinement protocol that can overcome some of these sampling limitations. Two of four benchmark proteins showed incremental improvement through all three rounds of the iterative refinement protocol. Molecular dynamics is most efficient in applying subtle but important rearrangements within secondary structure elements and is thus highly complementary to the Rosetta refinement, which focuses on side chains and loop regions. © 2013 American Chemical Society. Source

News Article
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Proteins are little Olympians in the games of life, racing around cells to trigger critical processes through interactions with specific genes. Sometimes they’re sprinters, sometimes hurdlers. But they generally find their genetic targets, whatever the obstacles. A new theoretical study by Rice University scientists looks at the roles of those obstacles, and how they hinder – and sometimes help – proteins in finding their targets along strands of DNA. In doing so, Rice biophysicist Anatoly Kolomeisky and his team said they are helping to resolve seeming conflicts between other studies on how proteins carry out their biological tasks. The study appears in the American Chemical Society’s Journal of Physical Chemistry. The authors, including Rice postdoctoral researcher Alexey Shvets and graduate student Maria Kochugaeva, set out to see how proteins actively seeking a target deal with blocking proteins. In computer simulations, they established cases where the obstacle is static on DNA, a hurdle to be overcome, and others where the obstacle is dynamic, repeatedly landing on and disassociating from the strand. “Most previous theories assume there’s only DNA and the protein, but there’s no obstruction between the protein and its target,” Kolomeisky said. “In reality, there are obstructions, and a lot of them. Cells are very crowded systems. The surprising thing is that a protein still manages to find its target sequence efficiently. But how? “We decided to look at these things because the literature has theoretical studies with contrary results,” he said. “One says obstacles are good; another says obstacles are bad. What we’re saying in unifying them is that, yes, sometimes they’re good and sometimes bad. People were looking at specific regimes or parameters and thinking theirs was a general case.” The Rice researchers attacked the challenge in stages. In their simplest simulation, the seeker protein is a sprinter, alighting on DNA and probing for its target, usually finding it in milliseconds. If the target is on the other side of an obstacle, the seeker becomes a hurdler: It eventually disassociates and continues searching. But if it falls within a segment shortened by an obstacle, the seeker finds it faster. “This was a surprise, that the presence of the obstacle is not always a bad thing,” Kolomeisky said. “But in reality, obstacles are not static,” he said. “Taking things step by step, we went to the next, where obstacles bind and unbind to the same place on DNA.” Even in these more-complex models, seeker proteins found benefits, he said. “Dynamic obstacles are generally better than static because sometimes they go away.” Kolomeisky said the fundamental paper should be of interest to anyone who studies gene transcription, signaling pathways or drug design. “This process of proteins finding a target on DNA is the beginning of all biological processes,” he said. “Activating a gene means a protein binds somewhere on DNA and starts a cascade. If they don’t do it well, you might have a problem — so it’s important to understand how and why the process works.” Kolomeisky said the study serves as a baseline for the lab, which will now turn its attention to a more complex scenario in which obstacles can also sprint along DNA, like a moving hurdle. Kolomeisky is a professor of chemistry and of chemical and biomolecular engineering. The Welch Foundation, the National Science Foundation and Rice’s Center for Theoretical Biological Physics supported the research.

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