GPU/CPU Algorithm for Generalized Born/Solvent-Accessible Surface Area Implicit Solvent Calculations

Tanner, David E.; Phillips, J. C.; Schulten, Klaus

doi:10.1021/ct3003089

Cited by 53 publications

(63 citation statements)

References 70 publications

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“…In addition, computation with the GB model in mixed double and single precision on GPU has been demonstrated to be fast and can reproduce the results in double precision on CPU [90]. An efficient GB/SA calculation scheme was also introduced into NAMD, in which the GB term is computed on the GPU while the SA term is calculated on the CPU, thereby implementing a hybrid CPU/GPU architecture [142]. …”

Section: Molecular Models For Membranes and Membrane Proteinsmentioning

confidence: 99%

Molecular dynamics simulations of biological membranes and membrane proteins using enhanced conformational sampling algorithms

Mori

Miyashita²,

et al. 2016

Biochimica et Biophysica Acta (BBA) - Biomembranes

127

108

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This paper reviews various enhanced conformational sampling methods and explicit/implicit solvent/membrane models, as well as their recent applications to the exploration of the structure and dynamics of membranes and membrane proteins. Molecular dynamics simulations have become an essential tool to investigate biological problems, and their success relies on proper molecular models together with efficient conformational sampling methods. The implicit representation of solvent/membrane environments is reasonable approximation to the explicit all-atom models, considering the balance between computational cost and simulation accuracy. Implicit models can be easily combined with replica-exchange molecular dynamics methods to explore a wider conformational space of a protein. Other molecular models and enhanced conformational sampling methods are also briefly discussed. As application examples, we introduce recent simulation studies of glycophorin A, phospholamban, amyloid precursor protein, and mixed lipid bilayers and discuss the accuracy and efficiency of each simulation model and method. This article is part of a Special Issue entitled: Membrane Proteins. Guest Editors: J.C. Gumbart and Sergei Noskov.

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Section: Molecular Models For Membranes and Membrane Proteinsmentioning

confidence: 99%

Molecular dynamics simulations of biological membranes and membrane proteins using enhanced conformational sampling algorithms

Mori

Miyashita²,

et al. 2016

Biochimica et Biophysica Acta (BBA) - Biomembranes

127

108

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“…Without well-defined EM densities for the nascent chain outside the ribosome, an initial model of the 85-AA nascent peptide was built by placing amino acids inside the cradle of the trigger factor according to distance information from cross-linking experiments [18]. The chain was then subjected to 50 ns of all-atom equilibration simulations in implicit solvent [49], with the TF and the ribosome constrained in positions and with harmonic distance restraints applied between the nascent chain and the trigger factor; the distance restraints were set up based on the cross-linking experiments [18] (Supplementary table 10). To extend the chain to 145 amino acids, the model of the 85-AA chain was mutated and 60 more amino acids were added in a straight conformation to the N-terminus, followed by 50 ns of all-atom equilibration simulations in implicit solvent [49].…”

Section: Simulated Systems and Molecular Dynamics Protocolsmentioning

confidence: 99%

“…The chain was then subjected to 50 ns of all-atom equilibration simulations in implicit solvent [49], with the TF and the ribosome constrained in positions and with harmonic distance restraints applied between the nascent chain and the trigger factor; the distance restraints were set up based on the cross-linking experiments [18] (Supplementary table 10). To extend the chain to 145 amino acids, the model of the 85-AA chain was mutated and 60 more amino acids were added in a straight conformation to the N-terminus, followed by 50 ns of all-atom equilibration simulations in implicit solvent [49]. Position and distance restraints similar to those applied in the equilibration of the 85-AA chain were employed.…”

Section: Simulated Systems and Molecular Dynamics Protocolsmentioning

confidence: 99%

Dynamic Behavior of Trigger Factor on the Ribosome

Deeng

Chan

Sluis

et al. 2016

Journal of Molecular Biology

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Trigger factor (TF) is the only ribosome-associated chaperone in bacteria. It interacts with hydrophobic segments in nascent chain (NCs) as they emerge from the ribosome. TF binds via its N-terminal ribosome-binding domain (RBD) mainly to ribosomal protein uL23 at the tunnel exit on the large ribosomal subunit. Whereas earlier structural data suggested that TF binds as a rigid molecule to the ribosome, recent comparisons of structural data on substrate-bound, ribosome-bound, and TF in solution from different species suggest that this chaperone is a rather flexible molecule. Here, we present two cryo-electron microscopy structures of TF bound to ribosomes translating an mRNA coding for a known TF substrate from Escherichia coli of a different length. The structures reveal distinct degrees of flexibility for the different TF domains, a conformational rearrangement of the RBD upon ribosome binding, and an increase in rigidity within TF when the NC is extended. Molecular dynamics simulations agree with these data and offer a molecular basis for these observations.

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“…In the last decade, we have noted an increasing use of GPU computing in molecular modelling, rendering and visualization [KBE09, SSE*10, DBG10, LBH11, KKC*11, CVT*11, PTRV12, TPS12, PRV13, DG13, PTRV13, LLNW14, DCD*14, DG15, HGVV16]. However cavity detection methods taking advantage of GPU processing power are not so commonly found in the literature; the exception lies in the methods we describe below.…”

Section: Gpu‐based Methodsmentioning

confidence: 99%

Geometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A Survey

Simões

Lopes

Dias

et al. 2017

Computer Graphics Forum

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Detecting and analyzing protein cavities provides significant information about active sites for biological processes (e.g., protein-protein or protein-ligand binding) in molecular graphics and modeling. Using the three-dimensional structure of a given protein (i.e., atom types and their locations in 3D) as retrieved from a PDB (Protein Data Bank) file, it is now computationally viable to determine a description of these cavities. Such cavities correspond to pockets, clefts, invaginations, voids, tunnels, channels, and grooves on the surface of a given protein. In this work, we survey the literature on protein cavity computation and classify algorithmic approaches into three categories: evolution-based, energy-based, and geometry-based. Our survey focuses on geometric algorithms, whose taxonomy is extended to include not only sphere-, grid-, and tessellation-based methods, but also surface-based, hybrid geometric, consensus, and time-varying methods. Finally, we detail those techniques that have been customized for GPU (Graphics Processing Unit) computing.

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GPU/CPU Algorithm for Generalized Born/Solvent-Accessible Surface Area Implicit Solvent Calculations

Cited by 53 publications

References 70 publications

Molecular dynamics simulations of biological membranes and membrane proteins using enhanced conformational sampling algorithms

Molecular dynamics simulations of biological membranes and membrane proteins using enhanced conformational sampling algorithms

Dynamic Behavior of Trigger Factor on the Ribosome

Geometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A Survey

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