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A High-Performance Parallel-Generalized Born Implementation Enabled by Tabulated Interaction Rescaling
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
2010 (English)In: Journal of Computational Chemistry, ISSN 0192-8651, E-ISSN 1096-987X, Vol. 31, no 14, 2593-2600 p.Article in journal (Refereed) Published
Abstract [en]

Implicit solvent representations, in general, and generalized Born models, in particular, provide an attractive way to reduce the number of interactions and degrees of freedom in a system. The instantaneous relaxation of the dielectric shielding provided by an implicit solvent model can be extremely efficient for high-throughput and Monte Carlo studies, and a reduced system size can also remove a lot of statistical noise. Despite these advantages, it has been difficult for generalized Born implementations to significantly outperform optimized explicit-water simulations due to more complex functional forms and the two extra interaction stages necessary to calculate Born radii and the derivative chain rule terms contributing to the force. Here, we present a method that uses a rescaling transformation to make the standard generalized Born expression a function of a single variable, which enables an efficient tabulated implementation on any modern CPU hardware. The total performance is within a factor 2 of simulations in vacuo. The algorithm has been implemented in Gromacs, including single-instruction multiple-data acceleration, for three different Born radius models and corresponding chain rule terms. We have also adapted the model to work with the virtual interaction sites commonly used for hydrogens to enable long-time steps, which makes it possible to achieve a simulation performance of 0.86 μs/day for BBA5 with 1-nm cutoff on a single quad-core desktop processor. Finally, we have also implemented a set of streaming kernels without neighborlists to accelerate the non-cutoff setup occasionally used for implicit solvent simulations of small systems.

Place, publisher, year, edition, pages
2010. Vol. 31, no 14, 2593-2600 p.
Keyword [en]
generalized born, tabulation, molecular dynamics
National Category
Natural Sciences
URN: urn:nbn:se:su:diva-38246DOI: 10.1002/jcc.21552ISI: 000282309800007OAI: diva2:308383
Available from: 2010-04-06 Created: 2010-04-06 Last updated: 2011-12-05Bibliographically approved
In thesis
1. Prediction, modeling, and refinement of protein structure
Open this publication in new window or tab >>Prediction, modeling, and refinement of protein structure
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Accurate predictions of protein structure are important for understanding many processes in cells. The interactions that govern protein folding and structure are complex, and still far from completely understood. However, progress is being made in many areas. Here, efforts to improve the overall quality of protein structure models are described. From a pure evolutionary perspective, in which proteins are viewed in the light of gradually accumulated mutations on the sequence level, it is shown how information from multiple sources helps to create more accurate models. A very simple but surprisingly accurate method for assigning confidence measures for protein structures is also tested. In contrast to models based on evolution, physics based methods view protein structures as the result of physical interactions between atoms. Newly implemented methods are described that both increase the time-scales accessible for molecular dynamics simulations almost 10-fold, and that to some extent might be able to refine protein structures. Finally, I compare the efficiency and properties of different techniques for protein structure refinement.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2010. 64 p.
Protein structure prediction, Multiple alignments, Quality assessment, Molecular dynamics, Implicit solvent, Refinement
National Category
Bioinformatics and Systems Biology Bioinformatics (Computational Biology) Theoretical Chemistry
Research subject
urn:nbn:se:su:diva-38253 (URN)978-91-7447-036-9 (ISBN)
Public defence
2010-05-12, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 10:00 (English)
At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 4: In press. Paper 5: Manuscript. Available from: 2010-04-20 Created: 2010-04-06 Last updated: 2010-04-09Bibliographically approved

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Larsson, PerLindahl, Erik
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