Dataset Information

TIGER2: an improved algorithm for temperature intervals with global exchange of replicas.

ABSTRACT: An empirical sampling method for molecular simulation based on "temperature intervals with global exchange of replicas" (TIGER2) has been developed to reduce the high demand for computational resources and the low computational efficiency of the conventional replica-exchange molecular dynamics (REMD) method. This new method overcomes the limitation of its previous version, called TIGER, which requires the assumption of constant heat capacity during quenching of replicas from elevated temperatures to the baseline temperature. The robustness of the TIGER2 method is examined by comparing it against a Metropolis Monte Carlo simulation for sampling the conformational distribution of a single butane molecule in vacuum, a REMD simulation for sampling the behavior of alanine dipeptide in explicit solvent, and REMD simulations for sampling the folding behavior of two peptides, (AAQAA)(3) and chignolin, in implicit solvent. The agreement between the results from these conventional sampling methods and the TIGER2 simulations indicates that the TIGER2 algorithm is able to closely approximate a Boltzmann-weighted ensemble of states for these systems but without the limiting assumptions that were required for the original TIGER algorithm. TIGER2 is an efficient replica-exchange sampling method that enables the number of replicas that are used for a replica-exchange simulation to be substantially reduced compared to the conventional REMD method.

SUBMITTER: Li X

PROVIDER: S-EPMC2719473 | biostudies-literature | 2009 May

REPOSITORIES: biostudies-literature

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TIGER2: an improved algorithm for temperature intervals with global exchange of replicas.

Li Xianfeng X Latour Robert A RA Stuart Steven J SJ

The Journal of chemical physics 20090501 17

An empirical sampling method for molecular simulation based on "temperature intervals with global exchange of replicas" (TIGER2) has been developed to reduce the high demand for computational resources and the low computational efficiency of the conventional replica-exchange molecular dynamics (REMD) method. This new method overcomes the limitation of its previous version, called TIGER, which requires the assumption of constant heat capacity during quenching of replicas from elevated temperature ...[more]

PMID: 19425768

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Project description:BackgroundFor the last 25 years species delimitation in prokaryotes (Archaea and Bacteria) was to a large extent based on DNA-DNA hybridization (DDH), a tedious lab procedure designed in the early 1970s that served its purpose astonishingly well in the absence of deciphered genome sequences. With the rapid progress in genome sequencing time has come to directly use the now available and easy to generate genome sequences for delimitation of species. GBDP (Genome Blast Distance Phylogeny) infers genome-to-genome distances between pairs of entirely or partially sequenced genomes, a digital, highly reliable estimator for the relatedness of genomes. Its application as an in-silico replacement for DDH was recently introduced. The main challenge in the implementation of such an application is to produce digital DDH values that must mimic the wet-lab DDH values as close as possible to ensure consistency in the Prokaryotic species concept.ResultsCorrelation and regression analyses were used to determine the best-performing methods and the most influential parameters. GBDP was further enriched with a set of new features such as confidence intervals for intergenomic distances obtained via resampling or via the statistical models for DDH prediction and an additional family of distance functions. As in previous analyses, GBDP obtained the highest agreement with wet-lab DDH among all tested methods, but improved models led to a further increase in the accuracy of DDH prediction. Confidence intervals yielded stable results when inferred from the statistical models, whereas those obtained via resampling showed marked differences between the underlying distance functions.ConclusionsDespite the high accuracy of GBDP-based DDH prediction, inferences from limited empirical data are always associated with a certain degree of uncertainty. It is thus crucial to enrich in-silico DDH replacements with confidence-interval estimation, enabling the user to statistically evaluate the outcomes. Such methodological advancements, easily accessible through the web service at http://ggdc.dsmz.de, are crucial steps towards a consistent and truly genome sequence-based classification of microorganisms.

Dataset Information

TIGER2: an improved algorithm for temperature intervals with global exchange of replicas.

Publications

TIGER2: an improved algorithm for temperature intervals with global exchange of replicas.

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