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Finding crystal structures from few diffraction data by a combination of a random search with genetic algorithms.

ABSTRACT: A new procedure for performing structural analysis of crystalline materials from diffraction data, using internal coordinates, is described. For starting information only unit-cell content, space group, chemical formula, molecular connectivity and a limited amount of diffraction data are required. After first selecting a number of solutions using a Monte Carlo approach with severe filters, which reject the most unrealistic solutions, genetic algorithms (crossover and mutations) are applied. In fact, the initial selection step alone is, frequently, a powerful tool for discovering structures, without recourse to the genetic algorithms. The procedure, while suffering from the limitation that connectivity must be known, is effective in cases where direct methods are not applicable because the diffraction data are scarce, are limited to low diffraction angles or are missing in specific portions of the reciprocal space. The main features of the algorithm are described and examples of validation given. The routines are now available as part of the freely distributed general-purpose program TRY. The program is available on the Web at http://www.theochem.unisa.it/try.html.

SUBMITTER: Immirzi A

PROVIDER: S-EPMC2483486 | biostudies-literature | 2008 Aug

REPOSITORIES: biostudies-literature

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Finding crystal structures from few diffraction data by a combination of a random search with genetic algorithms.

Immirzi Attilio A Erra Loredana L Tedesco Consiglia C

Journal of applied crystallography 20080716 Pt 4

A new procedure for performing structural analysis of crystalline materials from diffraction data, using internal coordinates, is described. For starting information only unit-cell content, space group, chemical formula, molecular connectivity and a limited amount of diffraction data are required. After first selecting a number of solutions using a Monte Carlo approach with severe filters, which reject the most unrealistic solutions, genetic algorithms (crossover and mutations) are applied. In f ...[more]

PMID: 19461847

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Project description:BackgroundUnraveling the mechanisms that regulate gene expression is a major challenge in biology. An important task in this challenge is to identify regulatory elements, especially the binding sites in deoxyribonucleic acid (DNA) for transcription factors. These binding sites are short DNA segments that are called motifs. Recent advances in genome sequence availability and in high-throughput gene expression analysis technologies have allowed for the development of computational methods for motif finding. As a result, a large number of motif finding algorithms have been implemented and applied to various motif models over the past decade. This survey reviews the latest developments in DNA motif finding algorithms.ResultsEarlier algorithms use promoter sequences of coregulated genes from single genome and search for statistically overrepresented motifs. Recent algorithms are designed to use phylogenetic footprinting or orthologous sequences and also an integrated approach where promoter sequences of coregulated genes and phylogenetic footprinting are used. All the algorithms studied have been reported to correctly detect the motifs that have been previously detected by laboratory experimental approaches, and some algorithms were able to find novel motifs. However, most of these motif finding algorithms have been shown to work successfully in yeast and other lower organisms, but perform significantly worse in higher organisms.ConclusionDespite considerable efforts to date, DNA motif finding remains a complex challenge for biologists and computer scientists. Researchers have taken many different approaches in developing motif discovery tools and the progress made in this area of research is very encouraging. Performance comparison of different motif finding tools and identification of the best tools have proven to be a difficult task because tools are designed based on algorithms and motif models that are diverse and complex and our incomplete understanding of the biology of regulatory mechanism does not always provide adequate evaluation of underlying algorithms over motif models.

Dataset Information

Finding crystal structures from few diffraction data by a combination of a random search with genetic algorithms.

Publications

Finding crystal structures from few diffraction data by a combination of a random search with genetic algorithms.

Similar Datasets

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