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Circular permutation as a tool to reduce surface entropy triggers crystallization of the signal recognition particle receptor beta subunit.

The production of diffraction-quality crystals remains a difficult obstacle on the road to high-resolution structural characterization of proteins. This is primarily a result of the empirical nature of the process. Although crystallization is not predictable, factors inhibiting it are well established. First, crystal formation is always entropically unfavorable. Reducing the entropic cost of crystallizing a given protein is thus desirable. It is common practice to map boundaries and remove unstructured regions surrounding the folded protein domain. However, a problem arises when flexible regions are not at the boundaries but within a domain. Such regions cannot be deleted without adding new restraints to the domain. We encountered this problem during an attempt to crystallize the beta subunit of the eukaryotic signal recognition particle (SRbeta), bearing a long and flexible internal loop. Native SRbeta did not crystallize. However, after circularly permuting the protein by connecting the spatially close N and C termini with a short heptapeptide linker GGGSGGG and removing 26 highly flexible loop residues within the domain, we obtained diffraction-quality crystals. This protein-engineering method is simple and should be applicable to other proteins, especially because N and C termini of protein domains are often close in space. The success of this method profits from prior knowledge of the domain fold, which is becoming increasingly common in today's postgenomic era.

Crystallization↗

Statistical measures of the structure of genomic sequences: entropy, complexity, and position information.

Identifying regions of DNA with extreme statistical characteristics is an important aspect of the structural analysis of complete genomes. Linguistic methods, mainly based on estimating word frequency, can be used for this as they allow for the delineation of regions of low complexity. Low complexity may be due to biased nucleotide composition, by tandem- or dispersed repeats, by palindrome-hairpin structures, as well as by a combination of all these features. We developed software tools in which various numerical measures of text complexity are implemented, including combinatorial and linguistic ones. We also added Hurst exponent estimate to the software to measure dependencies in DNA sequences. By applying these tools to various functional genomic regions, we demonstrate that the complexity of introns and regulatory regions is lower than that of coding regions, whilst Hurst exponent is larger. Further analysis of promoter sequences revealed that the lower complexity of these regions is associated with long-range correlations caused by transcription factor binding sites.

Algorithms↗

Recognition of protein coding genes in the yeast genome based on the relative-entropy of DNA.

In this paper, a new protein coding gene-finding algorithm specific for the yeast genome at 96% accuracy is proposed. Five-fold cross-validation tests are performed to confirm the above accuracy. By the new algorithm, the number of protein coding genes in the yeast genome is re-estimated. The estimate is based on the assumption that the unknown genes have similar statistical properties to the known genes. It is found that the number of protein coding genes in the 16 yeast chromosomes is less than or equal to 5,873, significantly coincident with the widely accepted range 5,800-6,000.

Algorithms↗

Entropy optimization by the PFANN network: application to blind source separation.

The aim of this paper is to present a study of polynomial functional-link neural units that learn through an information-theoretic-based criterion. First the structure of the neuron is presented and the unsupervised learning theory is explained and discussed, with particular attention being paid to its probability density function and cumulative distribution function approximation capability. Then a neural network formed by such neurons (the polynomial functional-link artificial neural network, or PFANN) is shown to be able to separate out linearly mixed eterokurtic source signals, i.e. signals endowed with either positive or negative kurtoses. In order to compare the performance of the proposed blind separation technique with those exhibited by existing methods, the mixture of densities (MOD) approach of Xu et al, which is closely related to PFANN, is briefly recalled; then comparative numerical simulations performed on both synthetic and real-world signals and a complexity evaluation are illustrated. These results show that the PFANN approach gives similar performance with a noticeable reduction in computational effort.

Artificial Intelligence↗