Functional divergence between evolutionary-related LuxG and Fre oxidoreductases of luminous bacteria.

In luminous bacteria NAD(P)H:flavin-oxidoreductases LuxG and Fre, there are homologous enzymes that could provide a luciferase with reduced flavin. Although Fre functions as a housekeeping enzyme, LuxG appears to be a source of reduced flavin for bioluminescence as it is transcribed together with luciferase. This study is aimed at providing the basic conception of Fre and LuxG evolution and revealing the peculiarities of the active site structure resulted from a functional variation within the oxidoreductase family. A phylogenetic analysis has demonstrated that Fre and LuxG oxidoreductases have evolved separately after the gene duplication event, and consequently, they have acquired changes in the conservation of functionally related sites. Namely, different evolutionary rates have been observed at the site responsible for specificity to flavin substrate (Arg 46). Also, Tyr 72 forming a part of a mobile loop involved in FAD binding has been found to be conserved among Fre in contrast to LuxG oxidoreductases. The conservation of different amino acid types in NAD(P)H binding site has been defined for Fre (arginine) and LuxG (proline) oxidoreductases.

Bacterial promoter prediction: Selection of dynamic and static physical properties of DNA for reliable sequence classification.

Predicting promoter activity of DNA fragment is an important task for computational biology. Approaches using physical properties of DNA to predict bacterial promoters have recently gained a lot of attention. To select an adequate set of physical properties for training a classifier, various characteristics of DNA molecule should be taken into consideration. Here, we present a systematic approach that allows us to select less correlated properties for classification by means of both correlation and cophenetic coefficients as well as concordance matrices. To prove this concept, we have developed the first classifier that uses not only sequence and static physical properties of DNA fragment, but also dynamic properties of DNA open states. Therefore, the best performing models with accuracy values up to 90% for all types of sequences were obtained. Furthermore, we have demonstrated that the classifier can serve as a reliable tool enabling promoter DNA fragments to be distinguished from promoter islands despite the similarity of their nucleotide sequences.