Alkane hydroxylase genes in psychrophile genomes and the potential for cold active catalysis.

BackgroundPsychrophiles are presumed to play a large role in the catabolism of alkanes and other components of crude oil in natural low temperature environments. In this study we analyzed the functional diversity of genes for alkane hydroxylases, the enzymes responsible for converting alkanes to more labile alcohols, as found in the genomes of nineteen psychrophiles for which alkane degradation has not been reported. To identify possible mechanisms of low temperature optimization we compared putative alkane hydroxylases from these psychrophiles with homologues from nineteen taxonomically related mesophilic strains.ResultsSeven of the analyzed psychrophile genomes contained a total of 27 candidate alkane hydroxylase genes, only two of which are currently annotated as alkane hydroxylase. These candidates were mostly related to the AlkB and cytochrome p450 alkane hydroxylases, but several homologues of the LadA and AlmA enzymes, significant for their ability to degrade long-chain alkanes, were also detected. These putative alkane hydroxylases showed significant differences in primary structure from their mesophile homologues, with preferences for specific amino acids and increased flexibility on loops, bends, and α-helices.ConclusionA focused analysis on psychrophile genomes led to discovery of numerous candidate alkane hydroxylase genes not currently annotated as alkane hydroxylase. Gene products show signs of optimization to low temperature, including regions of increased flexibility and amino acid preferences typical of psychrophilic proteins. These findings are consistent with observations of microbial degradation of crude oil in cold environments and identify proteins that can be targeted in rate studies and in the design of molecular tools for low temperature bioremediation

Multiple Biolgical Sequence Alignment: Scoring Functions, Algorithms, and Evaluations

Aligning multiple biological sequences such as protein sequences or DNA/RNA sequences is a fundamental task in bioinformatics and sequence analysis. These alignments may contain invaluable information that scientists need to predict the sequences\u27 structures, determine the evolutionary relationships between them, or discover drug-like compounds that can bind to the sequences. Unfortunately, multiple sequence alignment (MSA) is NP-Complete. In addition, the lack of a reliable scoring method makes it very hard to align the sequences reliably and to evaluate the alignment outcomes. In this dissertation, we have designed a new scoring method for use in multiple sequence alignment. Our scoring method encapsulates stereo-chemical properties of sequence residues and their substitution probabilities into a tree-structure scoring scheme. This new technique provides a reliable scoring scheme with low computational complexity. In addition to the new scoring scheme, we have designed an overlapping sequence clustering algorithm to use in our new three multiple sequence alignment algorithms. One of our alignment algorithms uses a dynamic weighted guidance tree to perform multiple sequence alignment in progressive fashion. The use of dynamic weighted tree allows errors in the early alignment stages to be corrected in the subsequence stages. Other two algorithms utilize sequence knowledge-bases and sequence consistency to produce biological meaningful sequence alignments. To improve the speed of the multiple sequence alignment, we have developed a parallel algorithm that can be deployed on reconfigurable computer models. Analytically, our parallel algorithm is the fastest progressive multiple sequence alignment algorithm