Clustering of protein domains for functional and evolutionary studies.

BACKGROUND: The number of protein family members defined by DNA sequencing is usually much larger than those characterised experimentally. This paper describes a method to divide protein families into subtypes purely on sequence criteria. Comparison with experimental data allows an independent test of the quality of the clustering. RESULTS: An evolutionary split statistic is calculated for each column in a protein multiple sequence alignment; the statistic has a larger value when a column is better described by an evolutionary model that assumes clustering around two or more amino acids rather than a single amino acid. The user selects columns (typically the top ranked columns) to construct a motif. The motif is used to divide the family into subtypes using a stochastic optimization procedure related to the deterministic annealing EM algorithm (DAEM), which yields a specificity score showing how well each family member is assigned to a subtype. The clustering obtained is not strongly dependent on the number of amino acids chosen for the motif. The robustness of this method was demonstrated using six well characterized protein families: nucleotidyl cyclase, protein kinase, dehydrogenase, two polyketide synthase domains and small heat shock proteins. Phylogenetic trees did not allow accurate clustering for three of the six families. CONCLUSION: The method clustered the families into functional subtypes with an accuracy of 90 to 100%. False assignments usually had a low specificity score.

Plasticity of the streptomyces genome-evolution and engineering of new antibiotics.

Streptomyces is a genus of soil dwelling bacteria with the ability to produce natural products that have found widespread use in medicine. Annotation of Streptomyces genome sequences has revealed far more biosynthetic gene clusters than previously imagined, offering exciting possibilities for future combinatorial biosynthesis. Experiments to manipulate modular biosynthetic clusters to create novel chemistries often result in no detectable product or product yield is extremely low. Understanding the coupling between components in these hybrid enzymes will be crucial for efficient synthesis of new compounds. We are using new algebraic approaches to predict protein properties, and homologous recombination to exploit natural evolutionary constraints to generate novel functional enzymes. The methods and techniques developed could easily be adapted to study modular, multi-interacting complex systems where appreciable biochemical and comparative sequence data are available, for example, clinically significant non-ribosomally synthesised peptides and polyketides.

Copulas in QTL mapping.

The standard variance components method for mapping quantitative trait loci is derived on the assumption of normality. Unsurprisingly, statistical tests based on this method do not perform so well if this assumption is not satisfied. We use the statistical concept of copulas to relax the assumption of normality and derive a test that can perform well under any distribution of the continuous trait. In particular, we discuss bivariate normal copulas in the context of sib-pair studies. Our approach is illustrated by a linkage analysis of lipoprotein(a) levels, whose distribution is highly skewed. We demonstrate that the asymptotic critical levels of the test can still be calculated using the interval mapping approach. The new method can be extended to more general pedigrees and multivariate phenotypes in a similar way as the original variance components method.