Background: The medical community requires computational tools that distinguish genetic differences having phenotypic impact within the vast number of mutations that do not. Tools that do this will become increasingly important for those seeking to use human genome sequence data to predict disease, make prognoses, and customize therapy to individual patients.
Results: An approach, termed DETECTER, is proposed to identify sites in a protein sequence where amino acid replacements are likely to have a significant effect on phenotype, including causing genetic disease. This approach uses a model-dependent tool to estimate the normalized replacement rate at individual sites in a protein sequence, based on a history of those sites extracted from an evolutionary analysis of the corresponding protein family. This tool identifies sites that have higher-than-average, average, or lower- than-average rates of change in the lineage leading to the sequence in the population of interest. The rates are then combined with sequence data to determine the likelihoods that particular amino acids were present at individual sites in the evolutionary history of the gene family. These likelihoods are used to predict whether any specific amino acid replacements, if introduced at the site in a modern human population, would have a significant impact on fitness. The DETECTER tool is used to analyze the cystic fibrosis transmembrane conductance regulator (CFTR) gene family.
Conclusions: In this system, DETECTER retrodicts amino acid replacements associated with the cystic fibrosis disease with greater accuracy than alternative approaches. While this result validates this approach for this particular family of proteins only, the approach may be applicable to the analysis of polymorphisms generally, including SNPs in a human population.

Modern yeast living in fleshy fruits rapidly convert sugars into bult ethanol through pyruvate. Pyruvate loses carbon dioxide to become acetaldehyde, which is reduced by alcohol dehydrogenase 1 (Adh1) to ethanol, which accumulates. Yeast later consumes the accumulated ethanol, exploiting Adh2, an Adh1 homolog differing by 24 (of 348) amino acids. Because many microorganisms cannot grow in ethanol, accumulated ethanol may help yeast defend resources in the fruit. We report here the reconstruction of the last common ancestor of Adh1 and Adh2, called AdhA. The kinetic behavior of AdhA suggests that it was optimized to make (not consume) ethanol. This is consistent with the hypothesis that before the Adh1-Adh2 duplication, yeast did not accumulate ethanol for later consumption but rather used AdhA to recycle NADH generated in the glycolytic pathway. Silent nucleotide dating suggests that the Adh1-Adh2 duplication occurred near the time of duplication of several other proteins involved in the accumulation of ethanol, possibly in the Cretaceous age when fleshy fruits arose. These results help to connect the chemical behavior of these enzymes through systems analysis to a time of global ecosystem change, a small but useful step towards a planetary systems biology.

Features of the physical environment surrounding an ancestral organism can be inferred by reconstructing sequences(1-9) of ancient proteins made by those organisms, resurrecting these proteins in the laboratory, and measuring their properties. Here, we resurrect candidate sequences for elongation factors of the Tu family (EF-Tu) found at ancient nodes in the bacterial evolutionary tree, and measure their activities as a function of temperature. The ancient EF-Tu proteins have temperature optima of 55-65degreesC. This value seems to be robust with respect to uncertainties in the ancestral reconstruction. This suggests that the ancient bacteria that hosted these particular genes were thermophiles, and neither hyperthermophiles nor mesophiles. This conclusion can be compared and contrasted with inferences drawn from an analysis of the lengths of branches in trees joining proteins from contemporary bacteria(10), the distribution of thermophily in derived bacterial lineages(11), the inferred G+C content of ancient ribosomal RNA(12), and the geological record combined with assumptions concerning molecular clocks(13). The study illustrates the use of experimental palaeobiochemistry and assumptions about deep phylogenetic relationships between bacteria to explore the character of ancient life.

The history of life on Earth is chronicled in the geological strata, the fossil record, and the genomes of contemporary organisms. When examined together, these records help identify metabolic and regulatory pathways, annotate protein sequences, and identify animal models to develop new drugs, among other features of scientific and biomedical interest. Together, planetary analysis of genome and proteome databases is providing an enhanced understanding of how life interacts with the biosphere and adapts to global change.

Most modern tools that analyze protein evolution allow individual sites to mutate at constant rates over the history of the protein family. However, Walter Fitch observed in the 1970s that, if a protein changes its function, the mutability of individual sites might also change. This observation is captured in the 'non-homogeneous gamma model', which extracts functional information from gene families by examining the different rates at which individual sites evolve. This model has recently been coupled with structural and molecular biology to identify sites that are likely to be involved in changing function within the gene family. Applying this to multiple gene families highlights the widespread divergence of functional behavior among proteins to generate paralogs and orthologs.