VariVis: a visualisation toolkit for variation databases

<p>Abstract</p> <p>Background</p> <p>With the completion of the Human Genome Project and recent advancements in mutation detection technologies, the volume of data available on genetic variations has risen considerably. These data are stored in online variation databases and provide important clues to the cause of diseases and potential side effects or resistance to drugs. However, the data presentation techniques employed by most of these databases make them difficult to use and understand.</p> <p>Results</p> <p>Here we present a visualisation toolkit that can be employed by online variation databases to generate graphical models of gene sequence with corresponding variations and their consequences. The VariVis software package can run on any web server capable of executing Perl CGI scripts and can interface with numerous Database Management Systems and "flat-file" data files. VariVis produces two easily understandable graphical depictions of any gene sequence and matches these with variant data. While developed with the goal of improving the utility of human variation databases, the VariVis package can be used in any variation database to enhance utilisation of, and access to, critical information.</p

Structural interpretation of mutations in phenylalanine hydroxylase protein aids in identifying genotype-phenotype correlations in phenylketonuria

Phenylalanine hydroxylase (PAH) is the enzyme that converts phenylalanine to tyrosine as a rate-limiting step in phenylalanine catabolism and protein and neurotransmitter biosynthesis. Over 300 mutations have been identified in the gene encoding PAH that result in a deficient enzyme activity and lead to the disorders hyperphenylalaninaemia and phenylketonuria. The determination of the crystal structure of PAH now allows the determination of the structural basis of mutations resulting in PAH deficiency. We present an analysis of the structural basis of 120 mutations with a 'classified' biochemical phenotype and/or available in vitro expression data. We find that the mutations can be grouped into five structural categories, based on the distinct expected structural and functional effects of the mutations in each category. Missense mutations and small amino acid deletions are found in three categories:'active site mutations', 'dimer interface mutations', and 'domain structure mutations'. Nonsense mutations and splicing mutations form the category of 'proteins with truncations and large deletions'. The final category, 'fusion proteins', is caused by frameshift mutations. We show that the structural information helps formulate some rules that will help predict the likely effects of unclassified and newly discovered mutations: proteins with truncations and large deletions, fusion proteins and active site mutations generally cause severe phenotypes; domain structure mutations and dimer interface mutations spread over a range of phenotypes, but domain structure mutations in the catalytic domain are more likely to be severe than domain structure mutations in the regulatory domain or dimer interface mutations

Difficulties in finding DNA mutations and associated phenotypic data in web resources using simple, uncomplicated search terms, and a suggested solution.

DNA mutation data currently reside in many online databases, which differ markedly in the terminology used to describe or define the mutation and also in completeness of content, potentially making it difficult both to locate a mutation of interest and to find sought-after data (eg phenotypic effect). To highlight the current deficiencies in the accessibility of web-based genetic variation information, we examined the ease with which various resources could be interrogated for five model mutations, using a set of simple search terms relating to the change in amino acid or nucleotide. Fifteen databases were investigated for the time and/or number of mouse clicks; clicks required to find the mutations; availability of phenotype data; the procedure for finding information; and site layout. Google and PubMed were also examined. The three locus-specific databases (LSDBs) generally yielded positive outcomes, but the 12 genome-wide databases gave poorer results, with most proving not to be searchable and only three yielding successful outcomes. Google and PubMed searches found some mutations and provided patchy information on phenotype. The results show that many web-based resources are not currently configured for fast and easy access to comprehensive mutation data, with only the isolated LSDBs providing optimal outcomes. Centralising this information within a common repository, coupled with a simple, all-inclusive interrogation process, would improve searching for all gene variation data

Functional analysis, using in vitro mutagenesis, of amino acids located in the phenylalanine hydroxylase active site

The 3-dimensionaI structure determination of rat phenylalanine hydroxylase (PAH) has identified potentially important amino acids lining the active site cleft with the majority of these having hydrophobic side-chains including several with aromatic side chains. Here we have analyzed the effect on rat PAH enzyme kinetics of in vitro mutagenesis of a number of these amino acids lining the PAH active site. Mutation of F299, Y324, F331, and Y343 caused a significant decrease in enzyme activity but no change in the K-m for substrate or cofactor. me conclude that these aromatic residues are essential for activity but are not significantly involved in binding of the substrate or cofactor. in contrast the PAH mutant, S349T, showed an 18-fold increase in K-m for phenylalanine, showing the first functional evidence that this residue was binding at or near the phenylalanine binding site. This confirms the recently published model for the binding of phenylalanine to the PAH active site that postulated S349 interacts with the amino group on the main chain of the phenylalanine molecule. This result differs with that found for the equivalent mutation (S395T), in the closely related tyrosine hydroxylase, which had no effect on substrate K-m, showing that while the architecture of the two active sites are very similar the amino acids that bind to the respective substrates are different. (C) 2000 Academic Press