PubMed Health⌕ Search

Biomedical subjects

Jason E Donald

Publications and source records attributed to Jason E Donald.

3 recordsLinked to original sources
PubMed2005

Predicting specificity-determining residues in two large eukaryotic transcription factor families.

Certain amino acid residues in a protein, when mutated, change the protein's function. We present an improved method of finding these specificity-determining positions that uses all the protein sequence data available for a family of homologous proteins. We study in detail two families of eukaryotic transcription factors, basic leucine zippers and nuclear receptors, because of the large amount of sequences and experimental data available. These protein families also have a clear definition of functional specificity: DNA-binding specificity. We compare our results to three other methods, including the evolutionary trace algorithm and a method that depends on orthology relationships. All of the predictions are compared to the available mutational and crystallographic data. We find that our method provides superior predictions of the known specificity-determining residues and also predicts residue positions within these families that deserve further study for their roles in functional specificity.

Algorithms↗
PubMed2005

Determining functional specificity from protein sequences.

MOTIVATION: Given a large family of homologous protein sequences, many methods can divide the family into smaller groups that correspond to the different functions carried out by proteins within the family. One important problem, however, has been the absence of a general method for selecting an appropriate level of granularity, or size of the groups. RESULTS: We propose a consistent way of choosing the granularity that is independent of the sequence similarity and sequence clustering method used. We study three large, well-investigated protein families: basic leucine zippers, nuclear receptors and proteins with three consecutive C2H2 zinc fingers. Our method is tested against known functional information, the experimentally determined binding specificities, using a simple scoring method. The significance of the groups is also measured by randomizing the data. Finally, we compare our algorithm against a popular method of grouping proteins, the TRIBE-MCL method. In the end, we determine that dividing the families at the proposed level of granularity creates very significant and useful groups of proteins that correspond to the different DNA-binding motifs. We expect that such groupings will be useful in studying not only DNA binding but also other protein interactions.

Algorithms↗