|
|
|
 |
v2.12 |
A completely redesigned web-based version of our Disulfide by Design application with added functionality.
|
|---|
|
v1.2 |
Disulfide by Design is an application for the rational design of disulfide bonds in proteins. For a given protein structural model, all residue pairs are rapidly assessed for proximity and
geometry consistent with disulfide formation assuming the residues were mutated to cysteines. The output displays residue pairs meeting the appropriate criteria. The input model will typically
be a Protein Data Bank (PDB) structure for the protein of interest; however, structures developed through homology modeling may also be used. Engineered disulfides have proven useful for increasing
the stability of proteins and to assist the investigation of protein dynamics and interactions. This software was written by Dr. Alan Dombkowski
and based on algorithms created for disulfide identification in protein fold recognition methods.
|
|---|
 |
v3.0 |
A web tool for augmented annotation and functional analysis of Oryctolagus cuniculus (rabbit) genes. A list of rabbit genes can be submitted using several identifier types,
including probe IDs from the standard Agilent gene expression microarray for rabbit. In addition to providing rabbit annotations from input gene lists, Better Bunny can identify putative orthologues
in human, mouse, and rat. Extensive functional annotation analysis can then be performed on these orthologous genes using an integrated link to DAVID.
|
|---|
 |
v1.6 |
Aberrant microRNA activity is found in many diseases, and studies often report numerous microRNAs concurrently dysregulated. Most target genes have binding sites for multiple microRNAs,
and it is important to consider their combinatorial effect on target gene repression. miR-AT! is a computational tool for the prediction of combinatorial activity of microRNAs.
Among the features of miR-AT! are: the ability to predict combinatorial targets for a list of microRNAs input by the user, selectable parameters for minimum number of sites and
number of unique microRNAs found in each target, minimum score criteria, integrated functional analysis of predicted targets, and a novel clustering implementation that enables
identification of transcripts with similar microRNA target site patterns. A publication demonstrating the utility of miR-AT! can be found
here.
|
|---|
|
|
|
|