Life Sciences Commons^™

Mimosa: A System For Minimotif Annotation, Jay Vyas, Ronald J. Nowling, Thomas Meusburger, David P. Sargeant, Krishna Kadaveru, Michael R. Gryk, Vamsi Kundeti, Sanguthevar Rajasekaran, Martin Schiller

Life Sciences Faculty Research

BACKGROUND:

Minimotifs are short peptide sequences within one protein, which are recognized by other proteins or molecules. While there are now several minimotif databases, they are incomplete. There are reports of many minimotifs in the primary literature, which have yet to be annotated, while entirely novel minimotifs continue to be published on a weekly basis. Our recently proposed function and sequence syntax for minimotifs enables us to build a general tool that will facilitate structured annotation and management of minimotif data from the biomedical literature.

RESULTS:

We have built the MimoSA application for minimotif annotation. The application supports management of …

Partitioning Of Minimotifs Based On Function With Improved Prediction Accuracy, Sanguthevar Rajasekaran, Tian Mi, Jerlin Camilus Merlin, Aaron Oommen, Patrick R. Gradie, Martin R. Schiller

Life Sciences Faculty Research

Background

Minimotifs are short contiguous peptide sequences in proteins that are known to have a function in at least one other protein. One of the principal limitations in minimotif prediction is that false positives limit the usefulness of this approach. As a step toward resolving this problem we have built, implemented, and tested a new data-driven algorithm that reduces false-positive predictions.

Methodology/Principal Findings

Certain domains and minimotifs are known to be strongly associated with a known cellular process or molecular function. Therefore, we hypothesized that by restricting minimotif predictions to those where the minimotif containing protein and target protein have …

Venn, A Tool For Titrating Sequence Conservation Onto Protein Structures, Jay Vyas, Michael R. Gryk, Martin R. Schiller

Life Sciences Faculty Research

Residue conservation is an important, established method for inferring protein function, modularity and specificity. It is important to recognize that it is the 3D spatial orientation of residues that drives sequence conservation. Considering this, we have built a new computational tool, VENN that allows researchers to interactively and graphically titrate sequence homology onto surface representations of protein structures. Our proposed titration strategies reveal critical details that are not readily identified using other existing tools. Analyses of a bZIP transcription factor and receptor recognition of Fibroblast Growth Factor using VENN revealed key specificity determinants. Weblink: http://sbtools.uchc.edu/venn/.

A Critical Role For Kalirin In Ngf Signaling Through Trka, Kausik Chakrabarti, Rong Lin, Noraisha I. Schiller, Yanping Wang, David Koubi, Ying-Xin Fan, Brian B. Rudkin, Gibbes R. Johnson, Martin R. Schiller

Life Sciences Faculty Research

Kalirin is a multidomain guanine nucleotide exchange factor (GEF) that activates Rho proteins, inducing cytoskeletal rearrangement in neurons. Although much is known about the effects of Kalirin on Rho GTPases and neuronal morphology, little is known about the association of Kalirin with the receptor/signaling systems that affect neuronal morphology. Our experiments demonstrate that Kalirin binds to and colocalizes with the TrkA neurotrophin receptor in neurons. In PC12 cells, inhibition of Kalirin expression using antisense RNA decreased nerve growth factor (NGF)-induced TrkA autophosphorylation and process extension. Kalirin overexpression potentiated neurotrophin-stimulated TrkA autophosphorylation and neurite outgrowth in PC12 cells at a low …