Life Sciences Commons^™

Secondary Structure, A Missing Component Of Sequence- Based Minimotif Definitions, David P. Sargeant, Michael R. Gryk, Mark W. Maciejewsk, Vishal Thapar, Vamsi Kundeti, Sanguthevar Rajasekaran, Pedro Romero, Keith Dunker, Shun-Cheng Li, Tomonori Kaneko, Martin Schiller

Life Sciences Faculty Research

Minimotifs are short contiguous segments of proteins that have a known biological function. The hundreds of thousands of minimotifs discovered thus far are an important part of the theoretical understanding of the specificity of protein-protein interactions, posttranslational modifications, and signal transduction that occur in cells. However, a longstanding problem is that the different abstractions of the sequence definitions do not accurately capture the specificity, despite decades of effort by many labs. We present evidence that structure is an essential component of minimotif specificity, yet is not used in minimotif definitions. Our analysis of several known minimotifs as case studies, analysis …

Characterization Of Beryllium As A Novel Agent To Study Cell Cycle Arrest And Cellular Senescence, Priyatham Gorjala

UNLV Theses, Dissertations, Professional Papers, and Capstones

Cancer cells evade senescence, apoptosis, and other constraints on proliferation, often via mutation of the p53 tumor suppressor gene (TP53). Normal human lung fibroblasts have been shown to enter premature senescence upon exposure to beryllium. In these cells, BeSO₄ stabilizes p53 protein, increases p21 gene expression, induces senescence-associated β-galactosidase activity and causes cell proliferation arrest. In the present study, we have investigated whether BeSO₄ is able to induce similar effects in cancer cells that have wildtype p53. We have demonstrated that beryllium salt at low concentration can induce molecular changes in the p53 signaling pathway leading to cell …

Achieving High Accuracy Prediction Of Minimotifs, Tian Mi, Sanguthevar Rajasekaran, Jerlin Camilus Merlin, Michael R. Gryk, Martin Schiller

Life Sciences Faculty Research

The low complexity of minimotif patterns results in a high false-positive prediction rate, hampering protein function prediction. A multi-filter algorithm, trained and tested on a linear regression model, support vector machine model, and neural network model, using a large dataset of verified minimotifs, vastly improves minimotif prediction accuracy while generating few false positives. An optimal threshold for the best accuracy reaches an overall accuracy above 90%, while a stringent threshold for the best specificity generates less than 1% false positives or even no false positives and still produces more than 90% true positives for the linear regression and neural network …

Reducing False-Positive Prediction Of Minimotifs With A Genetic Interaction Filter, Jerlin Camilus Merlin, Sanguthevar Rajasekaran, Tian Mi, Martin Schiller

Life Sciences Faculty Research

Background

Minimotifs are short contiguous peptide sequences in proteins that have known functions. At its simplest level, the minimotif sequence is present in a source protein and has an activity relationship with a target, most of which are proteins. While many scientists routinely investigate new minimotif functions in proteins, the major web-based discovery tools have a high rate of false-positive prediction. Any new approach that reduces false-positives will be of great help to biologists.

Methods and Findings

We have built three filters that use genetic interactions to reduce false-positive minimotif predictions. The basic filter identifies those minimotifs where the source/target …