Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Physical Sciences and Mathematics
Initial Characterization Of A Conserved Active Site Residue For The Cdc34 Ubiquitin Conjugating Enzyme, Arvin Akoopie
Initial Characterization Of A Conserved Active Site Residue For The Cdc34 Ubiquitin Conjugating Enzyme, Arvin Akoopie
Honors College Theses
Ubiquitin-conjugating enzymes (E2s) covalently modify protein substrates with ubiquitins. The active site cysteine residues on E2s are essential for catalyzing the transfer of ubiquitin from the E2 active site onto the protein substrate, however there is a limited amount of information available concerning additional active site residues for E2s that may also participate in catalysis. Cdc34 is an essential E2 that has merited the lion’s share of attention for biochemical analysis of the E2 family. Previous phylogenetic analysis of Cdc34 amino acid sequences has identified an invariably conserved histidine residue close to the active site cysteine in the primary structure, …
Achieving High Accuracy Prediction Of Minimotifs, Tian Mi, Sanguthevar Rajasekaran, Jerlin Camilus Merlin, Michael R. Gryk, Martin Schiller
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 …
Venn, A Tool For Titrating Sequence Conservation Onto Protein Structures, Jay Vyas, Michael R. Gryk, Martin R. Schiller
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/.