Life Sciences Commons^™

Flexc: Protein Flexibility Prediction Using Context-Based Statistics, Predicted Structural Features, And Sequence Information, Ashraf Yaseen, Mais Nijim, Brandon Williams, Lei Qian, Min Li, Jianxin Wang, Yaohang Li

Computer Science Faculty Publications

The fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is significant for analyzing the dynamic properties of proteins which will be helpful in predicting their functions.

Template-Based C8-Scorpion: A Protein 8 State Secondary Structure Prediction Method Using Structural Information And Context-Based Features, Ashraf Yaseen, Yaohang Li

Computer Science Faculty Publications

Background: Secondary structures prediction of proteins is important to many protein structure modeling applications. Correct prediction of secondary structures can significantly reduce the degrees of freedom in protein tertiary structure modeling and therefore reduces the difficulty of obtaining high resolution 3D models.

Methods: In this work, we investigate a template-based approach to enhance 8-state secondary structure prediction accuracy. We construct structural templates from known protein structures with certain sequence similarity. The structural templates are then incorporated as features with sequence and evolutionary information to train two-stage neural networks. In case of structural templates absence, heuristic structural information is incorporated instead. …

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 …

Parallel Progressive Multiple Sequence Alignment On Reconfigurable Meshes, Ken Nguyen, Yi Pan, Ge Nong

Computer Science Faculty Publications

Background: One of the most fundamental and challenging tasks in bio-informatics is to identify related sequences and their hidden biological significance. The most popular and proven best practice method to accomplish this task is aligning multiple sequences together. However, multiple sequence alignment is a computing extensive task. In addition, the advancement in DNA/RNA and Protein sequencing techniques has created a vast amount of sequences to be analyzed that exceeding the capability of traditional computing models. Therefore, an effective parallel multiple sequence alignment model capable of resolving these issues is in a great demand.

Results: We design O(1) run-time solutions …

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/.