Vorometric - Integrated search and alignment of protein structures using Voronoi contacts

Motivation: Identification and comparison of similar three dimensional (3D) protein structures has become an even greater challenge in the face of the rapidly growing structure databases. Here we introduce Vorometric, a new method that provides efficient search and alignment of a query protein against a database of protein structures. Voronoi contacts of the protein residues are enriched with the secondary structure information and a metric substitution matrix is developed to allow efficient indexing. The contact hits obtained from a distance-based indexing method are extended to obtain high scoring segment pairs, which are then used to generate structural alignments.
Results: Vorometric is the first to address both search and alignment problems in the protein structure databases. The experimental results show that Vorometric is simultaneously effective in retrieving similar protein structures, producing high quality structure alignments, and identifying cross-fold similarities. Vorometric outperforms current structure retrieval methods in search accuracy, while requiring comparable running times. Furthermore, the structural superpositions produced are shown to have better quality and coverage, when compared with those of the popular structure alignment tools.
Availability: Vorometric is available as a web service at http://bio.cse.ohio-state.edu/Vorometric
Contact: sacan [at] se.ohio-state.edu

Publication:

Ahmet Sacan, I. Hakki Toroslu, and Hakan Ferhatosmanoglu. Integrated Search and Alignment of Protein Structures. Bioinformatics, doi: 10.1093/bioinformatics/btn545, 2008

Smolign - Spatial Motifs Based Multiple Protein Structure Alignment

Available as a web service and downloadable java application at: http://bio.cse.ohio-state.edu/Smolign

EPO - Enhanced partial order curve comparison over multiple protein folding trajectories.

Available as a web service and downloadable java application at: http://bio.cse.ohio-state.edu/EPO

CellTrack - An Open-Source Software for Cell Tracking and Motility Analysis

Motivation: Cell motility is a critical part of many important biological processes. Automated and sensitive cell tracking is essential to cell motility studies where the tracking results can be used for diagnostic or curative decisions and where mathematical models can be developed to deepen our understanding of the mechanisms underlying cell motility.
Results: We have developed CellTrack: a self-contained, extensible, and cross-platform software package for cell tracking and motility analysis. Besides the general purpose image enhancement, object segmentation and tracking algorithms, we have implemented a novel edge-based method for sensitive tracking of the cell boundaries, and constructed an ensemble of methods that achieves refined tracking results even under large displacements or deformations of the cells.
Availability: CellTrack is an Open Source project and is freely available at http://db.cse.ohio-state.edu/CellTrack
Contact:

Publication:

Ahmet Sacan, Hakan Ferhatosmanoglu, and Huseyin Coskun. CellTrack: An Open-Source Software for Cell Tracking and Motility Analysis. Bioinformatics, 24(14):1647-1649, 2008.

MaD - Microarray Designer An Online Search Tool and Repository for Near-Optimal Microarray Experimental Designs

This software is currently being reviewed as part of a paper submission. Please check later for details and availability.

LFM-Pro: A Tool for Detecting Significant Local Structural Sites in Proteins

Motivation:The rapidly growing protein structure repositories have opened up new opportunities for discovery and analysis of functional and evolutionary relationships among proteins. Detecting conserved structural sites that are unique to a protein family is of great value in identification of functionally important atoms and residues. Currently available methods are computationally expensive and fail to detect biologically significant localfeatures.
Results:We propose LFM-Pro (Local Feature Mining in Proteins) as a framework for automatically discovering family specific local sites and the features associated with these sites. Our method uses the distance field to backbone atoms to detect geometrically significant structural centers of the protein. A feature vector is generated from the geometrical and bio-chemical environment around these centers. These features are then scored using a statistical measure, for their ability to distinguish a family of proteins from a background set of unrelated proteins, and successful features are combined into a representative set for the protein family. The utility and success of LFM-Pro are demonstrated on Trypsin-like Serine Proteases family of proteins. The results verify that our method is successful both in identifying the distinctive sites of a given family of proteins, and in classifying proteins using the extracted features.
Availability:The software is freely available for academic research use. See the link below.
Contact:

, {ozturk,hakan,yusu}@cse.ohio-state.edu

Publication - please cite the following:

Ahmet Sacan, Ozgur Ozturk, Hakan Ferhatosmanoglu, and Yusu Wang. LFM-Pro: A Tool for Detecting Significant Local Structural Sites in Proteins. Bioinformatics, 23(6):709-716, 2007.

Download Software:

You can download the LFM-Pro software package here. Please unzip the package, and follow the installation instructions available in the README.TXT file.