Traditionally, researchers have relied on sequence alignments to identify similar regions of proteins in order to classify protein function. One major downfall to this technique is that sequence similarity does not necessarily guarantee structural similarity. Therefore structures with similar sequences may not have the same active sites as once thought in the community. Multiple structure alignments have received considerable attention as an alternative to multiple sequence alignments. msTALI is a multiple structure alignment algorithm that utilizes several types of information, including torsion angles, backbone atom positions, surface accessibility, residue type, and others. It combines this information into an efficient progressive alignment algorithm. Applications include protein core extraction, active site identification, and many others. msTALI allows the user to specify the extent to which each type of information is used, and this allows the algorithm to be applicable to a wide variety of problems. Currently our lab is heavily investigating msTALI's utility for active site identification. Current target proteins include ATPases and Kinases.
- Devaun McFarland, Caroline Bullock, Benjamin Mueller, Homayoun Valafar, Application of msTALI in ATPase Active Site Identification, Proceedings of the International Conference on Bioinformatics & Computational Biology (BIOCOMP), July 2016, Las Vegas, NV
- Devaun McFarland, Homayoun Valafar, Utility of msTALI in Protein Active Site Identification, SE Regional IDeA Meeting, Nov 15-17 2013, Little Rock, AK
- Shealy, P., Valafar, H. (2012). Multiple structure alignment with msTALI. BMC bioinformatics, 13(1), 105. doi:10.1186/1471-2105-13-105, PMID:22607234. (NIH-1R01GM081793, MCB-0644195)