Dynamic programming used to align protein structures with a spectrum is robust.
Several efficient algorithms to conduct pairwise comparisons among large databases of protein structures have emerged in the recent literature. The central theme is the design of a measure between the Cα atoms of two protein chains, from which dynamic programming is used to compute an alignment. The efficiency and efficacy of these algorithms allows large-scale computational studies that would have been previously impractical. The computational study herein shows that the structural alignment algorithm eigen-decomposition alignment with the spectrum (EIGAs) is robust against both parametric and structural variation.
Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN 47803, USA. holder@rose-hulman.edu.
Allen
A
Holder
Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN 47803, USA. simonjc@rose-hulman.edu.
Jacqueline
J
Simon
Roche Diagnostics, Indianapolis, IN 46256, USA. strauserj@upmc.edu.
Jonathon
J
Strauser
Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN 47803, USA. taylorj7@rose-hulman.edu.
Jonathan
J
Taylor
Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN 47803, USA. shibberu@rose-hulman.edu.
Yosi
Y
Shibberu