Algorithmic complexity of protein structure alignment

COMP 265

Greg Dewey, GregDewey@kgi.edu and Yuting Jia. Applied Life Sciences, Keck Graduate Institute, 535 Watson Drive, Claremont, CA 91711
Structural information can identify common functional and evolutionary relationships in proteins with low sequence similarity. Consequently, structural alignments often reveal commonalities not seen by sequence alignment. Despite this important role in bioinformatics, structural alignment algorithms, scoring systems and significance testing are not at the level of sophistication of their counterparts in sequence alignment. We propose a new alignment method in which one protein structure is related to a second by the number of operations that must be performed to make the protein isomorphic with the first. Group operations about internal coordinates are used to transform one protein's coordinates into another. The similarity of two proteins can be described as the minimal number of operations in this transformation. This definition of protein similarity is closely related to the algorithmic complexity of the alignment and provides an information metric of structural similarities. Results on lattice proteins are discussed using this general method.