Fast protein structure alignment using Gaussian overlap scoring of backbone peptide fragment similarity

Motivation: Aligning and comparing protein structures is important for understanding their evolutionary and functional relationships. With the rapid growth of protein structure databases in recent years, the need to align, superpose, and compare protein structures rapidly and accurately has never been greater. Many structural alignment algorithms have been described in the last 20 years. However, achie-ving an algorithm which is both accurate and very fast remains a considerable challenge. Results: We have developed a novel protein structure alignment algorithm called Kpax which exploits the highly predictable cova-lent geometry of C atoms to dene multiple local coordinate frames in which backbone peptide fragments may be oriented and com-pared using sensitive Gaussian overlap scoring functions. A global alignment and hence a structural superposition may then be found rapidly using dynamic programming with secondary structure specic gap penalties. When superposing pairs of structures, Kpax tends to give tighter secondary structure overlays than several popular struc-ture alignment algorithms. When searching the CATH database, Kpax is faster and more accurate than the very efcient Yakusa algorithm, and it gives almost the same high level of fold recognition as TM-Align while being over 100 times faster