Abstract
In this paper, we proposed SProt - a novel algorithm for measuring protein structure similarity that puts emphasis on high-quality modeling of local similarities of the amino acids. This is achieved by representing each amino acid by its spatial neighborhood containing close amino acids.
The approach leads to good realworld results, especially for superfamily/fold classification accuracy and for precision at high recall levels where we outperform all the compared solutions. The focus on the quality of the modeling results in high computational demands of the method.
We resolve this handicap be introduction of SProt access method - a modification of LAESA metric access method - that highly decreases the runtime needed for scanning large datasets of protein structures. The speedup makes SProt competitive with the best contemporary solutions not only concerning the effectiveness but also the efficiency.