Protein distance matrices comparison using sequence alignment techniques

In this paper we present an efficient approach for comparing protein structures based on distance matrices. In the first step, the distance matrices are calculated and normalized in order to avoid the scaling distortion. Further, versions from these matrices are saved on different scales and for each two protein structures we compare their respective equal-scaled matrices. Thus, we avoid the problem of comparing two relatively different sized matrices, which can lead to many ambiguous cases. For the actual comparing of the matrices, a new method is introduced, which combines techniques both of partial dynamic programming for comparing the rows on the matrices, and a greedy approach for consolidating the row-row matching results. Analysis showed that this method is fairly more efficient in terms of time and memory complexity and accuracy as well, in comparison with the MatAlign and DALI algorithms, which are guided from the same idea for matrix alignment.