Finding short structural motifs for re-construction of proteins 3D structure

Abstract

With a view to find useful building blocks (short structural motifs) for reconstruction of 3D structure of proteins, we propose a modified neural gas learning algorithm that we call structural neural gas (SNG) algorithm. The SNG is applied on a benchmark protein data set and its performance is compared with a well known algorithm from the literature (two stage clustering algorithm (TSCA)). The SNG algorithm is found to generate better building blocks compared to TSCA. We have also compared the performance of SNG algorithm with that of a recently reported Incremental Structural Mountain Clustering Method (ISMCM). In general, ISMCM is found to use more building blocks to yield results comparable to that of SNG algorithm. We demonstrate the superiority of SNG over TSCA both in terms of local-fit and global-fit errors using fragments of length five, six, and seven. We also use a graphical means for comparison of the performance of the two algorithms.