Non-Negative Matrix Factorization for Selection of Near-Native Protein Tertiary Structures

Identifying biologically-active protein structure(s) from an ensemble of computed three-dimensional structures is a major challenge. Clustering-based methods are time-consuming and often under perform on structure datasets that are highly imbalanced. Energy landscape-based methods improve performance over imbalanced datasets but incur significant time costs. In this paper we propose a novel method based on non-negative matrix factorization. The method outperforms energy landscape-based clustering methods, addressing both time costs and challenges with imbalanced structure datasets.