Author(s)

Yate-Ching Yuan, Yingzi Shang, Hongzhi Li

Department of Molecular Medicine, City of Hope National Medical Center and Beckman Research Institute,Duarte, USA.
he School of Mathematics and Statistics, Hebei University of Economics and Business, Shijiazhuang, China.

This work demonstrates the so-called PCAC (Protein principal Component Analysis Clustering) method, which clusters large-scale decoy protein structures in protein structure prediction based on principal component analysis (PCA), is an ultra-fast and low-memory-requiring clustering method. It can be two orders of magnitude faster than the commonlyused pairwise rmsd-clustering (pRMSD) when enormous of decoys are involved. Instead of N(N – 1)/2 least-square fitting of rmsd calculations and N² memory units to store the pairwise rmsd values in pRMSD, PCAC only requires N rmsd calculations and N × P memory storage, where N is the number of structures to be clustered and P is the number of preserved eigenvectors. Furthermore, PCAC based on the covariance Cartesian matrix generates essentially the identical result as that from the reference rmsd-clustering (rRMSD). From a test of 41 protein decoy sets, when the eigenvectors that contribute a total of 90% eigenvalues are preserved, PCAC method reproduces the results of near-native selections from rRMSD.

Protein Structure Predicition; Protein Structure Cluster; Principal Component Analysis; Low-Momery-Requiring Clustering; Ultra-Fast Clustering

Y. Yuan, Y. Shang and H. Li, "A Low-Memory-Requiring and Fast Approach to Cluster Large-Scale Decoy Protein Structures," Open Journal of Biophysics, Vol. 2 No. 3, 2012, pp. 57-63. doi: 10.4236/ojbiphy.2012.23008.