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M. Nanias, M. Chinchio, J. Pillardy, D. R. Ripoll and H. A. Scheraga, “Packing Helices in Proteins by Global Optimization of a Potential Energy Function,” Proceedings of National Academy of Sciences of the USA, Vol. 100, No. 4, 2003, pp. 1706-1710. doi:10.1073/pnas.252760199

has been cited by the following article:

• TITLE: A Low-Memory-Requiring and Fast Approach to Cluster Large-Scale Decoy Protein Structures

  AUTHORS: Yate-Ching Yuan, Yingzi Shang, Hongzhi Li

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

  JOURNAL NAME: Open Journal of Biophysics, Vol.2 No.3, July 27, 2012

  ABSTRACT: 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.