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M. P. Jacobson, D. L. Pincus, C. S. Rapp, T. J. F. Day, B. Honig, D. E. Shaw and R. A. Friesner, “A Hierarchical Approach to All-Atom Protein Loop Prediction,” Proteins: Structure, Function, and Bioinformatics, Vol. 55, No. 2, 2004, pp. 351-367. doi:10.1002/prot.10613

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.