Premium
Density‐cluster NMA: A new protein decomposition technique for coarse‐grained normal mode analysis
Author(s) -
Demerdash Omar N. A.,
Mitchell Julie C.
Publication year - 2012
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.24072
Subject(s) - hessian matrix , block (permutation group theory) , cluster (spacecraft) , chemistry , cluster analysis , algorithm , normal mode , biological system , physics , computer science , mathematics , acoustics , combinatorics , artificial intelligence , biology , vibration , programming language
Normal mode analysis has emerged as a useful technique for investigating protein motions on long time scales. This is largely due to the advent of coarse‐graining techniques, particularly Hooke's Law‐based potentials and the rotational–translational blocking (RTB) method for reducing the size of the force‐constant matrix, the Hessian. Here we present a new method for domain decomposition for use in RTB that is based on hierarchical clustering of atomic density gradients, which we call Density‐Cluster RTB (DCRTB). The method reduces the number of degrees of freedom by 85–90% compared with the standard blocking approaches. We compared the normal modes from DCRTB against standard RTB using 1–4 residues in sequence in a single block, with good agreement between the two methods. We also show that Density‐Cluster RTB and standard RTB perform well in capturing the experimentally determined direction of conformational change. Significantly, we report superior correlation of DCRTB with B ‐factors compared with 1–4 residue per block RTB. Finally, we show significant reduction in computational cost for Density‐Cluster RTB that is nearly 100‐fold for many examples. Proteins 2012; © 2012 Wiley Periodicals, Inc.