Journal
JOURNAL OF MOLECULAR GRAPHICS & MODELLING
Volume 27, Issue 8, Pages 978-982Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jmgm.2008.12.006
Keywords
Computational mutagenesis; Tumor suppressor p53; Missense mutations; Distributed computing
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We applied our recently developed kinetic computational mutagenesis (KCM) approach [LT. Chong, W.C. Swope, J.W. Pitera, V.S. Pande, Kinetic computational alanine scanning: application to p53 oligomerization, J. Mol. Biol. 357 (3) (2006) 1039-1049] along with the MM-GBSA approach [J. Srinivasan, T.E. Cheatham 3rd, P. Cieplak, P.A. Kollman, D.A. Case, Continuum solvent studies of the stability of DNA, RNA, and phosphoramidate-DNA helices, J. Am. Chem. Soc. 120 (37) (1998) 9401 9409; P.A. Kollman, I. Massova, C.M. Reyes, B. Kuhn, S. Huo, L.T. Chong, M. Lee, T. Lee, Y. Duan, W. Wang, O. Donini, P. Cieplak,J. Srinivasan, D.A. Case, T.E. Cheatham 3rd., Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models, Acc. Chem. Res. 33 (12) (2000) 889-897] to evaluate the effects of all possible missense mutations on dimerization of the oligomerization domain (residues 326-355) of tumor suppressor p53. The true positive and true negative rates for KCM are comparable (within 5%) to those of MM-GBSA, although MM-GBSA is much less computationally intensive when it is applied to a single energy-minimized configuration per mutant dimer. The potential advantage of KCM is that it can be used to directly examine the kinetic effects of mutations. (C) 2008 Elsevier Inc. All rights reserved.
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