TY - JOUR
T1 - Determining protein topology from skeletons of secondary structures
AU - Wu, Yinghao
AU - Chen, Mingzhi
AU - Lu, Mingyang
AU - Wang, Qinghua
AU - Ma, Jianpeng
N1 - Funding Information:
The authors gratefully acknowledge the support from the National Institutes of Health (R01-GM067801). M.C. and M.L. are supported partially by a predoctoral fellowship from the W. M. Keck Foundation of the Gulf Coast Consortia through the Keck Center for Computational and Structural Biology. J.M. is a recipient of the Award for Distinguished Young Scholars Abroad from the National Natural Science Foundation of China. The authors also thank an anonymous referee for his or her careful and critical review that improved the paper substantially.
PY - 2005/7/15
Y1 - 2005/7/15
N2 - We report a novel computational procedure for determining protein native topology, or fold, by defining loop connectivity based on skeletons of secondary structures that can usually be obtained from low to intermediate-resolution density maps. The procedure primarily involves a knowledge-based geometry filter followed by an energetics-based evaluation. It was tested on a large set of skeletons covering a wide range of protein architecture, including one modeled from an experimentally determined 7.6 Å cryo-electron microscopy (cryo-EM) density map. The results showed that the new procedure could effectively deduce protein folds without high-resolution structural data, a feature that could also be used to recognize native fold in structure prediction and to interpret data in fields like structure genomics. Most importantly, in the energetics-based evaluation, it was revealed that, despite the inevitable errors in the artificially constructed structures and limited accuracy of knowledge-based potential functions, the average energy of an ensemble of structures with slightly different configurations around the native skeleton is a much more robust parameter for marking native topology than the energy of individual structures in the ensemble. This result implies that, among all the possible topology candidates for a given skeleton, evolution has selected the native topology as the one that can accommodate the largest structural variations, not the one rigidly trapped in a deep, but narrow, conformational energy well.
AB - We report a novel computational procedure for determining protein native topology, or fold, by defining loop connectivity based on skeletons of secondary structures that can usually be obtained from low to intermediate-resolution density maps. The procedure primarily involves a knowledge-based geometry filter followed by an energetics-based evaluation. It was tested on a large set of skeletons covering a wide range of protein architecture, including one modeled from an experimentally determined 7.6 Å cryo-electron microscopy (cryo-EM) density map. The results showed that the new procedure could effectively deduce protein folds without high-resolution structural data, a feature that could also be used to recognize native fold in structure prediction and to interpret data in fields like structure genomics. Most importantly, in the energetics-based evaluation, it was revealed that, despite the inevitable errors in the artificially constructed structures and limited accuracy of knowledge-based potential functions, the average energy of an ensemble of structures with slightly different configurations around the native skeleton is a much more robust parameter for marking native topology than the energy of individual structures in the ensemble. This result implies that, among all the possible topology candidates for a given skeleton, evolution has selected the native topology as the one that can accommodate the largest structural variations, not the one rigidly trapped in a deep, but narrow, conformational energy well.
KW - Geometry scoring
KW - Protein fold
KW - Secondary structure assignment
KW - Secondary-structural skeleton
KW - Topology
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U2 - 10.1016/j.jmb.2005.04.064
DO - 10.1016/j.jmb.2005.04.064
M3 - Article
C2 - 15961102
AN - SCOPUS:20444490870
SN - 0022-2836
VL - 350
SP - 571
EP - 586
JO - Journal of Molecular Biology
JF - Journal of Molecular Biology
IS - 3
ER -