Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 571-586 |
| Number of pages | 16 |
| Journal | Journal of Molecular Biology |
| Volume | 350 |
| Issue number | 3 |
| DOIs | |
| State | Published - Jul 15 2005 |
| Externally published | Yes |
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Keywords
- Geometry scoring
- Protein fold
- Secondary structure assignment
- Secondary-structural skeleton
- Topology
ASJC Scopus subject areas
- Virology
Cite this
Determining protein topology from skeletons of secondary structures. / Wu, Yinghao; Chen, Mingzhi; Lu, Mingyang; Wang, Qinghua; Ma, Jianpeng.
In: Journal of Molecular Biology, Vol. 350, No. 3, 15.07.2005, p. 571-586.Research output: Contribution to journal › Article
}
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
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
UR - http://www.scopus.com/inward/record.url?scp=20444490870&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=20444490870&partnerID=8YFLogxK
U2 - 10.1016/j.jmb.2005.04.064
DO - 10.1016/j.jmb.2005.04.064
M3 - Article
C2 - 15961102
AN - SCOPUS:20444490870
VL - 350
SP - 571
EP - 586
JO - Journal of Molecular Biology
JF - Journal of Molecular Biology
SN - 0022-2836
IS - 3
ER -