title: Determining protein topology from skeletons of secondary structures.
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 7.6A 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.
authors.affiliation: Department of Bioengineering, Rice University, Houston, TX 77005, USA.
authors.forename: Yinghao
authors.initials: Y
authors.lastname: Wu
authors.forename: Mingzhi
authors.initials: M
authors.lastname: Chen
authors.forename: Mingyang
authors.initials: M
authors.lastname: Lu
authors.forename: Qinghua
authors.initials: Q
authors.lastname: Wang
authors.forename: Jianpeng
authors.initials: J
authors.lastname: Ma
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D020285:Cryoelectron Microscopy; D030562:Databases, Protein; D008958:Models, Molecular; D015233:Models, Statistical; D008962:Models, Theoretical; D009010:Monte Carlo Method; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*
chemical_list: D011506:Proteins