A knowledge-driven approach for crystallographic protein model completion.
A knowledge-driven approach for crystallographic protein model completion.
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DOI：
10.1107/s0907444908001558
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发表时间：
2008-04
期刊：
影响因子：
--
通讯作者：
Perrakis A
中科院分区：
文献类型：
--
作者：
Joosten K;Cohen SX;Emsley P;Mooij W;Lamzin VS;Perrakis A
A novel method that uses the conformational distribution of Cα atoms in known structures is used to build short missing regions (‘loops’) in protein models. An initial tree of possible loop paths is pruned according to structural and electron-density criteria and the most likely loop conformation(s) are selected and built. One of the most cumbersome and time-demanding tasks in completing a protein model is building short missing regions or ‘loops’. A method is presented that uses structural and electron-density information to build the most likely conformations of such loops. Using the distribution of angles and dihedral angles in pentapeptides as the driving parameters, a set of possible conformations for the Cα backbone of loops was generated. The most likely candidate is then selected in a hierarchical manner: new and stronger restraints are added while the loop is built. The weight of the electron-density correlation relative to geometrical considerations is gradually increased until the most likely loop is selected on map correlation alone. To conclude, the loop is refined against the electron density in real space. This is started by using structural information to trace a set of models for the Cα backbone of the loop. Only in later steps of the algorithm is the electron-density correlation used as a criterion to select the loop(s). Thus, this method is more robust in low-density regions than an approach using density as a primary criterion. The algorithm is implemented in a loop-building program, Loopy, which can be used either alone or as part of an automatic building cycle. Loopy can build loops of up to 14 residues in length within a couple of minutes. The average root-mean-square deviation of the Cα atoms in the loops built during validation was less than 0.4 Å. When implemented in the context of automated model building in ARP/wARP, Loopy can increase the completeness of the built models.
