Hartmann Christoph, Antes Iris, Lengauer Thomas
Max-Planck-Institute for Informatics, Saarbrücken, Germany.
Protein Sci. 2007 Jul;16(7):1294-307. doi: 10.1110/ps.062658307. Epub 2007 Jun 13.
We introduce a new algorithm, IRECS (Iterative REduction of Conformational Space), for identifying ensembles of most probable side-chain conformations for homology modeling. On the basis of a given rotamer library, IRECS ranks all side-chain rotamers of a protein according to the probability with which each side chain adopts the respective rotamer conformation. This ranking enables the user to select small rotamer sets that are most likely to contain a near-native rotamer for each side chain. IRECS can therefore act as a fast heuristic alternative to the Dead-End-Elimination algorithm (DEE). In contrast to DEE, IRECS allows for the selection of rotamer subsets of arbitrary size, thus being able to define structure ensembles for a protein. We show that the selection of more than one rotamer per side chain is generally meaningful, since the selected rotamers represent the conformational space of flexible side chains. A knowledge-based statistical potential ROTA was constructed for the IRECS algorithm. The potential was optimized to discriminate between side-chain conformations of native and rotameric decoys of protein structures. By restricting the number of rotamers per side chain to one, IRECS can optimize side chains for a single conformation model. The average accuracy of IRECS for the chi1 and chi1+2 dihedral angles amounts to 84.7% and 71.6%, respectively, using a 40 degrees cutoff. When we compared IRECS with SCWRL and SCAP, the performance of IRECS was comparable to that of both methods. IRECS and the ROTA potential are available for download from the URL http://irecs.bioinf.mpi-inf.mpg.de.
我们引入了一种新算法IRECS（构象空间迭代约简法），用于为同源建模识别最可能的侧链构象集合。基于给定的旋转异构体库，IRECS根据每个侧链采用相应旋转异构体构象的概率，对蛋白质的所有侧链旋转异构体进行排序。这种排序使用户能够选择小的旋转异构体集合，这些集合最有可能包含每个侧链的近天然旋转异构体。因此，IRECS可以作为死端消除算法（DEE）的一种快速启发式替代方法。与DEE不同，IRECS允许选择任意大小的旋转异构体子集，从而能够定义蛋白质的结构集合。我们表明，每个侧链选择多个旋转异构体通常是有意义的，因为所选的旋转异构体代表了柔性侧链的构象空间。为IRECS算法构建了一种基于知识的统计势ROTA。对该势进行了优化，以区分蛋白质结构的天然和旋转异构体诱饵的侧链构象。通过将每个侧链的旋转异构体数量限制为一个，IRECS可以为单个构象模型优化侧链。使用40°的截止值时，IRECS对于chi1和chi1+2二面角的平均准确率分别为84.7%和71.6%。当我们将IRECS与SCWRL和SCAP进行比较时，IRECS的性能与这两种方法相当。IRECS和ROTA势可从网址http://irecs.bioinf.mpi-inf.mpg.de下载。
