Fernandez-Fuentes Narcis, Zhai Jun, Fiser András
Department of Biochemistry and Seaver Foundation Center for Bioinformatics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.
Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W173-6. doi: 10.1093/nar/gkl113.
ArchPRED server (http://www.fiserlab.org/servers/archpred) implements a novel fragment-search based method for predicting loop conformations. The inputs to the server are the atomic coordinates of the query protein and the position of the loop. The algorithm selects candidate loop fragments from a regularly updated loop library (Search Space) by matching the length, the types of bracing secondary structures of the query and by satisfying the geometrical restraints imposed by the stem residues. Subsequently, candidate loops are inserted in the query protein framework where their side chains are rebuilt and their fit is assessed by the root mean square deviation (r.m.s.d.) of stem regions and by the number of rigid body clashes with the environment. In the final step remaining candidate loops are ranked by a Z-score that combines information on sequence similarity and fit of predicted and observed [/psi] main chain dihedral angle propensities. The final loop conformation is built in the protein structure and annealed in the environment using conjugate gradient minimization. The prediction method was benchmarked on artificially prepared search datasets where all trivial sequence similarities on the SCOP superfamily level were removed. Under these conditions it was possible to predict loops of length 4, 8 and 12 with coverage of 98, 78 and 28% with at least of 0.22, 1.38 and 2.47 A of r.m.s.d. accuracy, respectively. In a head to head comparison on loops extracted from freshly deposited new protein folds the current method outperformed in a approximately 5:1 ratio an earlier developed database search method.
ArchPRED服务器(http://www.fiserlab.org/servers/archpred)实现了一种基于片段搜索的新颖方法来预测环的构象。该服务器的输入是查询蛋白的原子坐标和环的位置。该算法通过匹配长度、查询的支撑二级结构类型,并满足茎残基施加的几何约束,从定期更新的环库(搜索空间)中选择候选环片段。随后,将候选环插入查询蛋白框架中,在那里重建它们的侧链,并通过茎区域的均方根偏差(r.m.s.d.)以及与环境的刚体冲突数量来评估它们的契合度。在最后一步,剩余的候选环通过一个Z分数进行排名,该Z分数结合了序列相似性信息以及预测和观察到的[/psi]主链二面角倾向的契合度信息。最终的环构象在蛋白质结构中构建,并使用共轭梯度最小化在环境中进行退火处理。该预测方法在人工制备的搜索数据集上进行了基准测试,其中去除了得SCOP超家族水平上所有微不足道的序列相似性。在这些条件下,能够分别预测长度为4、8和12的环,覆盖率分别为98%、78%和28%,r.m.s.d. 精度至少为0.22 Å、1.38 Å和2.47 Å。在对从新沉积的新蛋白质折叠中提取的环进行的直接比较中,当前方法以大约5:1的比例优于早期开发的数据库搜索方法。