FINDSITE-metal: integrating evolutionary information and machine learning for structure-based metal-binding site prediction at the proteome level.
FINDSITE-metal: integrating evolutionary information and machine learning for structure-based metal-binding site prediction at the proteome level.
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DOI：
10.1002/prot.22913
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发表时间：
2011-03
影响因子：
2.9
通讯作者：
Skolnick, Jeffrey
中科院分区：
文献类型：
--
作者：
Brylinski, Michal;Skolnick, Jeffrey
关键词：
基因序列的快速积累，其中许多是假设的蛋白质与未知的功能，刺激了精确的计算工具的蛋白质功能预测与进化/结构为基础的方法显示出相当大的希望的发展。在本文中，我们提出了FINDSITE金属，一种新的线程为基础的方法，专门设计用于检测金属结合位点的蛋白质结构模型。使用不同质量的蛋白质结构的综合基准表明，弱同源蛋白质模型提供了足够的结构信息，相当准确的注释FINDSITE金属。将结构/进化信息与机器学习相结合，可以得到高度准确的金属结合注释;对于TASSER构建的蛋白质模型，其天然结构的平均Cα RMSD为8.9 μ m，前五个预测金属位置中最好的59.5%（71.9%）与晶体结构中的结合金属在4 μ m（8 μ m）以内。对于大多数目标，检测到多个金属结合位点，其中最佳预测结合位点分别在65.6%和83.1%的情况下位于第1级和前2级。此外，对于铁、铜、锌、钙和镁离子，结合金属可以以通常70- 90%的高准确度预测。FINDSITE metal还提供了一组置信度指数，帮助评估预测的可靠性。最后，我们描述了FINDSITE-金属在蛋白质组范围内的应用，该应用量化了人类蛋白质组的金属结合互补物。FINDSITE-metal可在http://cssb.biology.gatech.edu/findsite-metal/上免费向学术界提供。
The rapid accumulation of gene sequences, many of which are hypothetical proteins with unknown function, has stimulated the development of accurate computational tools for protein function prediction with evolution/structure-based approaches showing considerable promise. In this paper, we present FINDSITE-metal, a new threading-based method designed specifically to detect metal binding sites in modeled protein structures. Comprehensive benchmarks using different quality protein structures show that weakly homologous protein models provide sufficient structural information for quite accurate annotation by FINDSITE-metal. Combining structure/evolutionary information with machine learning results in highly accurate metal binding annotations; for protein models constructed by TASSER, whose average Cα RMSD from the native structure is 8.9 Å, 59.5% (71.9%) of the best of top five predicted metal locations are within 4 Å (8 Å) from a bound metal in the crystal structure. For most of the targets, multiple metal binding sites are detected with the best predicted binding site at rank 1 and within the top 2 ranks in 65.6% and 83.1% of the cases, respectively. Furthermore, for iron, copper, zinc, calcium and magnesium ions, the binding metal can be predicted with high, typically 70-90%, accuracy. FINDSITE-metal also provides a set of confidence indexes that help assess the reliability of predictions. Finally, we describe the proteome-wide application of FINDSITE-metal that quantifies the metal binding complement of the human proteome. FINDSITE-metal is freely available to the academic community at http://cssb.biology.gatech.edu/findsite-metal/.
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