Chelliah Vijayalakshmi, Taylor William R
Division of Mathematical Biology, The National Institute for Medical Research, The Ridgeway, Mill Hill, London NW7 1AA, UK.
BMC Bioinformatics. 2008;9 Suppl 1(Suppl 1):S13. doi: 10.1186/1471-2105-9-S1-S13.
The prediction of protein structure can be facilitated by the use of constraints based on a knowledge of functional sites. Without this information it is still possible to predict which residues are likely to be part of a functional site and this information can be used to select model structures from a variety of alternatives that would correspond to a functional protein.
Using a large collection of protein-like decoy models, a score was devised that selected those with predicted functional site residues that formed a cluster. When tested on a variety of small alpha/beta/alpha type proteins, including enzymes and non-enzymes, those that corresponded to the native fold were ranked highly. This performance held also for a selection of larger alpha/beta/alpha proteins that played no part in the development of the method.
The use of predicted site positions provides a useful filter to discriminate native-like protein models from non-native models. The method can be applied to any collection of models and should provide a useful aid to all modelling methods from ab initio to homology based approaches.
基于功能位点的知识使用约束条件有助于蛋白质结构的预测。如果没有这些信息,仍然有可能预测哪些残基可能是功能位点的一部分,并且这些信息可用于从对应于功能蛋白质的各种替代模型结构中选择模型结构。
使用大量类似蛋白质的诱饵模型,设计了一种评分方法,该方法选择那些具有形成簇的预测功能位点残基的模型。在包括酶和非酶在内的各种小α/β/α型蛋白质上进行测试时,那些与天然折叠相对应的模型被高度排名。对于未参与该方法开发的一些较大的α/β/α蛋白质,这种性能也成立。
使用预测的位点位置提供了一个有用的筛选器,以区分天然样蛋白质模型和非天然模型。该方法可应用于任何模型集合,并且应该为从从头算到基于同源性的所有建模方法提供有用的帮助。