一种用于预测跨膜β桶的高度精确的统计方法。
A highly accurate statistical approach for the prediction of transmembrane beta-barrels.
机构信息
Department of Biochemistry, Tulane University Health Sciences Center, New Orleans, LA 70112, USA.
出版信息
Bioinformatics. 2010 Aug 15;26(16):1965-74. doi: 10.1093/bioinformatics/btq308. Epub 2010 Jun 10.
MOTIVATION
Transmembrane beta-barrels (TMBBs) belong to a special structural class of proteins predominately found in the outer membranes of Gram-negative bacteria, mitochondria and chloroplasts. TMBBs are surface-exposed proteins that perform a variety of functions ranging from nutrient acquisition to osmotic regulation. These properties suggest that TMBBs have great potential for use in vaccine or drug therapy development. However, membrane proteins, such as TMBBs, are notoriously difficult to identify and characterize using traditional experimental approaches and current prediction methods are still unreliable.
RESULTS
A prediction method based on the physicochemical properties of experimentally characterized TMBB structures was developed to predict TMBB-encoding genes from genomic databases. The Freeman-Wimley prediction algorithm developed in this study has an accuracy of 99% and MCC of 0.748 when using the most efficient prediction criteria, which is better than any previously published algorithm.
AVAILABILITY
The MS Windows-compatible application is available for download at http://www.tulane.edu/~biochem/WW/apps.html.
动机
跨膜β-桶（TMBB）属于蛋白质的一个特殊结构类别，主要存在于革兰氏阴性菌、线粒体和叶绿体的外膜中。TMBB 是表面暴露的蛋白质，具有从营养物质获取到渗透压调节等多种功能。这些特性表明 TMBB 具有在疫苗或药物治疗开发中应用的巨大潜力。然而，膜蛋白（如 TMBB）很难用传统的实验方法来识别和描述，并且当前的预测方法仍然不可靠。
结果
本研究开发了一种基于经过实验表征的 TMBB 结构的理化性质的预测方法，用于从基因组数据库中预测 TMBB 编码基因。使用最有效的预测标准，本研究中开发的 Freeman-Wimley 预测算法的准确率为 99%，MCC 为 0.748，优于任何以前发表的算法。
可用性
适用于 MS Windows 的应用程序可在 http://www.tulane.edu/~biochem/WW/apps.html 下载。
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