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首页> 美国卫生研究院文献>Protein Science : A Publication of the Protein Society >DDOMAIN: Dividing structures into domains using a normalized domain–domain interaction profile
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DDOMAIN: Dividing structures into domains using a normalized domain–domain interaction profile

机译：域：使用规范化的域-域交互配置文件将结构划分为域

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摘要

Dividing protein structures into domains is proven useful for more accurate structural and functional characterization of proteins. Here, we develop a method, called DDOMAIN, that divides structure into DOMAINs using a normalized contact-based domain–domain interaction profile. Results of DDOMAIN are compared to AUTHORS annotations (domain definitions are given by the authors who solved protein structures), as well as to popular SCOP and CATH annotations by human experts and automatic programs. DDOMAIN's automatic annotations are most consistent with the AUTHORS annotations (90% agreement in number of domains and 88% agreement in both number of domains and at least 85% overlap in domain assignment of residues) if its three adjustable parameters are trained by the AUTHORS annotations. By comparison, the agreement is 83% (81% with at least 85% overlap criterion) between SCOP-trained DDOMAIN and SCOP annotations and 77% (73%) between CATH-trained DDOMAIN and CATH annotations. The agreement between DDOMAIN and AUTHORS annotations goes beyond single-domain proteins (97%, 82%, and 56% for single-, two-, and three-domain proteins, respectively). For an “easy” data set of proteins whose CATH and SCOP annotations agree with each other in number of domains, the agreement is 90% (89%) between “easy-set”-trained DDOMAIN and CATH/SCOP annotations. The consistency between SCOP-trained DDOMAIN and SCOP annotations is superior to two other recently developed, SCOP-trained, automatic methods PDP (protein domain parser), and DomainParser 2. We also tested a simple consensus method made of PDP, DomainParser 2, and DDOMAIN and a different version of DDOMAIN based on a more sophisticated statistical energy function. The DDOMAIN server and its executable are available in the services section on .
机译：事实证明，将蛋白质结构分成多个域对于蛋白质的更准确的结构和功能表征很有用。在这里，我们开发了一种称为DDOMAIN的方法，该方法使用归一化的基于接触的域-域交互配置文件将结构划分为DOMAIN。将DDOMAIN的结果与AUTHORS注释（域定义由解决蛋白质结构的作者提供）进行比较，并与人类专家和自动程序与流行的SCOP和CATH注释进行比较。如果DDOMAIN的三个可调整参数由AUTHORS批注训练，则DDOMAIN的自动批注与AUTHORS批注最一致（在域数中90％一致，在两个域数中88％一致，并且残基的域分配中至少85％重叠） 。相比之下，经过SCOP训练的DDOMAIN和SCOP注释之间的一致性为83％（81％，重叠标准至少为85％），而经过CATH训练的DDOMAIN和CATH注释之间的一致性为77％（73％）。 DDOMAIN和AUTHORS注释之间的协议超出了单域蛋白（单域，二域和三域蛋白分别为97％，82％和56％）。对于其CATH和SCOP注释在结构域数目上彼此一致的“简单”蛋白质数据集，“易设置”训练的DDOMAIN和CATH / SCOP注释之间的一致性为90％（89％）。经过SCOP训练的DDOMAIN和SCOP注释之间的一致性优于其他两个最近开发的，经过SCOP训练的自动方法PDP（蛋白质域解析器）和DomainParser2。我们还测试了由PDP，DomainParser 2和DDOMAIN和基于更复杂的统计能量函数的DDOMAIN的不同版本。 DDOMAIN服务器及其可执行文件在上的服务部分中可用。

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