GeMMA: functional subfamily classification within superfamilies of predicted protein structural domains.
GeMMA: functional subfamily classification within superfamilies of predicted protein structural domains.
复制标题
DOI：
10.1093/nar/gkp1049
复制
发表时间：
2010-01
影响因子：
14.9
通讯作者：
Orengo C
中科院分区：
文献类型：
--
作者：
Lee DA;Rentzsch R;Orengo C
GeMMA（Genome Modelling and Model Annotation）是一种新的蛋白质功能亚家族自动分类方法。GeMMA的一个主要优势是它能够对具有数万个成员的非常大且多样化的超家族进行亚分类，而不需要初始的多序列比对。它的性能被证明是可比的既定的高性能方法SCI-PHY。GeMMA遵循凝聚聚类协议，该协议使用现有软件进行敏感和准确的多序列比对和轮廓-轮廓比较。所产生的亚家族被证明是等同的质量是否使用整个蛋白质序列或只是组件预测的结构域的序列。一个更快的，启发式版本的GeMMA，也使用分布式计算，以保持原始实现的性能水平。使用的GeMMA，以增加功能多样的Pfam家族的功能注释覆盖率的证明。它进一步表明，如何GeMMA集群可以帮助预测的影响，实验确定蛋白质结构域结构的比较蛋白质建模覆盖率，在结构基因组学的背景下。
GeMMA (Genome Modelling and Model Annotation) is a new approach to automatic functional subfamily classification within families and superfamilies of protein sequences. A major advantage of GeMMA is its ability to subclassify very large and diverse superfamilies with tens of thousands of members, without the need for an initial multiple sequence alignment. Its performance is shown to be comparable to the established high-performance method SCI-PHY. GeMMA follows an agglomerative clustering protocol that uses existing software for sensitive and accurate multiple sequence alignment and profile–profile comparison. The produced subfamilies are shown to be equivalent in quality whether whole protein sequences are used or just the sequences of component predicted structural domains. A faster, heuristic version of GeMMA that also uses distributed computing is shown to maintain the performance levels of the original implementation. The use of GeMMA to increase the functional annotation coverage of functionally diverse Pfam families is demonstrated. It is further shown how GeMMA clusters can help to predict the impact of experimentally determining a protein domain structure on comparative protein modelling coverage, in the context of structural genomics.
登录
查看更多内容
影响因子：
12.3
作者：
Brown, Shoshana D;Gerlt, John A;Seffernick, Jennifer L;Babbitt, Patricia C
通讯作者：
Babbitt, Patricia C
影响因子：
4.3
作者：
Engelhardt BE;Jordan MI;Muratore KE;Brenner SE
通讯作者：
Brenner SE
影响因子：
14.9
作者：
Bru C;Courcelle E;Carrère S;Beausse Y;Dalmar S;Kahn D
通讯作者：
Kahn D
影响因子：
5.6
作者：
Addou, Sarah;Rentzsch, Robert;Orengo, Christine A.
通讯作者：
Orengo, Christine A.
影响因子：
14.9
作者：
John, B;Sali, A
通讯作者：
Sali, A