Domain size distributions can predict domain boundaries
Domain size distributions can predict domain boundaries
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DOI：
10.1093/bioinformatics/16.7.613
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发表时间：
2000-07-01
期刊：
影响因子：
5.8
通讯作者：
Bryant, SH
中科院分区：
文献类型：
--
作者：
Wheelan, SJ;Marchler-Bauer, A;Bryant, SH
动机：在3D结构数据库中观察到的蛋白质结构域的大小遵循令人惊讶的窄分布。此外，在超过80%的情况下，结构域由单链连续片段形成。这些观察结果意味着，一些选择的域边界上，否则未表征的序列更有可能比其他人，仅基于预测域的大小和段数。这个属性可能会被用来猜测蛋白质结构域boundaries.Results的位置：为了测试这种可能性，我们枚举假定的结构域边界，并计算其相对可能性下的概率模型，只考虑预测域的大小和段数。我们在交叉验证测试中使用已知3D结构的序列，最有可能的猜测是否与观察到的结构域结构一致。我们发现，域边界预测是令人惊讶的成功序列长达400个残基，以这种方式猜测域边界可以提高线程分析的灵敏度。
Motivation: The sizes of protein domains observed in the 3D-structure database follow a surprisingly narrow distribution. Structural domains are furthermore formed from a single-chain continuous segment in over 80% of instances. These observations imply that some choices of domain boundaries on an otherwise uncharacterized sequence are more likely than others, based solely on the size and segment number of predicted domains. This property might be used to guess the locations of protein domain boundaries.Results: To test this possibility we enumerate putative domain boundaries and calculate their relative likelihood under a probability model that considers only the size and segment number of predicted domains. We ask in a cross-validated test using sequences with known 3D structure, whether the most likely guesses agree with the observed domain structure. We find that domain boundary predictions are surprisingly successful for sequences up to 400 residues long and that guessing domain boundaries in this way can improve the sensitivity of threading analysis.