Generalized dead-end elimination algorithms make large-scale protein side-chain structure prediction tractable: implications for protein design and structural genomics.
Generalized dead-end elimination algorithms make large-scale protein side-chain structure prediction tractable: implications for protein design and structural genomics.
复制标题
广义的死端消除算法使大规模蛋白质侧链结构预测变得容易处理：对蛋白质设计和结构基因组学的影响。
DOI：
10.1006/jmbi.2000.4424
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发表时间：
2001
影响因子：
5.6
通讯作者：
Hellinga,HW
中科院分区：
文献类型：
--
作者：
Looger,LL;Hellinga,HW
The dead-end elimination (DEE) theorems are powerful tools for the combinatorial optimization of protein side-chain placement in protein design and homology modeling. In order to reach their full potential, the theorems must be extended to handle very hard problems. We present a suite of new algorithms within the DEE paradigm that significantly extend its range of convergence and reduce run time. As a demonstration, we show that a total protein design problem of 10115combinations, a hydrophobic core design problem of 10244combinations, and a side-chain placement problem of 101044combinations are solved in less than two weeks, a day and a half, and an hour of CPU time, respectively. This extends the range of the method by approximately 53, 144 and 851 log-units, respectively, using modest computational resources. Small to average-sized protein domains can now be designed automatically, and side-chain placement calculations can be solved for nearly all sizes of proteins and protein complexes in the growing field of structural genomics.
