PROTEIN CLASSIFICATION BY STOCHASTIC MODELING AND OPTIMAL FILTERING OF AMINO-ACID-SEQUENCES
PROTEIN CLASSIFICATION BY STOCHASTIC MODELING AND OPTIMAL FILTERING OF AMINO-ACID-SEQUENCES
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DOI：
10.1016/0025-5564(94)90004-3
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发表时间：
1994-01-01
影响因子：
4.3
通讯作者：
SMITH, TF
中科院分区：
文献类型：
--
作者：
WHITE, JV;STULTZ, CM;SMITH, TF
根据蛋白质的氨基酸序列预测蛋白质的三级结构类别是一个信号处理问题。氨基酸序列被视为包含决定蛋白质结构类别的信号的“时间序列”。描述了一种用于为识别的单域蛋白质的结构类建立详细的随机信号模型的方法。我们解决了从一组候选者中确定该模型的问题，这是蛋白质整个氨基酸序列最可能的生成器。该解决方案采用了一种适合在并行计算机体系结构上实现的非线性最优滤波算法。以前的方法只能正确地将80%的单域蛋白质分类到三种非常广泛的结构类型中，而我们的方法在12个更详细的类别中达到了这个水平。
The prediction of a protein's tertiary structural class from its amino-acid sequence is formulated as a signal-processing problem. The amino-acid sequence is treated as a ''time series'' of symbols containing signals that determine the protein's structural class. A methodology is described for building detailed stochastic signal models for recognized structural classes of single-domain proteins. We solve the problem of determining that model, from a set of candidates, which is the most probable generator of a protein's entire amino-acid sequence. The solution employs a nonlinear, optimal filtering algorithm, which is suited for implementation on parallel computer architectures. Previous approaches have only been able to classify correctly 80% of single-domain proteins within three very broad structural types, while our approach achieves this level across twelve much more detailed classes.