Protein secondary structure prediction based on position-specific scoring matrices
Protein secondary structure prediction based on position-specific scoring matrices
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DOI：
10.1006/jmbi.1999.3091
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发表时间：
1999-09-17
影响因子：
5.6
通讯作者：
Jones, DT
中科院分区：
文献类型：
--
作者：
Jones, DT
一个两阶段的神经网络已被用来预测蛋白质二级结构的基础上产生的位置特异性评分矩阵PSI-BLAST。尽管使用的方法简单方便，结果被发现是上级的其他方法，包括流行的PHD方法，根据我们自己的基准测试结果和结果，从最近的关键评估技术的蛋白质结构预测实验（CASP 3），该方法进行了评估，严格的盲测。使用基于一组187个独特折叠的新测试集，以及基于结构相似性标准而不是先前使用的序列相似性标准的三向交叉验证（在测试集和训练集中没有类似的折叠）这里提出的方法（PSIPRED）实现了76.5%到78.3%的平均Q（3）得分这取决于所使用的观察到的二级结构的精确定义，这是迄今为止任何方法的最高公开得分。鉴于该方法在CASP 3中的成功，有理由相信本文所述的评价总体上公平地表明了该方法的性能。(C)北京：科学出版社.
A two-stage neural network has been used to predict protein secondary structure based on the position specific scoring matrices generated by PSI-BLAST. Despite the simplicity and convenience of the approach used, the results are found to be superior to those produced by other methods, including the popular PHD method according to our own benchmarking results and the results from the recent Critical Assessment of Techniques for Protein Structure Prediction experiment (CASP3), where the method was evaluated by stringent blind testing. Using a new testing set based on a set of 187 unique folds, and three-way cross-validation based on structural similarity criteria rather than sequence similarity criteria used previously (no similar folds were present in both the testing and training sets) the method presented here (PSIPRED) achieved an average Q(3) score of between 76.5% to 78.3% depending on the precise definition of observed secondary structure used, which is the highest published score for any method to date. Given the success of the method in CASP3, it is reasonable to be confident that the evaluation presented here gives a fair indication of the performance of the method in general. (C) 1999 Academic Press.