IMPROVEMENTS IN PROTEIN SECONDARY STRUCTURE PREDICTION BY AN ENHANCED NEURAL NETWORK
IMPROVEMENTS IN PROTEIN SECONDARY STRUCTURE PREDICTION BY AN ENHANCED NEURAL NETWORK
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DOI：
10.1016/0022-2836(90)90154-e
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发表时间：
1990-07-05
影响因子：
5.6
通讯作者：
LANGRIDGE, R
中科院分区：
文献类型：
--
作者：
KNELLER, DG;COHEN, FE;LANGRIDGE, R
计算神经网络最近被用于预测蛋白质序列和二级结构之间的映射。它们已经被证明足以确定这两个集合之间的一阶依赖关系，但是，到目前为止，还无法获得有助于确定二级结构的高阶信息。通过在输入序列中加入检测周期性的神经网络单元，我们略微提高了二级结构预测的精度。三级结构类的使用可以显著提高精度。对于所有-.alpha的类别，最佳情况预测为79%。蛋白质。提出了一种利用神经网络对结构假设进行验证和细化的方案。讨论了将学习算法应用于序列异质性代表性不足以及局部和全局影响未充分划分的数据集的操作困难。
Computational neural networks have recently been used to predict the mapping between protein sequence and secondary structure. They have proven adequate for determining the first-order dependence between these two sets, but have, until now, been unable to garner higher-order information that helps determine secondary structure. By adding neural network units that detect periodicities in the input sequence, we have modestly increased the secondary structure prediction accuracy. The use of tertiary structural class causes a marked increase in accuracy. The best case prediction was 79% for the class of all-.alpha. proteins. A scheme for employing neural networks to validate and refine structural hypotheses is proposed. The operational difficulties of applying a learning algorithm to a dataset where sequence heterogeneity is under-represented and where local and global effects are inadequately partitioned are discussed.