Prediction of protein hydration sites from sequence by modular neural networks
Prediction of protein hydration sites from sequence by modular neural networks
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DOI：
10.1093/protein/11.1.11
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发表时间：
1998-01-01
期刊：
影响因子：
--
通讯作者：
Wade, RC
中科院分区：
文献类型：
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作者：
Ehrlich, L;Reczko, M;Wade, RC
蛋白质的水合特性是其结构和功能的重要决定因素。在这里，模块化神经网络用于使用蛋白质序列信息预测有序水合位点。首先，使用两个独立的神经网络根据序列预测二级结构和溶剂可及性。这些预测与蛋白质序列一起用作预测残基、主链原子和侧链水合的网络的输入。这些网络接受蛋白质晶体结构的训练。通过添加二级结构和溶剂可及性的信息，改进了水化的预测，并且使用这些属性的实际值，可以以 0.43 的马修斯系数预测残留水化的准确度为 77%。然而，准确度为 60-70% 的预测属性数据导致使用实际值观察到的预测性能改进不到一半。包含属性信息允许在网络中使用较小的序列窗口来预测水合作用。它对主链原子水合位点预测的准确性的影响大于远侧链，对非极性残基的水合位点预测的影响大于极性残基的影响。该网络提供了对有序水位点的位置与蛋白质残基的结构和化学特征之间相互依赖性的洞察。
The hydration properties of a protein are important determinants of its structure and function. Here, modular neural networks are employed to predict ordered hydration sites using protein sequence information. First, secondary structure and solvent accessibility are predicted from sequence with two separate neural networks. These predictions are used as input together with protein sequences for networks predicting hydration of residues, backbone atoms and sidechains. These networks are trained with protein crystal structures. The prediction of hydration is improved by adding information on secondary structure and solvent accessibility and, using actual values of these properties, residue hydration can be predicted to 77% accuracy with a Matthews coefficient of 0.43. However, predicted property data with an accuracy of 60-70% result in less than half the improvement in predictive performance observed using the actual values. The inclusion of property information allows a smaller sequence window to be used in the networks to predict hydration. It has a greater impact on the accuracy of hydration site prediction for backbone atoms than far sidechains and for non-polar than polar residues. The networks provide insight into the mutual interdependencies between the location of ordered water sites and the structural and chemical characteristics of the protein residues.