Combining sequence and structural profiles for protein solvent accessibility prediction.
Combining sequence and structural profiles for protein solvent accessibility prediction.
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DOI：
10.1142/9781848162648_0017
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
R. Bondugula;Dong Xu
中科院分区：
文献类型：
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作者：
R. Bondugula;Dong Xu
溶剂可及性是蛋白质的重要结构特征。我们提出了一种用于溶剂可及性预测的新方法，该方法更有效地使用已知的结构和序列信息。我们首先使用模糊均值算子根据与查询蛋白质具有相似序列的已知结构片段的溶剂可及性来估计查询蛋白质的相对溶剂可及性。然后，我们使用神经网络整合估计的溶剂可及性和查询蛋白质的位置特异性评分矩阵。我们在由 3386 个非冗余蛋白质组成的大数据集上测试了我们的方法。与其他方法的比较表明，我们的方法的预测精度略有提高。当新数据可用时，生成的系统不需要重新训练。我们将我们的方法合并到 MUPRED 系统中，该系统可作为 Web 服务器使用，网址为 http://digbio.missouri.edu/mupred。
Solvent accessibility is an important structural feature for a protein. We propose a new method for solvent accessibility prediction that uses known structure and sequence information more efficiently. We first estimate the relative solvent accessibility of the query protein using fuzzy mean operator from the solvent accessibilities of known structure fragments that have similar sequences to the query protein. We then integrate the estimated solvent accessibility and the position specific scoring matrix of the query protein using a neural network. We tested our method on a large data set consisting of 3386 non-redundant proteins. The comparison with other methods show slightly improved prediction accuracies with our method. The resulting system does need not be re-trained when new data is available. We incorporated our method into the MUPRED system, which is available as a web server at http://digbio.missouri.edu/mupred.