Prediction of coordination number and relative solvent accessibility in proteins
Prediction of coordination number and relative solvent accessibility in proteins
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DOI：
10.1002/prot.10069
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发表时间：
2002-05-01
影响因子：
2.9
通讯作者：
Casadio, R
中科院分区：
文献类型：
--
作者：
Pollastri, G;Baldi, P;Casadio, R
了解蛋白质中所有残基的配位数和相对溶剂可及性对于推导用于建模蛋白质折叠和蛋白质结构以及评分远程同源性搜索的约束至关重要。我们开发了双向递归神经网络架构的集合，以提高联系人和可访问性预测的最新水平，利用大量的策划数据和进化信息。系综用于区分残基接触或相对溶剂可及性的两种不同状态，高于或低于由残基分布或可及性截止值的平均值确定的阈值。对于配位数，系综实现的性能范围在70.6-73.9%之间，这取决于用于区分接触的半径（6埃-12埃）。这些性能代表了比基线统计预测值高出16-20%的增益，总是将氨基酸分配给最大的类别，并且比任何以前的方法高出4-7%。不同半径预测器的组合进一步提高了性能。对于相关的15-30%范围内的可访问性阈值，集合始终达到77%以上的性能，比基线预测高出10-16%，比其他现有的预测值高出几个百分点。对于这两个问题，我们量化的改进，由于进化信息的形式PSI-BLAST生成的配置文件在BLAST配置文件。预测程序以两个网络服务器CON pro和ACCPro的形式实现，可在http://promoter.ics上获得。uci.edu/BRNN-PRED/.
Knowing the coordination number and relative solvent accessibility of all the residues in a protein is crucial for deriving constraints useful in modeling protein folding and protein structure and in scoring remote homology searches. We develop ensembles of bidirectional recurrent neural network architectures to improve the state of the art in both contact and accessibility prediction, leveraging a large corpus of curated data together with evolutionary information. The ensembles are used to discriminate between two different states of residue contacts or relative solvent accessibility, higher or lower than a threshold determined by the average value of the residue distribution or the accessibility cutoff. For coordination numbers, the ensemble achieves performances ranging within 70.6-73.9% depending on the radius adopted to discriminate contacts (6Angstrom-12Angstrom). These performances represent gains of 16-20% over the baseline statistical predictor, always assigning an amino acid to the largest class, and are 4-7% better than any previous method. A combination of different radius predictors further improves performance. For accessibility thresholds in the relevant 15-30% range, the ensemble consistently achieves a performance above 77%, which is 10-16% above the baseline prediction and better than other existing predictors, by up to several percentage points. For both problems, we quantify the improvement due to evolutionary information in the form of PSI-BLAST-generated profiles over BLAST profiles. The prediction programs are implemented in the form of two web servers, CON pro and ACCpro, available at http://promoter.ics. uci.edu/BRNN-PRED/.