Peng Hung-Pin, Yang An-Suei
Genomics Research Center, Academia Sinica. 128 Academia Road, Section 2, Nankang District, Taipei 115, Taiwan, ROC.
Bioinformatics. 2007 Nov 1;23(21):2836-42. doi: 10.1093/bioinformatics/btm456. Epub 2007 Sep 7.
As protein structure database expands, protein loop modeling remains an important and yet challenging problem. Knowledge-based protein loop prediction methods have met with two challenges in methodology development: (1) loop boundaries in protein structures are frequently problematic in constructing length-dependent loop databases for protein loop predictions; (2) knowledge-based modeling of loops of unknown structure requires both aligning a query loop sequence to loop templates and ranking the loop sequence-template matches.
We developed a knowledge-based loop prediction method that circumvents the need of constructing hierarchically clustered length-dependent loop libraries. The method first predicts local structural fragments of a query loop sequence and then structurally aligns the predicted structural fragments to a set of non-redundant loop structural templates regardless of the loop length. The sequence-template alignments are then quantitatively evaluated with an artificial neural network model trained on a set of predictions with known outcomes. Prediction accuracy benchmarks indicated that the novel procedure provided an alternative approach overcoming the challenges of knowledge-based loop prediction.
随着蛋白质结构数据库的扩展，蛋白质环建模仍然是一个重要且具有挑战性的问题。基于知识的蛋白质环预测方法在方法开发中面临两个挑战：（1）蛋白质结构中的环边界在构建用于蛋白质环预测的长度依赖性环数据库时经常出现问题；（2）对未知结构的环进行基于知识的建模既需要将查询环序列与环模板进行比对，又需要对环序列 - 模板匹配进行排序。
我们开发了一种基于知识的环预测方法，该方法无需构建分层聚类的长度依赖性环库。该方法首先预测查询环序列的局部结构片段，然后将预测的结构片段与一组非冗余的环结构模板进行结构比对，而不考虑环的长度。然后，使用在一组已知结果的预测上训练的人工神经网络模型对序列 - 模板比对进行定量评估。预测准确性基准表明，该新方法提供了一种克服基于知识的环预测挑战的替代方法。
