Yang Zheng Rong, Thomson Rebecca, McNeil Philip, Esnouf Robert M
School of Engineering and Computer Science, Exeter University, Exeter EX4 4QF, UK.
Bioinformatics. 2005 Aug 15;21(16):3369-76. doi: 10.1093/bioinformatics/bti534. Epub 2005 Jun 9.
Recent studies have found many proteins containing regions that do not form well-defined three-dimensional structures in their native states. The study and detection of such disordered regions is important both for understanding protein function and for facilitating structural analysis since disordered regions may affect solubility and/or crystallizability.
We have developed the regional order neural network (RONN) software as an application of our recently developed 'bio-basis function neural network' pattern recognition algorithm to the detection of natively disordered regions in proteins. The results of blind-testing a panel of nine disorder prediction tools (including RONN) against 80 protein sequences derived from the Protein Data Bank shows that, based on the probability excess measure, RONN performed the best.
最近的研究发现，许多蛋白质包含在其天然状态下不能形成明确三维结构的区域。对这些无序区域的研究和检测对于理解蛋白质功能以及促进结构分析都很重要，因为无序区域可能会影响溶解性和/或结晶性。
我们开发了区域有序神经网络（RONN）软件，这是我们最近开发的“生物基函数神经网络”模式识别算法在蛋白质天然无序区域检测中的应用。对来自蛋白质数据库的80个蛋白质序列进行的一组九种无序预测工具（包括RONN）的盲测结果表明，基于概率超额度量，RONN表现最佳。
