Prediction of β-turns
Prediction of β-turns
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DOI：
10.1111/j.1399-3011.1997.tb00608.x
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发表时间：
2009-01
期刊：
影响因子：
--
通讯作者：
Kou-Chen Chou
中科院分区：
文献类型：
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作者：
Kou-Chen Chou
Kohonen 的自组织模型是一种神经网络模型，用于预测蛋白质中的 β 转角。训练数据库中有455条β转角四肽和3807条非β转角四肽。测试数据库中110条β转角四肽和30229条非β转角四肽的预测正确率分别为81.8%和90.7%。神经网络模型的高质量预测意味着沿着多肽链的残基耦合效应对于蛋白质折叠过程中反转转角（例如β转角）的形成非常重要。
Kohonen's self-organization model, a neural network model, is applied to predict the β-turns in proteins. There are 455 β-turn tetrapeptides and 3807 non-β-turn tetrapeptides in the training database. The rates of correct prediction for the 110 β-turn tetrapeptides and 30,229 non-β-turn tetrapeptides in the testing database are 81.8% and 90.7%, respectively. The high quality of prediction of neural network model implies that the residue-coupled effect along a polypeptide chain is important for the formation of reversal turns, such as β-turns, during the process of protein folding.