Prediction of β-turns
Prediction of β-turns
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DOI：
10.1023/a:1022559300504
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发表时间：
1998-05-01
期刊：
影响因子：
--
通讯作者：
Chou, KC
中科院分区：
文献类型：
--
作者：
Cai, YD;Yu, H;Chou, KC
Kohonen的自组织模型，一种神经网络模型，被用于预测蛋白质中的β转变。在训练数据库中有455个β -turn四肽和3807个非β -turn四肽。测试数据库中110个β -turn四肽和30229个非β -turn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表明，在蛋白质折叠过程中，沿多肽链的残基偶联效应对反向旋转（如β -旋转）的形成很重要。
Kohonen's self-organization model, a neural network model, is applied to predict the beta-turns in proteins. There are 455 beta-turn tetrapeptides and 3807 non-beta-turn tetrapeptides in the training database. The rates of correct prediction for the 110 beta-turn tetrapeptides and 30,229 non-beta-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 beta-turns, during the process of protein folding.