Disulfide connectivity prediction using secondary structure information and diresidue frequencies
Disulfide connectivity prediction using secondary structure information and diresidue frequencies
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DOI：
10.1093/bioinformatics/bti328
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发表时间：
2005-05-15
期刊：
影响因子：
5.8
通讯作者：
Clote, P
中科院分区：
文献类型：
--
作者：
Ferrè, F;Clote, P
动机：我们描述了一个独立的算法来预测二硫键的合作伙伴在一个蛋白质中只给出的氨基酸序列，使用一种新的神经网络架构（双残基神经网络），并给定输入的对称侧翼区的N-末端和C-末端半胱氨酸增强残基二级结构（螺旋，线圈，片）以及进化信息。该方法的动机是通过观察游离半胱氨酸和半胱氨酸的二级结构偏好的偏差，以及通过使用双残基位置特异性评分矩阵获得的有希望的初步结果。通过4重交叉验证的接收器操作特征曲线进行校准，我们对二级结构的调节使我们的新型双残基神经网络能够表现良好，并且在某些情况下比当前最先进的方法更好。当预测二级结构而不是从三维蛋白质结构中推导时，可以看到性能略有下降。
Motivation: We describe a stand-alone algorithm to predict disulfide bond partners in a protein given only the amino acid sequence, using a novel neural network architecture (the diresidue neural network), and given input of symmetric flanking regions of N-terminus and C-terminus half-cystines augmented with residue secondary structure (helix, coil, sheet) as well as evolutionary information. The approach is motivated by the observation of a bias in the secondary structure preferences of free cysteines and half-cystines, and by promising preliminary results we obtained using diresidue position-specific scoring matrices.Results: As calibrated by receiver operating characteristic curves from 4-fold cross-validation, our conditioning on secondary structure allows our novel diresidue neural network to perform as well as, and in some cases better than, the current state-of-the-art method. A slight drop in performance is seen when secondary structure is predicted rather than being derived from three-dimensional protein structures.