Márquez-Chamorro Alfonso E, Aguilar-Ruiz Jesús S
School of Engineering, Pablo de Olavide University, Seville, Spain.
Evol Bioinform Online. 2015 Oct 20;11:223-9. doi: 10.4137/EBO.S25349. eCollection 2015.
The problem of protein structure prediction (PSP) is one of the main challenges in structural bioinformatics. To tackle this problem, PSP can be divided into several subproblems. One of these subproblems is the prediction of disulfide bonds. The disulfide connectivity prediction problem consists in identifying which nonadjacent cysteines would be cross-linked from all possible candidates. Determining the disulfide bond connectivity between the cysteines of a protein is desirable as a previous step of the 3D PSP, as the protein conformational search space is highly reduced. The most representative soft computing approaches for the disulfide bonds connectivity prediction problem of the last decade are summarized in this paper. Certain aspects, such as the different methodologies based on soft computing approaches (artificial neural network or support vector machine) or features of the algorithms, are used for the classification of these methods.
蛋白质结构预测（PSP）问题是结构生物信息学中的主要挑战之一。为了解决这个问题，PSP可分为几个子问题。其中一个子问题是二硫键预测。二硫键连接性预测问题在于从所有可能的候选者中识别出哪些不相邻的半胱氨酸会发生交联。确定蛋白质中半胱氨酸之间的二硫键连接性作为三维PSP的前一步是很有必要的，因为蛋白质构象搜索空间会大大缩小。本文总结了过去十年中用于二硫键连接性预测问题的最具代表性的软计算方法。基于软计算方法（人工神经网络或支持向量机）的不同方法或算法特征等某些方面被用于这些方法的分类。
