NEW LOCAL POTENTIAL FOR PROTEIN THREADING AND FOLDING

对蛋白质结构的精确计算预测将是一个非常重要的问题 了解与(或由)引起的疾病有关的有用工具 结构未知的蛋白质。三级结构预测 基于二级结构知识的算法已经被 描述了一段时间。最近发展起来的一种神经网络方法是 能够用75%预测三种状态之一的二级结构 百分比准确率。此外，这种方法产生了估计的 在每个残基上找到螺旋、薄片或线圈的概率 蛋白质，它可以用来推导出预测的脊椎集合 二面角。使用准化学近似，这些预测 可以利用二面角频率来创建伪势，该赝势 包括主干上所有顺序局部相互作用的影响 二面角。当与代表非 局域相互作用--蛋白质折叠的有用能量函数 可以创建模拟和结构预测。潜力 该模型的应用包括更精确的二级和三级 可以用当前方法获得的结构预测，以及 对蛋白质折叠过程的可能洞察。

Accurate computational predictions of protein structure would be a very useful tool for understanding diseases related to (or caused by) proteins with unknown structure. Tertiary structure prediction algorithms based on a knowledge of secondary structure have been described for some time. A recently developed neural network method is able to predict secondary structure in one of three states with 75 percent accuracy. Furthermore, this method produces estimated probabilities of finding helix, sheet, or coil at every residue in a protein, which can be used to derive predicated ensembles of backbone dihedral angles. Using the quasichemical approximation, these predicted dihedral angle frequencies can be sued to create a pseudopotential which includes the effect of all sequentially local interactions on backbone dihedral angles. When combined with pseudopotentials representing non- local interactions, a useful energy function for protein folding simulations and structure prediction could be created. Potential applications of this model include more accurate secondary and tertiary structure prediction that can be obtained with current methods, and possible insight into the process of protein folding.