NEW LOCAL POTENTIAL FOR PROTEIN THREADING AND FOLDING

精确的计算预测蛋白质结构将是一个非常重要的 了解与疾病有关（或由疾病引起）的有用工具 未知结构的蛋白质 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。 最近开发的神经网络方法是 能够预测二级结构在三个国家之一，75 %的准确度。 此外，该方法产生估计 发现螺旋，片，或线圈在每个残基的概率 蛋白质，其可用于衍生主链的预测集合 二面角 使用准化学近似，这些预测 二面角频率可以用来产生赝势， 包括所有连续局部相互作用对骨架的影响 二面角 当与代表非- 局部相互作用，蛋白质折叠的有用能量函数 可以创建模拟和结构预测。 潜在 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潜在洞察。

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.