NOVEL STATISTICAL ENERGY FUNCTIONS AND APPLICATIONS TO PROTEIN STRUCTURE PREDIC

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 蛋白质结构预测是理论生物物理学中最具挑战性的领域之一，在结构基因组学和合理的药物设计中起着至关重要的作用。我们最近开发了两个新的统计能量函数：OPUS-Ca，一个基于C-α的势函数，由七个典型的分子相互作用组成；以及OPUS-PSP，一个来自侧链堆积的取向相关的统计全原子势。我们希望将这两个势函数应用到OPUS中，这是一种概念上的新结构预测方法，它采用了多尺度、多层和自上而下的预测策略。OPUS结合了基于模板的方法和从头开始的方法来预测初级序列中的3D蛋白质结构。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Protein structure prediction is one of the most challenging areas in theoretical biophysics, and it plays an essential role in structural genomics and rational drug design. We have recently developed two novel statistical energy functions: OPUS-Ca, a C-alpha-based potential function composed of seven representative molecular interactions; and OPUS-PSP, an orientation-dependent statistical all-atom potential derived from side-chain packing. We wish to apply these two potential functions to OPUS, a conceptually new structure prediction method that employs multi-scale, multi-layer and top-down prediction strategies. OPUS combines template-based and de novo methods to predict 3D protein structures from primary sequences.