NOVEL STATISTICAL ENERGY FUNCTIONS AND APPLICATIONS TO PROTEIN STRUCTURE PREDIC

该子项目是利用 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得主要资金， 因此可以在其他CRISP条目中表示。列出的机构是 中心，不一定是研究者的机构。 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，它在结构基因组学和合理药物设计中起着至关重要的作用。我们最近开发了两种新的统计能量函数：OPUS-Ca，一个基于C-α的势函数，由七个代表性的分子相互作用组成; OPUS-PSP，一个来自侧链堆积的取向依赖的统计全原子势。我们希望将这两个潜在的功能OPUS，一个概念上新的结构预测方法，采用多尺度，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方法，从一级序列预测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.