THEORETICAL PROTEIN STRUCTURE PREDICTION BY HOMOLGY

描述：阿巴吉安博士之前已经证明了一种改进的 能量函数，结合了一个强大的构象搜索程序， 能准确预测小分子的详细(全原子)结构 和多肽片段。建议将这些预付款应用于 开发一种全面而准确的同源预测程序。 该方法在同源建模测试用例中的应用表明，该方法具有较好的通用性 允许在理想化条件下成功预测环和侧链 条件。然而，变形但僵硬的结构环境 建模的环路或其他主干片段被发现是最大的 成功进行同源建模的障碍。现建议：将 允许在回路的环境中重新排列的回路建模方法； 自动确定这种“软”环境；要应用 多肽末端、可移动二级结构元素的预测方法 和多肽平面取向误差；测试和提炼所有组分 该方法使用已知同源对的最佳测试集 标准数据库。

DESCRIPTION: Dr. Abagyan has previously demonstrated that an improved energy function, combined with a powerful conformational search procedure, can accurately predict the detailed (all-atom) structure of small molecules and polypeptide fragments. It is proposed to apply these advances to the development of a comprehensive and accurate homology prediction procedure. Application of this method to homology modeling test cases showed that it permits successful prediction of loops and side chains under idealized conditions. However, the deformed yet rigid structural environment of modeled loops or other backbone fragments was found to be the largest obstacle to successful homology modeling. It is proposed: to adapt the loop modeling method to permit rearrangements in the loop's surroundings; to automate the determination of this "soft" environment; to apply the prediction method to polypeptide ends, mobile secondary structure elements and peptide plane orientation errors; to test and refine all components of the method, using an optimal test set of known homologous pairs from the standard database.