High-Accuracy Models of Proteins from Remote Homology

描述（由申请人提供）：该项目的目标是开发新的计算技术，以显著提高蛋白质结构预测的四个领域的最新技术：(1)蛋白质折叠识别，能够在序列相似性在所谓的“模糊区”内或以下时识别结构同源物；(2)经验势函数，可以有效地从有问题的模型中识别出最佳模型，并可以指导生成高质量的结构模型；(3)通过扩展结构规则，通过同时进行骨干穿线，充分利用更精确的二体和三体能量函数，以及通过系统快速地生成和应用实验中有限的结构数据，实现精确的序列结构比对；(4)通过应用新的环生成和预测技术，以及通过开发严格和高效的侧链包装预测算法，准确预测环和侧链。该项目成果的关键要素将是一个非常有效的预测和建模计算方法和实现的集合，它可以解决从序列中构建高质量结构的最具挑战性的问题。针对PDB中不具有紧密结构同源的蛋白质，将开发一种用于高精度结构预测的计算管道。这些计算能力可能对药物设计和疾病研究产生深远的影响。

DESCRIPTION (provided by applicant): The goal of this project is to develop novel computational techniques to significantly improve the state-of-the-art in four areas of protein structure prediction: (1) protein fold recognition capable of identifying structural homologs when the sequence similarity is within or below the so-called "twilight-zone"; (2) empirical potential functions that can effectively identify the best models from those that are problematic and can guide the generation of high-quality structural models; (3) accurate sequence-structure alignments, through expanded structural rules, through simultaneous backbone threading to take full advantage of the more accurate two-body and three-body energy functions, and through systematic and rapid generation and application of limited structural data from experiments; and (4) accurate prediction of loops and side-chains through applications of novel loop generation and prediction techniques, and through development of rigorous and efficient algorithms for side-chain packing prediction. The key elements of the outcome of this project will be an ensemble of very effective prediction and modeling computational methods and implementations, which can address the most challenging problems in building high quality structures from sequences. A computational pipeline for high accuracy structure prediction designed for proteins that do not have close structural homologs in PDB will be developed. These computational capabilities could have profound impacts to drug design and disease studies.