High-Accuracy Models of Proteins from Remote Homology

描述(申请人提供)：这个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水平：(1)蛋白质折叠识别，当序列相似性在所谓的“暮光区”内或以下时，能够识别结构同系物；(2)经验势函数，能够有效地从有问题的模型中识别最佳模型，并能够指导高质量结构模型的生成；(3)通过扩展结构规则，通过同时主干穿线，充分利用更精确的二体和三体能量函数，通过系统而快速地生成和应用有限的实验结构数据，实现准确的序列-结构比对；(4)通过应用新的环生成和预测技术，以及通过开发严格高效的侧链堆积预测算法，实现对环和侧链的准确预测。该项目成果的关键要素将是一套非常有效的预测和建模计算方法和实现，这些方法和实现可以解决从序列建立高质量结构的最具挑战性的问题。将开发一种用于高精度结构预测的计算管道，用于在PDB中没有紧密结构同源的蛋白质。这些计算能力可能会对药物设计和疾病研究产生深远影响。

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.