High-Accuracy Models of Proteins from Remote Homology

描述（申请人提供）：本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水平：（1）蛋白质折叠识别，当序列相似性处于或低于所谓的“twilight-zone”时，能够识别结构同源物;（2）经验势函数，能够有效地从存在问题的模型中识别出最佳模型，并能够指导高质量结构模型的生成;（3）精确的序列-结构比对，通过扩展的结构规则，通过同时主链穿线以充分利用更精确的二体和三体能量函数，和（4）通过应用新的环生成和预测技术，以及通过开发用于侧链堆积预测的严格和有效的算法，精确预测环和侧链。该项目成果的关键要素将是非常有效的预测和建模计算方法和实现的集合，可以解决从序列构建高质量结构中最具挑战性的问题。将开发一个用于高精度结构预测的计算管道，该管道针对PDB中不具有紧密结构同源物的蛋白质而设计。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.