CompBio: A New Paradigm of Protein Threading: simultaneous backbone threading and side-chain packing prediction.

CompBio：蛋白质线程的新范式：同时主链线程和侧链包装预测。

• 批准号: 0621700
• 负责人: Ying Xu
• 金额: $ 27.5万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0621700&HistoricalAwards=false
• 关键词: CompBio; New; Paradigm; Protein; Threading

The knowledge of the tertiary structure is essential to understanding of the biological function and functional mechanism of a protein. The importance of computational solution to protein structures is increasing owing to the rapid growth in the number of sequenced genomes, and the relatively slow growth rate in the number of experimentally determined protein structures. Here we propose to develop a new paradigm for threading-based protein structure prediction using side-chain information. While residue-based approaches have made threading computationally feasible to predict protein structures in the past decade, we clearly see an urgent need now for more accurate prediction techniques that can provide structural data at higher resolution, to meet the rapidly growing need of structural and functional genomics studies. To address this challenging issue, we will develop a novel computational framework for solving a generalized threading problem, in which backbone threading and side-chain packing are predicted simultaneously. Specifically, we will (a) develop a new energy function that combines residue-level information for backbone threading and atom-level information for side-chain packing; (b) develop a novel algorithmic framework for solving the generalized threading problem; (c) extend this algorithmic framework to deal with more general threading problems such as constrained threading problems; (d) test and evaluate the new threading energy functions and algorithms to demonstrate the feasibility and the power of using side-chain information in protein structure prediction, and (e) develop a freely accessible web-based prediction server to benefit the entire research community. To the best of our knowledge, this project probably represents the first systematic effort in generalizing the current threading paradigm to include the detailed side-chain information during the threading process. While the side-chain information should help to significantly improve the prediction accuracy and resolution of protein structures, the generalized threading problem raises some very challenging computational problems. We expect that this new threading capability, when fully developed and implemented, will significantly improve the state of the art of protein structure prediction. In addition, the new threading energy functions and the algorithmic techniques developed in this project will prove to be useful to other researchers in their own development of protein structure prediction capabilities. We believe that our new threading framework will lead the way in developing a new generation of prediction techniques for protein structures, which will not only provide much more accurate backbone structures compared to the existing threading methods but also provide a big portion of the missing structural information by the current threading techniques, i.e., the side-chains. This project provides an ideal training ground for both undergraduate and graduate students to learn bioinformatics tool development for solving complex biological problems. A new course on "Algorithms for Protein Structure Prediction and Modeling" will be developed based on the research results of this project, which will be offered to both undergraduate and graduate students.

了解蛋白质的三级结构对于了解蛋白质的生物学功能和功能机制至关重要。蛋白质结构的计算解决方案的重要性正在增加，由于测序的基因组数量的快速增长，以及实验确定的蛋白质结构的数量相对缓慢的增长速度。在这里，我们提出了一个新的模式，线程为基础的蛋白质结构预测使用侧链信息。虽然基于残差的方法在过去十年中已经使线程计算预测蛋白质结构变得可行，但我们清楚地看到现在迫切需要更准确的预测技术，可以提供更高分辨率的结构数据，以满足结构和功能基因组学研究的快速增长的需求。为了解决这个具有挑战性的问题，我们将开发一个新的计算框架来解决一个广义的线程问题，其中骨干线程和侧链包装同时预测。具体来说，我们将（a）开发一个新的能量函数，它结合了主干线程的剩余水平信息和侧链包装的原子水平信息;（B）开发一个新的算法框架来解决广义线程问题;（c）扩展这个算法框架来处理更一般的线程问题，如约束线程问题;（d）测试和评估新的线程能量函数和算法，以证明在蛋白质结构预测中使用侧链信息的可行性和力量，以及（e）开发一个免费访问的基于网络的预测服务器，使整个研究界受益。 据我们所知，这个项目可能代表了第一个系统性的努力，在推广目前的线程范式，包括详细的侧链信息在线程过程中。虽然侧链信息应该有助于显着提高蛋白质结构的预测精度和分辨率，但广义线程问题提出了一些非常具有挑战性的计算问题。我们预计，这种新的线程能力，当充分开发和实施，将显着提高蛋白质结构预测的艺术状态。此外，该项目中开发的新的线程能量函数和算法技术将被证明对其他研究人员自己开发蛋白质结构预测能力是有用的。 我们相信，我们的新线程框架将引领新一代蛋白质结构预测技术的发展，与现有的线程方法相比，它不仅可以提供更准确的骨架结构，而且还可以提供当前线程技术所缺失的大部分结构信息，即，侧链该项目为本科生和研究生提供了一个理想的培训基地，学习生物信息学工具开发，以解决复杂的生物学问题。基于本项目的研究成果，将开发一门新的课程“蛋白质结构预测和建模的算法”，该课程将面向本科生和研究生。