Predicting Protein Structure with Guided Conformation Space Search

通过引导构象空间搜索预测蛋白质结构

• 批准号: 7139225
• 负责人: OLIVER BROCK
• 金额: $ 23.81万
• 依托单位: UNIVERSITY OF MASSACHUSETTS AMHERST
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7139225
• 关键词: bioinformatics; chemical models; chemical structure; computational biology; computer program /software; computer simulation; conformation; mathematical model; molecular biology information system; nuclear magnetic resonance spectroscopy; protein protein interaction; protein structure function; structural biology; three dimensional imaging /topography

DESCRIPTION (provided by applicant): Protein structure prediction is one of the great challenges in structural biology. The ability to accurately predict the three-dimensional structure of proteins would bring about significant scientific advances and would facilitate finding cures and treatments for many diseases. We propose a novel computational framework for protein structure prediction. The novelty of the framework lies in its approach to conformation space search. Conformation space search is considered to be the primary bottleneck towards consistent, high-resolution prediction. The proposed approach to conformation space search represents a major conceptual shift in protein structure prediction, made possible by combining insights and algorithms from robotics and machine learning with techniques from molecular biology in an innovative manner. The key innovation comes from the insight that target-specific information can effectively guide conformation space search towards biologically relevant regions. We propose to develop a framework for protein structure prediction that achieves biological accuracy and computational efficiency by guiding conformation space search using target-specific information. The proposed framework exploits two sources of target-specific information: 1) information about the characteristics of the target's energy landscape acquired continuously during search, and 2) spatial restraints about the target's structure obtained from NMR experiments. As search progresses, the continuous integration of these sources of information will tailor conformation space search to the particular characteristics of the target. This tailored conformation space exploration can overcome the current bottleneck, yielding highly accurate and efficient structure prediction. The ability to determine the three-dimensional structures of proteins, which represent the molecular machinery inside every cell, would greatly facilitate finding cures or treatments for many diseases. This research effort will develop of a novel, efficient, and biologically accurate computational approach to determine the three-dimensional structure of proteins.

描述（由申请人提供）：蛋白质结构预测是结构生物学的巨大挑战之一。准确预测蛋白质的三维结构的能力将带来重大的科学进步，并有助于寻找许多疾病的治疗方法和治疗方法。我们提出了一个新的蛋白质结构预测计算框架。该框架的新颖性在于其构象空间搜索的方法。构象空间搜索被认为是一致，高分辨率预测的主要瓶颈。提出的构象空间搜索方法代表了蛋白质结构预测的重大概念转变，这是通过将机器人和机器学习中的见解和算法与分子生物学的技术相结合，以创新的方式结合起来。关键创新来自于洞察力，即特定于目标的信息可以有效地指导构象空间搜索与生物学相关区域。我们建议开发一个蛋白质结构预测的框架，该预测通过使用目标特定信息引导构象空间搜索来实现生物准确性和计算效率。提出的框架利用了目标特定信息的两个来源：1）有关在搜索过程中连续获得的目标特征的信息，以及2）关于目标结构从NMR实验获得的空间约束。随着搜索的进行，这些信息来源的持续集成将量身定制空间搜索到目标的特定特征。量身定制的构象空间探索可以克服当前的瓶颈，从而产生高度准确有效的结构预测。确定代表每个细胞内分子机械的蛋白质的三维结构的能力，将极大地促进许多疾病的治疗方法或治疗方法。这项研究工作将发展出一种新颖，高效且在生物学上精确的计算方法，以确定蛋白质的三维结构。