Protein Structure Prediction Using First Principles

使用第一原理预测蛋白质结构

• 批准号: 7683007
• 负责人: Christodoulos Achilleus Floudas
• 金额: $ 24.54万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-05-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7683007
• 关键词: Address; Amino Acids; Arts; Benchmarking; Binding; CASP7 gene; Chemistry; Computational Biology; Derivation procedure; Development; Event; Free Energy; Generations; Lead; Membrane Proteins; Methods; Modeling; Peptides; Pharmaceutical Preparations; Proteins; Public Health; Research; Resolution; Structure; Tertiary Protein Structure; Testing; Uncertainty; Work; base; beta pleated sheet; blind; combinatorial; drug discovery; improved; novel; novel strategies; protein structure; protein structure prediction; public health relevance; restraint; stem

DESCRIPTION (provided by applicant): The overall aim of this project is to improve our understanding of first principles structure prediction of proteins. Based upon our earlier research work on the Astro-Fold (Klepeis and Floudas, 2003c) (see Figure 1 in section C.2), the proposed research is directed towards the development of an enhanced framework for protein structure prediction. This framework consists of (a) free energy calculations for helical segment prediction, (b) a combinatorial optimization approach for the prediction of beta strands and beta sheet topology, (c) a new proposed approach for predicting inter-helical tertiary contacts, (d) a new proposed approach for general residue-residue contact prediction, (e) the derivation of improved distance restraints from predictions in (a-d), and a new proposed optimization-based iterative approach, (e) the generation of an ensemble of low energy tertiary protein structures via the aBB deterministic global optimization approach and torsional angle dynamics, and (f) a new proposed approach for the selection of the predicted tertiary protein structure, from the ensemble of low energy protein structures, using a distance dependent force field developed based on high and/or medium resolution decoys. We put forward the following four specific aims: Specific Aim 1: Investigate and develop a novel approach for the prediction of inter-helical tertiary contacts for a-helical proteins, and a/b proteins. Specific Aim 2: Investigate and develop a novel optimization method for the prediction of residue-residue contacts in alpha helical, alpha/beta and beta proteins. Specific Aim 3: Investigate a new iterative optimization-based approach for the generation of additional and improved distance restraints for the tertiary structure prediction. Specific Aim 4: Study and develop a powerful force field which will be able to discriminate the folded structure among high resolution and/or medium resolution decoys generated from the search of tertiary protein structures. Study a robust optimization approach for the force held development. Investigate, test, and validate the overall proposed enhanced framework for protein structure prediction. PUBLIC HEALTH RELEVANCE The protein structure prediction using first principles is not only of fundamental importance to computational biology and chemistry, but also of major importance for advancing the understanding of protein-protein and protein-peptide interactions which have a direct impact on drug discovery. Improvements of predictive methods for the elucidation of protein structures for globular and membrane proteins will lead into development of novel drugs which will benefit the public health.

描述（由申请人提供）：该项目的总体目的是提高我们对蛋白质的第一原理结构预测的理解。基于我们对Astro折叠的较早研究工作（Klepeis和Floudas，2003c）（请参阅第2节中的图1），该研究旨在开发增强的蛋白质结构预测框架。该框架由（a）用于螺旋节段预测的自由能计算，（b）用于预测β链和β片拓扑的组合优化方法，（c）一种预测螺旋间高级触点的新的拟议方法，（d）一种新建议的方法，用于一种新建议的方法 残留残留 - （e）（e）（a-d）中预测的改进距离限制的推导，以及新的基于优化的迭代方法，（e）通过ABB确定性的全局优化方法和（f）的结构源，（f）在质疑的方法中生成低能蛋白质结构的集合，并获得了一种新的概述的方法，并获得了新的概述的方法。结构，使用基于较高和/或中分辨率诱饵开发的距离依赖性力场。我们提出了以下四个特定目标： 具体目标1：研究并开发了一种新的方法，用于预测A螺旋蛋白和A/B蛋白的螺旋间三级接触。 具体目标2：研究并开发了一种新型优化方法，用于预测α螺旋，α/β和β蛋白中残基弥散接触。 特定目的3：研究一种基于迭代优化的新方法，以生成三级结构预测的额外和改进的距离限制。 特定目的4：研究和发展强大的力场，该场将能够区分从搜索三级蛋白质结构而产生的高分辨率和/或中等分辨率诱饵之间的折叠结构。研究针对力持有发展的强大优化方法。调查，测试和验证总体提出的蛋白质结构预测框架。 公共卫生相关性使用第一原理的蛋白质结构预测不仅对计算生物学和化学的重要性，而且对于促进对蛋白质蛋白质和蛋白质肽相互作用的理解的主要重要性，这对药物发现有直接影响。阐明球状和膜蛋白质蛋白质结构的预测方法的改进将导致开发新的药物，从而使公共卫生受益。