REFINEMENT OF PREDICTED LOW-RESOLUTION PROTEIN MODELS TO HIGH-RESOLUTION ALL-AT

将预测的低分辨率蛋白质模型细化为高分辨率 All-AT

• 批准号: 7601397
• 负责人: JEFFREY SKOLNICK
• 金额: $ 0.03万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2008-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7601397
• 关键词: Affect; Algorithms; Amino Acid Sequence; Amino Acids; Computer Retrieval of Information on Scientific Projects Database; Data; Distant; Drug Design; Environment; Failure; Free Energy; Funding; Generations; Goals; Grant; Institution; Laboratories; Ligands; Methodology; Modeling; Molecular Conformation; Pharmaceutical Preparations; Plant Roots; Proteins; Relative (related person); Research; Research Personnel; Resolution; Resources; Screening procedure; Shapes; Solvents; Source; Structure; United States National Institutes of Health; Vertebral column; improved; molecular mechanics; protein structure; protein structure prediction

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. A major challenge in protein structure prediction is the refinement of low-resolution predicted models (e.g. those with a backbone root-mean-square-deviation, RMSD, from the native structure of about 5 ), to high-resolution all-atom structures with a RMSD of less than 2 . To date, there have only been a few documented instances where structures at atomic detail have improved relative to their initial starting conformation. Even if low-resolution models with a RMSD from the native structure of about 5 were available for a target protein, the refinement to a high-resolution structure with a RMSD of less than 2 can be difficult to achieve. Given the increasing ability of protein structure prediction algorithms, such as TASSER developed in our laboratory, to assemble approximately correct native structures, the effort should be made to refine such models so that they can be used for ligand screening as well as more general types of functional annotation. High-resolution structures are useful in detailed studies of mechanisms as well as in the design of drug molecules in which the (atomic) local environment of the small (drug) molecule requires accurate characterization. While significant progress has been made in the generation of low-resolution structures and their selection, the final step of structural adjustment still poses significant difficulties. Despite considerable effort, structure refinement from proteins having near native structures has proven to be very difficult, especially if all atom models and atomic potentials are used. It is necessary for successful refinement of low-resolution models that the force field recognizes the native structure of a protein as the lowest free energy minimum, and it shows correlation between energy and the native-likeness of decoy structures. Current all-atom force fields in many cases fail to recognize the native structure as the lowest free energy minimum and energy of the decoys do not monotonically decreases as the native conformation is approached. The source of this inaccuracy may be: a). the mathematical form of the potential b). lack of important physical interactions in the potential, e.g. polarization or correlation effects (many body interactions), or c). inadequate parameterization of the force field, which does not include sufficient information about the global shape of the energy landscape. Most all-atom force fields rely on the assumption that the conformation of each residue is energetically insensitive to the conformation of its neighbors (i.e. they do not contain explicit correlation terms for the backbone conformation (torsional angles) of the neighboring residues). However, there are experimental and statistical data from solved protein structures, showing that the backbone conformation of a residue in the amino acid chain is correlated with the conformation and identity of the neighboring residues. These data reflect the influence of the environment of the protein, where the correlations of local effects with solvent and non-local effects are non-separable. Therefore, it is important to determine to what extent the local interactions (between residues close in amino acid sequence) alter the accessible conformational space and how much of this effect originates from non-local interactions (between residues close in space but distant in sequence) and solvation, and whether current extant force fields, that do not explicitly include backbone correlation and polarization can reproduce these effects. Conceivably, ignoring some important interactions could result in inaccuracies of the relative conformational energies thereby affecting the ability to identify the native structure as the lowest energy minimum. However, if the functional form of current molecular mechanics potentials is mathematically correct, then the failure to recognize the native structure as the global energy minimum could arise from incorrect details of the force field parameters that should be fixable by reparameterization. The long-term goal of this project is to evaluate and improve accuracy of current all-atom force fields and develop methodology to refine the low-resolution protein models, resulted from the protein structure prediction approaches developed by Skolnick and coworkers, to high-resolution structures in all-atom representation.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 蛋白质结构预测的主要挑战是低分辨率预测模型的改进（例如那些具有骨干根平方 - 偏离的骨架型RMSD，从约5的天然结构）到具有RMSD的高分辨率全原子结构。迄今为止，只有少数记录的实例，与最初的起始构象相对于原子细节的结构有所改善。即使来自天然结构的RMSD的低分辨率模型可用于目标蛋白，也很难实现对RMSD小于2的高分辨率结构的细化。鉴于蛋白质结构预测算法的越来越多的能力，例如在我们的实验室中开发的Tasser，可以组装近似正确的天然结构，因此应努力改善此类模型，以便它们可用于配体筛选以及更一般的功能注释类型。高分辨率结构可用于详细研究机制以及在其中（药物）分子的（原子）局部环境需要准确表征的药物分子的设计。尽管在低分辨率结构及其选择中已经取得了重大进展，但结构调整的最后一步仍然带来了很大的困难。尽管付出了巨大的努力，但事实证明，蛋白质的结构细化已被证明非常困难，尤其是在使用所有原子模型和原子电位时。有必要成功地改进低分辨率模型，使力场识别蛋白质的天然结构是最低的自由能最小值，并且显示了能量与诱饵结构的天然风格之间的相关性。在许多情况下，当前的全原子力场未能将天然结构视为最低的自由能最小值和诱饵的能量，并且随着天然构象的接近，诱饵的能量不会单调减少。这种不准确的来源可能是：a）。潜在b）的数学形式。潜力缺乏重要的物理相互作用，例如极化或相关效应（许多身体相互作用）或C）。力场的参数化不足，该参数不包括有关能量景观全局形状的足够信息。大多数全原子力场都取决于以下假设：每个残基的构象在邻居的构象上都无敏感（即它们不包含相邻残基的骨架构象（扭转角）的明确相关项）。但是，有来自解决的蛋白质结构的实验和统计数据，表明氨基酸链中残基的骨架构象与相邻残基的构象和身份相关。这些数据反映了蛋白质环境的影响，在蛋白质的环境中，局部效应与溶剂和非本地效应的相关性是不可分离的。因此，重要的是要确定局部相互作用（在氨基酸序列中关闭的残基之间）在多大程度上改变了可访问的构象空间，以及这种效应的多少起源于非本地相互作用（在空间中的残基相近，但在序列中距离遥远）和溶解度，以及当前的现有力场，是否不概念性地将backbone相关化和极化包括这些效果。可以想象，忽略某些重要的相互作用可能导致相对构象能的准确性，从而影响将天然结构识别为最低能量最小值的能力。但是，如果电流分子力学电位的功能形式在数学上是正确的，则无法将天然结构识别为全球能量最小值可能是由于力场参数的不正确细节而引起的，应通过重新聚体化来固定。该项目的长期目标是评估和提高当前全原子力场的准确性，并开发方法来完善低分辨率蛋白质模型，这是由Skolnick及其同事开发的蛋白质结构预测方法引起的，到全原子代表中的高分辨率结构。