Alignments and Improved Refinements for High-Accuracy Protein Structure Modeling

高精度蛋白质结构建模的比对和改进改进

• 批准号: 7304593
• 负责人: Patrice A Koehl
• 金额: $ 27.15万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-15 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7304593
• 关键词: Algorithms; Amino Acid Sequence; Biological; Code; Communities; Computational Biology; Data Sources; Development; Ensure; Family; Goals; Homologous Gene; Homology Modeling; Mechanics; Methods; Modeling; Molecular Conformation; Nature; Paper; Peptide Sequence Determination; Pliability; Positioning Attribute; Procedures; Property; Protein Conformation; Proteins; Publishing; Range; Request for Applications; Research Personnel; Sampling; Score; Sequence Alignment; Side; Solutions; Solvents; Source; Structural Models; Structural Protein; Structure; Vertebral column; Visual; base; computer program; concept; data integration; design; experience; improved; insertion/deletion mutation; interest; molecular dynamics; novel strategies; originality; programs; protein structure; quantum; skills; software development; success; tool

DESCRIPTION (provided by applicant): This proposal entitled "Correct Alignments and Improved Refinements for High-Accuracy Structure Modeling" follows closely the two goals defined by the NIH RFA, namely, (1) getting crystal structure quality models for close homologs (more than 30% sequence identity) and (2) building high accuracy models for remote homologs (as low as 10% sequence identity). We believe that reaching these goals will require the development of (a) new approaches to integrate all biological and structural information available on a protein sequence family to improve the alignment of the target protein sequence to a known structural template, (2) new methods for sampling the conformational space accessible to a protein structure, and (3) new methods that provide accurate refinements of near native protein structural models. We will achieve these goals through the following specific aims. (1) Generate accurate alignments between the target protein sequence and a structural template by combining a wide range of different sources of information. Essential to this aim is the integration of these data into a unified framework. We will develop the concept of residue position annotation (RPA) for homology modeling, in which different positions in the sequence have different impact in the modeling procedure depending on their properties, derived from multiple sequence alignments. We will use the framework of mean field minimization to design a new alignment package that incorporate different types of constraints, even non-additive. (2) Build high accuracy models for the target protein. Homology modeling based on a given sequence alignment between a target sequence and the sequence of a template protein whose structure is known usually involve two steps: loop building, to fix regions of insertion and deletion in the alignment, and side-chain modeling. We will elaborate from our extensive experience in developing solutions to both problems to propose new approaches that circumvent these difficulties. (3) Improve initial models to generate crystal structure quality models using structure refinement. We will follow three directions (a) minimization and molecular dynamics with improved force fields, including quantum mechanical terms, (b) minimization and molecular dynamics with improved implicit solvent models and (c) energy minimization with cooperative many-body energy terms. (4) Organize all computer programs developed within this proposal into a user-centric package, including visual computing tools, to make it accessible to the biological community at large.

描述（由申请人提供）：该提案为“正确的对齐方式和改进的高素质结构模型的改进”，紧密遵循NIH RFA所定义的两个目标，即（1）（1）获得近距离同源物的晶体结构质量模型（超过30％的序列身份）和（2）以低准确性的模型（以低于偏远的序列构建远距离的同源性），为10％的同源性（2）。我们认为，实现这些目标将需要开发（a）新方法，以整合蛋白质序列家族上可用的所有生物学和结构信息，以改善目标蛋白质序列与已知结构模板的对齐，（2）新方法，用于对蛋白质结构可访问的构型空间，并提供蛋白质结构的新方法，以及（3）新的新方法，这些方法可提供近乎新的本机蛋白质结构的新方法。我们将通过以下特定目标实现这些目标。 （1）通过组合多种不同的信息来源，在目标蛋白序列和结构模板之间产生准确的比对。这个目的至关重要的是将这些数据集成到统一的框架中。我们将开发用于同源性建模的残留位置注释（RPA）的概念，在该模型中，序列中的不同位置在建模过程中具有不同的影响，具体取决于其属性，这些属性来自多个序列比对。我们将使用平均字段最小化的框架来设计一个新的对齐软件包，该包装包含不同类型的约束，甚至是非添加的约束。 （2）为目标蛋白建立高精度模型。基于目标序列和模板蛋白序列之间的给定序列比对的同源性建模通常涉及两个步骤：环构建，以固定对齐方式中的插入和缺失区域，以及侧链建模。我们将从我们在开发两个问题的解决方案方面的丰富经验中阐述，以提出规避这些困难的新方法。 （3）改进初始模型，使用结构改进生成晶体结构质量模型。我们将遵循三个方向（a）具有改进力场的最小化和分子动力学，包括量子机械项，（b）最小化和分子动力学，具有改进的隐式溶剂模型，以及（c）通过合作多体能量项最小化的能量最小化。 （4）将本提案中开发的所有计算机程序组织成一个以用户为中心的软件包，包括视觉计算工具，以使其可供整个生物群落访问。