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: 7485118
• 负责人: Patrice A Koehl
• 金额: $ 25.08万
• 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/7485118
• 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）：该提案题为“正确比对和改进的高精度结构建模”密切遵循NIH RFA定义的两个目标，即(1)获得近同源物的晶体结构质量模型（超过30%的序列同一性）和(2)建立远同源物的高精度模型（低至10%的序列同一性）。我们认为，要实现这些目标，需要开发以下新方法：(a)整合蛋白质序列家族的所有生物和结构信息，以改善目标蛋白质序列与已知结构模板的一致性；(2)对蛋白质结构可接近的构象空间进行采样的新方法；(3)提供接近天然蛋白质结构模型的精确改进的新方法。我们将通过以下具体目标来实现这些目标。