Bayesian Statistics and Algorithms for Homology Modeling

用于同源建模的贝叶斯统计和算法

• 批准号: 7790626
• 负责人: ROLAND L DUNBRACK
• 金额: $ 31.1万
• 依托单位: RESEARCH INST OF FOX CHASE CAN CTR
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-05-15 至 2012-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7790626
• 关键词: Algorithms; Amino Acids; Bioinformatics; Biological; Biological Models; Biology; Classification; Collaborations; Complex; Computer software; Data; Databases; Deposition; Disease; Docking; Environment; Fox Chase Cancer Center; Goals; Health; Homo; Homology Modeling; Human; Individual; Knowledge; Libraries; Ligand Binding; Ligands; Methods; Modeling; Modification; Molecular Conformation; Nonparametric Statistics; Nucleic Acids; Phosphorylation; Positioning Attribute; Post-Translational Protein Processing; Probability; Proteins; Research; Resolution; Sequence Alignment; Side; Source; Standardization; Statistical Methods; Structure; System; Technology; Tertiary Protein Structure; Vertebral column; Visual; Work; base; chemical standardization; database structure; density; design; electron density; glycosylation; graphical user interface; improved; interest; knowledge base; markov model; monomer; next generation; programs; protein complex; protein oligomer; protein structure; protein structure prediction; software development; statistics

DESCRIPTION (provided by applicant): Our main goal is to improve protein structure prediction methods in order to develop models of proteins in biologically relevant states. Such states may include the target protein as a homo-oligomer; complexed with other proteins, nucleic acids, and ligands; covalently modified through phosphorylation and glycosylation; and in alternate physiologically relevant conformations. Information on the structure of these states for any one target may come from a number of different templates; this information can be assembled into a composite model from which biological inferences can be made. The next generation of the backbone-dependent rotamer library will be developed using classical and Bayesian non-parametric statistics, and it will be extended to include protein modifications, such as phosphorylated and glycosylated amino acids. Electron density analysis will be used to exclude residues with uncertain or dynamic conformations. The resulting libraries will be incorporated into the next generation of our widely used side-chain prediction program SCWRL. A very general structural bioinformatics platform will be constructed to enable statistical and conformational analysis of protein structures on a routine basis. We propose to develop interactive methods and software for producing biologically meaningful models of proteins and protein complexes, based on multiple structure alignments, hidden Markov models, and combined information from diverse structures - ligand-bound and unbound structures, monomers and homo-oligomers, and protein complexes. Project narrative Knowledge of protein structures and their complexes is vital to understanding function and mechanism. We will develop algorithms, databases, and software for predicting structure in biologically relevant states, including homo-oligomers, post-translational modifications, and protein complexes. These methods will be used to improve human health through the prediction of proteins involved in disease.

描述（由申请人提供）：我们的主要目标是改进蛋白质结构预测方法，以开发处于生物学相关状态的蛋白质模型。此类状态可包括作为同源寡聚体的靶蛋白；与其他蛋白质、核酸和配体复合；通过磷酸化和糖基化进行共价修饰；以及替代的生理相关构象。任何一个目标的这些状态结构的信息可能来自许多不同的模板；这些信息可以被组装成一个复合模型，从中可以进行生物学推论。下一代主链依赖性旋转异构体库将使用经典和贝叶斯非参数统计来开发，并将扩展到包括蛋白质修饰，例如磷酸化和糖基化氨基酸。电子密度分析将用于排除具有不确定或动态构象的残基。由此产生的库将被纳入我们广泛使用的下一代侧链预测程序 SCWRL 中。将构建一个非常通用的结构生物信息学平台，以便能够对蛋白质结构进行常规统计和构象分析。我们建议开发交互式方法和软件，基于多重结构比对、隐马尔可夫模型以及来自不同结构（配体结合和非结合结构、单体和同源低聚物以及蛋白质复合物）的组合信息，生成具有生物学意义的蛋白质和蛋白质复合物模型。项目叙述 蛋白质结构及其复合物的知识对于理解功能和机制至关重要。我们将开发算法、数据库和软件来预测生物学相关状态的结构，包括同源寡聚物、翻译后修饰和蛋白质复合物。这些方法将用于通过预测与疾病有关的蛋白质来改善人类健康。