Advanced Sequence-Based Prediction of Protein Function

先进的基于序列的蛋白质功能预测

• 批准号: 6559851
• 负责人: ANDREW F NEUWALD
• 金额: $ 15.2万
• 依托单位: COLD SPRING HARBOR LABORATORY
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-30 至 2006-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6559851
• 关键词: computer assisted sequence analysis; computer simulation; computer system design /evaluation; information systems; mathematical model; method development; protein sequence; protein structure function; statistics /biometry; structural biology

The goal of this project is to improve the prediction of protein structure and function and thereby enhance our understanding of cellular processes mediated by their associated proteins. The specific aims are: (i) to deepen our understanding of the relationship between sequence and structure, (ii) to develop fast and sensitivity tools for searching a database of advanced, family-specific hidden Markov models of protein domains; (iii) to improve the accuracy of multiple sequence alignments; (iv) to develop tools for coordinated analysis of various forms of biological data; and (v) to advance our understanding of specific cellular processes through comprehensive analysis of the proteins that collectively orchestrate those processes. Methods to be used include hidden Markov models, simulated annealing, Gibbs sampling and other Monte Carlo methods, secondary structure and structural threading methods, gapped BLAST and PSI-BLAST heuristic routines, phylogenetic and clustering techniques, as well as other statistical and algorithmic methods. During their development, these methods will be applied to the analysis of various cellular processes, including signaling pathways, RNA processing, chromosome dynamics, and chaperone-mediated assembly and disassembly of protein complexes.

这个项目的目标是改进对蛋白质结构和功能的预测，从而增强我们对相关蛋白质介导的细胞过程的理解。具体目标是：(I)加深我们对序列和结构之间关系的理解；(Ii)开发快速和灵敏的工具，用于搜索高级的、特定于家族的蛋白质结构域的隐马尔可夫模型数据库；(Iii)提高多序列比对的准确性；(Iv)开发各种形式的生物数据的协调分析工具；以及(V)通过对共同协调这些过程的蛋白质的全面分析，促进我们对特定细胞过程的理解。使用的方法包括隐马尔可夫模型、模拟退火法、吉布斯抽样和其他蒙特卡罗方法、二级结构和结构穿线方法、间隙BLAST和PSI-BLAST启发式例程、系统发育和聚类技术以及其他统计和算法方法。在它们的发展过程中，这些方法将被应用于各种细胞过程的分析，包括信号通路、RNA处理、染色体动力学以及伴侣介导的蛋白质复合体的组装和拆解。