CAREER: Novel Sampling Approaches for Protein Modeling Applications

职业：蛋白质建模应用的新型采样方法

• 批准号: 0845702
• 负责人: Yaohang Li
• 金额: $ 40万
• 依托单位: North Carolina Agricultural & Technical State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2011-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845702&HistoricalAwards=false
• 关键词: CAREER; Novel; Sampling; Approaches; Protein

CAREER: Novel Sampling Approaches for Protein Modeling ApplicationsYaohang LiNorth Carolina A&T State UniversityAccurately modeling protein or protein complex structure is considered a significant grand challenge that has broad economic and scientific impact. One of the key obstacles is the absence of a reliable sampling method that can efficiently explore the tremendously large protein conformation space. This CAREER project investigates efficient sampling approaches that can lead to prediction of high resolution protein structures with accuracy and reliability currently not achievable in computational protein modeling. The rationale is to integrate various physics- and knowledge-based scoring functions via multi-scoring functions sampling to explore the complex protein conformation space. The research work includes 1) establishing computational models for multi-scoring functions sampling in protein structure modeling with theoretically and mathematically rigorous justification; 2) designing novel sampling algorithms to efficiently explore large protein conformation space; 3) applying the sampling algorithms to important protein modeling applications including ab initio protein folding and protein-protein docking; and 4) developing a resource-efficient protein modeling programming paradigm.The efficient sampling approaches developed in this research can be applied to a variety of important computational biology applications, which will provide a critical stepping stone toward reliable resolution improvement in protein models for practical use. Success of high resolution protein modeling will have significant impact on genomic study, disease research, bio-energy development, and the drug-design industry. In addition to its research impact, the educational goal of this CAREER project is to attract excellent students, particularly the minorities, to participate in computational biology research and pursue computational science career.

职业：用于蛋白质建模应用的新型抽样方法Yaohang Linorth Carolina A＆T州立大学对蛋白质或蛋白质复杂结构进行建模是一项重大的巨大挑战，具有广泛的经济和科学影响。关键障碍之一是缺乏可靠的抽样方法，该方法可以有效地探索巨大的蛋白质构象空间。该职业项目调查了有效的抽样方法，这些方法可以导致对高分辨率蛋白质结构的预测，这些蛋白质结构的准确性和可靠性目前在计算蛋白质建模中目前尚不可实现。理由是通过多得分功能采样来整合各种物理和知识的评分功能，以探索复杂的蛋白质构象空间。研究工作包括1）建立用于具有理论和数学上严格理由的蛋白质结构建模中多得分功能采样的计算模型； 2）设计新型采样算法以有效探索大型蛋白质构象空间； 3）将采样算法应用于重要的蛋白质建模应用，包括抗量蛋白折叠和蛋白质 - 蛋白质对接； 4）开发一种资源有效的蛋白质建模编程范式。本研究中开发的有效抽样方法可以应用于各种重要的计算生物学应用，该应用将为蛋白质模型的可靠分辨率改善用于实际用途的可靠分辨率，以提供实际使用。高分辨率蛋白质建模的成功将对基因组研究，疾病研究，生物能源发展和药物设计产业产生重大影响。除研究影响外，该职业项目的教育目标是吸引优秀的学生，尤其是少数群体，以参与计算生物学研究并从事计算科学职业。