Specificity and Selectivity in Protein-Ion Binding

蛋白质-离子结合的特异性和选择性

基本信息

批准号：
10397564
负责人：
JAY PONDER
金额：
$ 31.29万
依托单位：
UNIVERSITY OF TEXAS AT AUSTIN
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-05-01 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10397564
关键词：
Algorithms Binding Binding Proteins Biological Biological Process C2 Domain Charge Communicable Diseases Communities Complex Data Development Diagnostic EF Hand Motifs Educational workshop Electrostatics Free Energy Future Geometry Goals Heart Diseases Infrastructure Ions Knowledge Lead Ligands Malignant Neoplasms Mediating Metabolism Metal Ion Binding Metalloproteins Metals Modeling Molecular Movement Neurodegenerative Disorders Penetration Performance Physics Play Potential Energy Proteins Public Health Research Role Science Signal Transduction Site Sodium Chloride Specificity Structure System Therapeutic Thermodynamics Time Training Transition Elements base computer studies computerized tools design diagnostic strategy driving force improved inhibitor innovation inorganic phosphate insight mechanical energy models and simulation molecular dynamics molecular mechanics next generation novel diagnostics novel therapeutics physical model protonation quantum response simulation simulation software small molecule therapeutic protein therapeutic target treatment fees

项目摘要

SUMMARY Over 30% of all proteins bind metal ions, including transition metals, for structural and functional purposes. Even though there are rich experimental structural and thermodynamic information on protein-ion complexes, our understanding of the physical basis for the specificity and selectivity in protein-ion recognition remains lacking. There is a great need for accurate physical models and efficient simulation software to enable computational study of protein-ion systems. We propose to develop a next generation classical force field AMOEBA+, based on the existing AMOEBA potential, to systematically model permanent electrostatics, repulsion, dispersion, charge penetration, polarization, charge transfer, and ligand field effect after quantum mechanical energy decomposition and experimental data. The new potential and high- performance molecular simulation software (Tinker for CPU & GPU systems) will allow us to comprehend the structural, physical and thermodynamic driving forces underlying protein-ion recognition using molecular dynamics simulations. Given the fundamental importance of protein interaction with transition metals including Zn, Cu, Ni, Co, Fe, and Mn, this research will have a broad impact on advancing our scientific knowledge about ions in biomolecular structure and function, and lead to new computational tools to accelerate the design of new diagnostic and therapeutic molecules targeting protein-ion interactions.

概括超过 30% 的蛋白质结合金属离子，包括过渡金属，用于结构和功能性目的。尽管有丰富的实验结构和蛋白质-离子复合物的热力学信息，我们对物理的理解蛋白质离子识别的特异性和选择性的基础仍然缺乏。有非常需要准确的物理模型和高效的仿真软件来实现蛋白质离子系统的计算研究。我们建议开发下一代经典力场AMOEBA+，基于现有的AMOEBA势，系统地模拟永久静电、排斥、色散、电荷量子后的穿透、极化、电荷转移和配体场效应机械能分解和实验数据。新的潜力和高高性能分子模拟软件（Tinker for CPU & GPU Systems）将允许我们理解潜在的结构、物理和热力学驱动力使用分子动力学模拟进行蛋白质离子识别。鉴于基本蛋白质与过渡金属（包括 Zn、Cu、Ni、Co、Fe 和）相互作用的重要性嗯，这项研究将对推进我们的科学知识产生广泛的影响离子在生物分子结构和功能中的作用，并导致新的计算工具加速针对蛋白质离子的新诊断和治疗分子的设计互动。