Accurate Simulations of Peptide Aggregation

肽聚集的精确模拟

• 批准号: 7915265
• 负责人: PAUL E SMITH
• 金额: $ 21.68万
• 依托单位: KANSAS STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7915265
• 关键词: Affect; Amino Acids; Charge; Computer Simulation; Data; Disease; Health; Heart; Modeling; Peptides; Process; Proteins; Research; Research Design; Research Personnel; Role; Series; Side; Simulate; Solutions; Solvents; System; Testing; Thermodynamics; Urea; Vertebral column; aqueous; biological systems; functional group; intermolecular interaction; molecular dynamics; programs; protein aggregation; simulation; solute; theories; tool

It has been established that peptide and protein aggregation lies at the heart of many diseases. Hence, a detailed understanding of exactly when and how peptides or proteins tend to aggregate would be beneficial to a wide range of researchers studying a variety of health related issues. This proposal outlines a program for the systematic study of peptide aggregation using a unique combination of approaches including: currently available experimental thermodynamic data; the theory of preferential interactions as characterized by Kirkwood-Buff (KB) integrals; a simple model for peptide aggregation using preferential interactions between different functional groups in solution; and computer simulation. A theory and model is developed and demonstrated that can decompose and quantify the interactions between different amino acid side chains using existing experimental data on activity coefficients of small peptides, thus enabling predictions of the tendency for aggregation of any small peptide. Computer simulations are proposed to investigate the atomic level details of the aggregation process. A new peptide and protein force field (KBFF) that can reproduce the experimental KB integrals will be completed and used for the simulations. Aim 1. To quantify and characterize the interactions between functional groups observed in peptides. Analysis of existing experimental data will be performed in aqueous solution to determine preferential interaction (PI) parameters for different amino acid and small peptide systems. A simple model of the Pis will be developed and will then be used to isolate and quantify the Pis between different function groups on those peptides. Aim 2. To produce an accurate force field specifically designed for the study of preferential interactions in biomolecular systems. The KBFF approach will be extended to include all amino acid side chains. Aim 3. To understand the role of cosolvents in modifying intermolecular interactions. The addition of cosolvents to a solution of a solute and solvent changes the interactions between the solute molecules. This provides a tool for investigating the strength of intermolecular interactions common in biological systems and how they may be modified. We will focus on the effects of urea and NaCI during our initial studies.

已经确定肽和蛋白质聚集是许多疾病的核心。因此,