Computational Modeling and Design of Antimicrobial Peptides

抗菌肽的计算模型和设计

• 批准号: 7265324
• 负责人: YIANNIS KAZNESSIS
• 金额: $ 21.41万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7265324
• 关键词: Algorithms; Antibiotics; Antiviral Agents; Area; Binding; Bioinformatics; Biological; Biological Models; Blood - brain barrier anatomy; Categories; Cell Wall; Cell membrane; Chemical Engineering; Classification; Classification Scheme; Collaborations; Complex; Computational Science; Computer Simulation; Computing Methodologies; Development; Disruption; Doctor of Philosophy; Drug resistance; Electrostatics; Elements; Engineering; Environment; Equilibrium; Exhibits; Experimental Designs; Family; Feedback; Foundations; Hemolysis; Hydrocarbons; Hydrogen Bonding; Hydrophobic Interactions; Hydrophobicity; Immune response; In Vitro; Institutes; Knowledge; Laboratories; Lead; Light; Lipid Bilayers; Mammalian Cell; Measurement; Mechanics; Mediating; Membrane; Methods; Micelles; Minnesota; Modeling; Molecular; Molecular Conformation; Numbers; Paper; Pattern; Pattern Recognition; Peptide Synthesis; Peptides; Pharmacologic Substance; Phospholipids; Physics; Postdoctoral Fellow; Process; Productivity; Protocols documentation; Public Health; Publications; Pulmonary Surfactant-Associated Proteins; Qualifying; Research; Research Personnel; Resistance; Resolution; Resources; Scheme; Science; Scientist; Solid; Specificity; Speed; Structure; Students; Supercomputing; System; Technology; Testing; Therapeutic; Toxic effect; Training; Trypsin Inhibitors; Validation; Viral; Work; antimicrobial; antimicrobial peptide; base; biomedical scientist; cathelicidin; cell envelope; cheminformatics; computer science; computerized tools; data mining; design; digital; experience; improved; in vivo; knowledge base; membrane model; model design; molecular dynamics; molecular mechanics; novel; pathogen; programs; protein aminoacid sequence; research study; simulation; skills; sound; success; tool

DESCRIPTION (provided by applicant): We propose the development of computational tools that promote the progress of antimicrobial peptide engineering. Drug-resistant pathogens are becoming a significant public health concern. The prolific use of antibiotics in the last few decades inevitably increased the bacterial species with resistance. Antimicrobial peptides (AMPs), recognized as potent components of eukaryotic innate immune response mechanisms, appear to be promising therapeutic anti-pathogen agents. A wide array of experiments suggests a complex interplay between the bacterial cell envelope components and the peptides. However, how exactly AMPs modulate membrane structure remains largely unclear. We leverage the high resolution, atomic level picture of molecular dynamics simulations, to understand the interactions of AMPs with bacterial and mammalian membranes. We also develop data mining algorithms that identify recurring sequence and structural patterns in known, naturally occurring AMPs. Importantly, active collaborations with leading research groups in the areas of antimicrobial peptides and peptide/membrane interactions provide the necessary feedback mechanism for validation and refinement of computational results. The three specific aims of this project are: 1. Quantify the interactions between AMPs and mammalian, bacterial and viral model membranes using high productivity computer simulations. 2. Recognize the sequence/structural elements that are responsible for cathelicidin and minidefensin antimicrobial activity 3. Establish a process of feedback mechanisms between experiments, computer models and new experimental design in order to promote rational peptide engineering. Experimentally investigate novel peptides based on model-driven design rules.

描述（由申请人提供）：我们建议开发促进抗微生物肽工程进展的计算工具。耐药病原体正在成为一个重大的公共卫生问题。在过去的几十年里，抗生素的大量使用不可避免地增加了具有耐药性的细菌种类。抗菌肽是真核生物天然免疫应答机制中的重要组成部分，是一种有前途的抗病原体药物。大量的实验表明，细菌细胞包膜成分和肽之间存在复杂的相互作用。然而，AMP如何确切地调节膜结构仍不清楚。我们利用分子动力学模拟的高分辨率，原子水平的图片，了解抗菌肽与细菌和哺乳动物膜的相互作用。我们还开发了数据挖掘算法，识别已知的，自然发生的AMP中的重复序列和结构模式。重要的是，与抗菌肽和肽/膜相互作用领域的领先研究小组的积极合作为验证和改进计算结果提供了必要的反馈机制。 该项目的三个具体目标是： 1.使用高生产率计算机模拟定量AMP与哺乳动物、细菌和病毒模型膜之间的相互作用。 2.识别负责cathelicidin和minidefensin抗菌活性的序列/结构元件 3.建立实验、计算机模型和新实验设计之间的反馈机制，以促进合理的肽工程。基于模型驱动的设计规则实验研究新型肽。