Computational Modeling /Design of Antimicrobial Peptides

描述（由申请人提供）：我们建议开发促进抗菌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。近几十年来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（AMPs）被认为是真核生物先天免疫反应机制的有效成分，是一种很有前景的抗病原体治疗药物。大量的实验表明，细菌包膜成分和多肽之间存在复杂的相互作用。然而，amp究竟如何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，来了解amp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，用于识别已知的自然发生的amp中重复出现的序列和结构模式。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。

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.