Precision Design of Antimicrobial Peptides Against Bacterial Infections

抗细菌感染抗菌肽的精密设计

• 批准号: 10708842
• 负责人: Jianing Li
• 金额: $ 30.29万
• 依托单位: PURDUE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-23 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10708842
• 关键词: Affect; Animals; Anti-Bacterial Agents; Antibiotic Resistance; Antibiotics; Antimicrobial Cationic Peptides; Bacteria; Bacterial Antibiotic Resistance; Bacterial Infections; Binding; Biological; Biological Assay; Calorimetry; Cells; Cellular Assay; Characteristics; Chemicals; Clinic; Communicable Diseases; Communities; Complex; Computational Technique; Dangerousness; Data; Development; Drug resistance; Environment; Extravasation; Failure; Fluorescence; Free Energy; Future; Generations; Goals; Growth; Healthcare; Homo; Infection; Innate Immune System; Klebsiella pneumoniae; Knowledge; Learning; Lipids; Machine Learning; Measurement; Membrane; Methodology; Methods; Microbiology; Modernization; Molecular; Peptide Synthesis; Peptides; Periplasmic Proteins; Plants; Process; Property; Proteins; Public Health; Research; Resistance; Risk; Structure; Testing; Therapeutic; Time; Titrations; Toxic effect; Vesicle; Work; analog; antibiotic resistant infections; antimicrobial peptide; bacterial resistance; combat; computerized tools; cost effective; design; drug resistant pathogen; experimental study; generative adversarial network; innovation; invention; knowledge of results; large datasets; light scattering; lipid transport; magainin; membrane model; multi-scale modeling; natural antimicrobial; network models; next generation; novel; novel therapeutics; pathogen; pathogenic bacteria; peptide structure; protein aminoacid sequence; research and development; simulation; small molecule; synergism; tachyplesin; therapeutic target; tool

PROJECT SUMMARY Antibiotic resistance of bacterial pathogens is one of the greatest public health challenges of our time. It causes difficult-to-treat infections and jeopardizes modern healthcare advancements. As the emergence of bacterial resistance is outpacing the development of new antibiotics, we must find cost-effective, innovative approaches to discover new antibacterial therapeutics complementary to small-molecule antibiotics. Antimicrobial peptides (AMPs), as a new class of antibacterial agents, represent one of the most promising solutions to fill this void, since they generally undergo faster development, display rapid onsets of killing, and most importantly show lower risks of induced resistance, compared to small-molecule antibiotics. Yet, very few analogs or modified derivatives of natural AMPs have been approved in practice, and most of the failure is caused by systemic or local toxicity associated with broad-spectrum antibacterial activity. Toward a long-term goal to discover effective, selective AMPs as therapeutics to target a narrow spectrum of specific antibiotic-resistant pathogens, our objective is to develop the new capacity needed for such discovery, by integrating innovative approaches and applications of machine learning, multiscale modeling, peptide synthesis, and microbiology. We have developed the first generative adversarial network model (AMP-GAN) to produce AMP candidates with diverse sequences and structures, as well as accurate multiscale models and methods to study the mechanisms of AMP aggregation and target interactions. It is our central hypothesis that AMP selectivity may be achieved via controlling their sequence, structure, interaction, aggregation, and co-aggregation. In pursuit of three specific aims to establish a novel methodology toward discovery of narrow-spectrum AMPs, we will (i) generate selective AMP sequences with predictable activity and pathogen targets, (ii) identify AMPs to target characteristic biomolecules in pathogens, and (iii) modulate AMP aggregation to tune cell selectivity or to achieve synergy. We will advance our computational techniques like AMP-GAN and top-down simulations in conjugation with chemical characterizations (for structure and dynamics) and cellular assays (for activity and toxicity). We anticipate gaining a fundamental understanding of how to design narrow-spectrum AMPs, as well as how to combine new computational and experimental tools to achieve desired AMP selectivity. Overall, this contribution can be significant since it will establish new avenues for precision AMP design and bring more AMPs closer to the clinic by overcoming their known pitfalls. The resulting knowledge will be widely shared in the scientific community for AMP research and development. Our concepts and approaches are innovative, as they shift the current paradigm of broad-spectrum AMP design towards higher accuracy, diversity, and target selectivity through precision AMP design. Collectively, given the increasing need for treatment options against antibiotic-resistant infections, the methodology and tools from this proposed research will enable the discovery of new therapeutics for challenging infectious diseases.

项目概要 细菌病原体的抗生素耐药性是我们这个时代最大的公共卫生挑战之一。它导致 难以治疗的感染并危及现代医疗保健的进步。随着细菌的出现 耐药性的发展速度超过了新抗生素的开发速度，我们必须找到具有成本效益的创新方法 发现与小分子抗生素互补的新抗菌疗法。抗菌肽 （AMP）作为一类新型抗菌剂，是填补这一空白的最有前途的解决方案之一， 因为它们通常会经历更快的发育，表现出快速的杀戮能力，最重要的是表现出较低的 与小分子抗生素相比，存在诱导耐药性的风险。然而，很少有类似物或修饰衍生物 的天然AMP已在实践中获得批准，大多数失败是由全身或局部毒性引起的 与广谱抗菌活性有关。朝着发现有效的、有选择性的长期目标 AMP 作为针对窄谱特定抗生素耐药病原体的治疗方法，我们的目标是 通过整合创新方法和应用，开发此类发现所需的新能力 机器学习、多尺度建模、肽合成和微生物学。我们开发了第一个 生成对抗网络模型（AMP-GAN），用于生成具有不同序列的 AMP 候选者 结构，以及精确的多尺度模型和方法来研究 AMP 聚集机制 和目标交互。我们的中心假设是 AMP 选择性可以通过控制它们来实现 序列、结构、相互作用、聚合和共聚合。为了实现三个具体目标，建立 一种发现窄谱 AMP 的新方法，我们将 (i) 生成选择性 AMP 序列 具有可预测的活性和病原体目标，(ii) 识别 AMP 以靶向特征生物分子 (iii) 调节 AMP 聚集以调整细胞选择性或实现协同作用。我们将前进 我们的计算技术（如 AMP-GAN 和自上而下的模拟与化学结合） 表征（结构和动力学）和细胞测定（活性和毒性）。我们预计将获得 对如何设计窄谱 AMP 以及如何结合新的 计算和实验工具可实现所需的 AMP 选择性。总的来说，这个贡献可以 意义重大，因为它将为精确 AMP 设计建立新途径，并使更多 AMP 更接近临床 通过克服他们已知的陷阱。由此产生的知识将在科学界广泛共享 AMP 研究和开发。我们的理念和方法具有创新性，因为它们改变了当前的 广谱 AMP 设计范式通过以下方式实现更高的准确性、多样性和目标选择性： 精密放大器设计。总的来说，鉴于对抗生素耐药性治疗方案的需求不断增加 这项拟议研究的方法和工具将使新疗法的发现成为可能 应对具有挑战性的传染病。