Precision Design of Antimicrobial Peptides Against Bacterial Infections

抗细菌感染抗菌肽的精密设计

• 批准号: 10522451
• 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/10522451
• 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; Membrane Proteins; 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; 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.

项目总结 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。它会导致 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 耐药性正在超过新抗生素的发展，我们必须找到具有成本效益的、创新的方法 寻找与小分子抗生素互补的新的抗菌疗法。抗菌肽 AMPS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。 因为它们通常经历更快的发展，表现出快速的杀戮，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的衍生品 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、有选择性的长期目标前进 AMPS作为治疗药物，针对少数特定的抗生素耐药病原体，我们的目标是 通过集成创新方法和应用程序，开发此类发现所需的新能力 机器学习、多尺度建模、多肽合成和微生物学。我们已经开发出第一个 产生式对抗网络模型(AMP-GAN)，以产生具有不同序列和 结构，以及精确的多尺度模型和方法来研究AMP的聚集机制 和目标交互。这是我们的中心假设，AMP的选择性可以通过控制它们的 序列、结构、相互作用、聚集和共同聚集。在追求三个具体目标的过程中， 一种发现窄谱AMP的新方法，我们将(I)产生选择性AMP序列 具有可预测的活性和病原体靶标，(Ii)识别AMP以靶向 (3)调节AMP聚集以调节细胞选择性或实现协同作用。我们将继续前进 我们的计算技术，如AMP-GaN和自上而下的化学共轭模拟 表征(结构和动力学)和细胞分析(活性和毒性)。我们预计将获得 基本了解如何设计窄谱AMP，以及如何结合新的 计算和实验工具，以实现所需的AMP选择性。总体而言，这种贡献可以是 意义重大，因为它将为精确的AMP设计开辟新的途径，并使更多的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分享 AMP研发。我们的概念和方法是创新的，因为它们改变了当前的 通过以下方式实现更高精度、多样性和目标选择性的广谱AMP设计范例 精密AMP设计。总体而言，鉴于对抗生素耐药性治疗选择的需求日益增加 感染，这项拟议研究的方法和工具将使发现新的治疗方法成为可能 挑战传染病。