Multiscale Modeling of B. Anthracis Surface Layer Assembly and Depolymerization by Nanobodies

纳米抗体对炭疽杆菌表面层组装和解聚的多尺度建模

• 批准号: 10615187
• 负责人: Alexander Pak
• 金额: $ 21.54万
• 依托单位: COLORADO SCHOOL OF MINES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10615187
• 关键词: Address; Adhesions; Adopted; Affinity; Anabolism; Anthrax disease; Antibiotic Resistance; Antibiotic Therapy; Antibiotics; Antibodies; Bacillus anthracis; Bacteria; Bacterial Antibiotic Resistance; Behavior; Binding; Biophysics; Body Surface; Capsid Proteins; Cell Adhesion; Cell Wall; Cell membrane; Centers for Disease Control and Prevention (U.S.); Characteristics; Clostridium difficile; Complementarity Determining Regions; Computer Simulation; Derivation procedure; Engineering; Epitopes; Exhibits; Free Energy; Genetic Programming; Goals; Grain; Health; Health Care Costs; Human; Knowledge; Mediating; Mediation; Microbiology; Modeling; Molecular Conformation; Monoclonal Antibodies; Mutation; Nanoporous; Outcome; Pathogenesis; Protein Biosynthesis; Protein Engineering; Proteins; Protocols documentation; Protomer; Recombinants; Reporting; Rest; Role; Rupture; Sampling; Sequence Homology; Solubility; Stress; Structure; Surface; Tertiary Protein Structure; Testing; Virulence; Virulence Factors; Virus; Work; antibiotic resistant infections; antigen binding; biomacromolecule; cell envelope; cell growth; combat; combinatorial; computer framework; computerized tools; depolymerization; design; experimental study; fighting; fitness; flexibility; immunogenicity; improved; models and simulation; molecular dynamics; mouse model; multi-scale modeling; mutant; nanobodies; pathogen; pathogenic bacteria; prediction algorithm; pressure; prevent; risk mitigation; screening; self assembly; simulation; therapeutic target; two-dimensional

Project Summary Alternative strategies to conventional antibiotics are needed to combat rising antibiotic resistance in bacteria. Therapeutics that target virulence factors would instead disarm bacteria and mitigate the risk of developing antibiotic resistance. In this proposal, we investigate the disruption of bacterial surface layer proteins (SLPs), which self-assemble into a para-crystalline surface layer (S-layer), a virulence factor that mediates bacterial aggregation, adhesion, and protection. Nanobodies, which exhibit low immunogenicity in humans, and are easier to purify and deliver compared to monoclonal antibodies, were recently demonstrated to depolymerize S- layers in the case of Bacillus anthracis, which led to complete survival in mice models under sustained treatment. Our scientific premise is that nanobody-based inhibition of S-layers is a viable antivirulence strategy once tuned for each bacterial pathogen. We propose to leverage our multiscale computer simulation expertise to identify the as-yet unknown mechanism of action and to determine sequence motifs that enhance nanobody-induced S-layer depolymerization in Bacillus anthracis. Our aims include (1) verification that depolymerization is induced by S-layer rigidification through the use of coarse-grained modeling and simulation and (2) determination of nanobodies with improved antivirulence by computationally tailoring existing nanobodies. The computational protocols developed herein are systematic and generalizable beyond Bacillus anthracis. We expect our findings and computational tools to extend to other SLP-expressing bacteria, including urgent antibiotic-resistant threats such as Clostridioides difficile, and aid the global fight against antibiotic-resistant bacteria.

项目摘要 需要传统抗生素的替代策略来对抗日益增长的抗生素 细菌中的抗药性。针对毒力因子的疗法将取而代之解除细菌的武装 并减轻产生抗生素耐药性的风险。在这份提案中，我们调查了 破坏细菌表面层蛋白(SLP)，这些蛋白自组装成类晶体 表层(S层)，一种介导细菌聚集、黏附和 保护。纳米抗体，在人类中表现出低免疫原性，更容易纯化 并与单抗相比，最近被证明能解聚S- 在炭疽芽孢杆菌的情况下，这导致了在小鼠模型中完全存活 持续治疗。我们的科学前提是，基于纳米体的S层的抑制是一种 可行的抗病毒策略曾经针对每种细菌病原体进行了调整。我们建议利用 我们的多尺度计算机模拟专业知识，以确定迄今未知的机制 作用并确定增强纳米体诱导的S层的序列基序 炭疽芽孢杆菌的解聚作用。我们的目标包括(1)验证解聚是 通过使用粗粒度建模和模拟，诱导S-层硬化 (2)通过计算裁剪现有的纳米体来确定具有改进的抗病毒力的纳米体 纳米物体。这里开发的计算协议是系统的和可推广的 超越了炭疽芽孢杆菌。我们希望我们的发现和计算工具能够扩展到其他 表达SLP的细菌，包括紧急的抗生素耐药性威胁，如梭状芽胞杆菌 艰难梭菌，并帮助全球抗击抗药性细菌。