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.

项目概要 需要传统抗生素的替代策略来应对不断增长的抗生素 细菌的耐药性。针对毒力因子的治疗反而会解除细菌的武装 并降低产生抗生素耐药性的风险。在本提案中，我们调查了 破坏细菌表面层蛋白 (SLP)，使其自组装成准晶状体 表面层（S 层），介导细菌聚集、粘附和传播的毒力因子 保护。纳米抗体在人类中表现出低免疫原性，并且更容易纯化 与单克隆抗体相比，最近被证明可以解聚 S- 在炭疽杆菌的情况下，其导致小鼠模型完全存活 持续治疗。我们的科学前提是，基于纳米抗体的 S 层抑制是一种 一旦针对每种细菌病原体进行了调整，就会产生可行的抗毒策略。我们建议利用杠杆 我们的多尺度计算机模拟专业知识可以识别迄今为止未知的机制 作用并确定增强纳米抗体诱导的 S 层的序列基序 炭疽杆菌中的解聚。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由 S 层刚性化引起 (2) 通过计算定制现有的纳米抗体来确定具有改进的抗毒力 纳米抗体。本文开发的计算协议是系统性的且可推广的 超越炭疽杆菌。我们希望我们的发现和计算工具能够扩展到其他领域 表达 SLP 的细菌，包括梭菌等紧急抗生素耐药威胁 艰难梭菌，并帮助全球对抗抗生素耐药细菌。