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

项目摘要 需要替代传统抗生素的策略来对抗不断上升的抗生素 细菌的抵抗力。靶向毒力因子的治疗方法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），其自组装成准晶体 表面层（S-层），介导细菌聚集、粘附和 保护纳米抗体，在人体中表现出低免疫原性，并且更容易纯化 与单克隆抗体相比，最近证明可以去除S- 在炭疽杆菌的情况下，这导致小鼠模型在 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，以确定尚未未知的机制， 作用，并确定增强纳米抗体诱导的S层的序列基序 在炭疽芽孢杆菌中解聚。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， (2)通过计算调整现有的纳米抗体来确定具有改进的抗毒性的纳米抗体 纳米抗体本文所开发的计算协议是系统的和可推广的 除了炭疽杆菌我们希望我们的研究结果和计算工具可以扩展到其他领域。 表达SLP的细菌，包括紧迫的抗药性威胁，如梭菌 艰难梭菌，并帮助全球对抗耐药细菌。

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.