Cecil Adam J, Sogues Adrià, Gurumurthi Mukund, Lane Kaylee S, Remaut Han, Pak Alexander J
Department of Chemical and Biological Engineering, Colorado School of Mines, Golden, CO 80401, USA.
Structural and Molecular Microbiology, VIB-VUB Center for Structural Biology, Pleinlaan 2, 1050 Brussels, Belgium.
PNAS Nexus. 2024 Nov 26;3(12):pgae538. doi: 10.1093/pnasnexus/pgae538. eCollection 2024 Dec.
Nanobody (Nb)-induced disassembly of surface array protein (Sap) S-layers, a two-dimensional paracrystalline protein lattice from , has been presented as a therapeutic intervention for lethal anthrax infections. However, only a subset of existing Nbs with affinity to Sap exhibit depolymerization activity, suggesting that affinity and epitope recognition are not enough to explain inhibitory activity. In this study, we performed all-atom molecular dynamics simulations of each Nb bound to the Sap binding site and trained a collection of machine learning classifiers to predict whether each Nb induces depolymerization. We used feature importance analysis to filter out unnecessary features and engineered remaining features to regularize the feature landscape and encourage learning of the depolymerization mechanism. We find that, while not enforced in training, a gradient-boosting decision tree is able to reproduce the experimental activities of inhibitory Nbs while maintaining high classification accuracy, whereas neural networks were only able to discriminate between classes. Further feature analysis revealed that inhibitory Nbs restrain Sap motions toward an inhibitory conformational state described by domain-domain clamping and induced twisting of domains normal to the lattice plane. We believe these motions drive Sap lattice depolymerization and can be used as design targets for improved Sap-inhibitory Nbs. Finally, we expect our method of study to apply to S-layers that serve as virulence factors in other pathogens, paving the way forward for Nb therapeutics that target depolymerization mechanisms.
纳米抗体（Nb）诱导表面阵列蛋白（Sap）S层（一种来自[具体来源未提及]的二维准晶蛋白晶格）的解体，已被提出作为治疗致命炭疽感染的一种干预措施。然而，现有的对Sap具有亲和力的纳米抗体中只有一部分表现出解聚活性，这表明亲和力和表位识别不足以解释抑制活性。在本研究中，我们对与Sap结合位点结合的每个纳米抗体进行了全原子分子动力学模拟，并训练了一组机器学习分类器来预测每个纳米抗体是否会诱导解聚。我们使用特征重要性分析来滤除不必要的特征，并对剩余特征进行工程处理，以规范特征格局并促进对解聚机制的学习。我们发现，虽然在训练中没有强制要求，但梯度提升决策树能够在保持高分类准确率的同时重现抑制性纳米抗体的实验活性，而神经网络只能区分不同类别。进一步的特征分析表明，抑制性纳米抗体将Sap的运动限制在一种由结构域-结构域钳制和垂直于晶格平面的结构域诱导扭曲所描述的抑制性构象状态。我们认为这些运动驱动了Sap晶格的解聚，并可作为改进的Sap抑制性纳米抗体的设计靶点。最后，我们期望我们的研究方法能够应用于在其他病原体中作为毒力因子的S层，为靶向解聚机制的纳米抗体治疗铺平道路。
