Sgrignani Jacopo, Buscarini Sara, Locatelli Patrizia, Guerra Concetta, Furlan Alberto, Chen Yingyi, Zoppi Giada, Cavalli Andrea
Institute for Research in Biomedicine (IRB), Università della Svizzera Italiana (USI), Bellinzona, Switzerland.
Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
Front Immunol. 2025 Sep 12;16:1631868. doi: 10.3389/fimmu.2025.1631868. eCollection 2025.
Lipocalin-2 (LCN2) is an acute-phase glycoprotein whose upregulation is associated with blood-brain-barrier breakdown and neuroinflammation, making it a potential diagnostic and therapeutic target.
We developed an end-to-end, AI-guided workflow to design de-novo miniproteins targeting LCN2. Backbone scaffolds were generated using RFdiffusion, sequences optimized with ProteinMPNN, and candidates filtered in silico based on AlphaFold2 confidence metrics (mean interface pAE < 10) and binding free energy predicted by Prodigy. From an initial library of 10,000 designs, five candidates were expressed and purified from E. coli. Binding affinities were assessed using biolayer interferometry (BLI), and structural interactions were analyzed via computational modeling.
BLI identified MiniP-2 as the lead construct, exhibiting a dissociation constant (Kd) of 4.2 nM. Structural modeling indicated that binding is primarily mediated by backbone hydrogen bonds, complemented by a stabilizing salt bridge between Arg37 of MiniP-2 and Asp97 of LCN2. Surface plasmon resonance (SPR) competition experiments demonstrated that MiniP-2 inhibits LCN2 binding to MMP-9, highlighting its potential to interfere with pathological LCN2 interactions.
These results demonstrate that a fully computational generative workflow can yield nanomolar LCN2 binders in a single design-build-test cycle. MiniP-2 represents a promising starting point for affinity maturation, structural studies, and in vivo evaluation as an imaging probe or antagonist of LCN2-mediated signaling.
脂质运载蛋白-2（LCN2）是一种急性期糖蛋白，其上调与血脑屏障破坏和神经炎症相关，使其成为一个潜在的诊断和治疗靶点。
我们开发了一种端到端的、人工智能引导的工作流程，用于设计靶向LCN2的全新微型蛋白质。使用RFdiffusion生成主链支架，用ProteinMPNN优化序列，并基于AlphaFold2置信度指标（平均界面pAE < 10）和Prodigy预测的结合自由能在计算机上筛选候选物。从10000个设计的初始文库中，选择了5个候选物在大肠杆菌中表达和纯化。使用生物层干涉术（BLI）评估结合亲和力，并通过计算建模分析结构相互作用。
BLI确定MiniP-2为主要构建体，其解离常数（Kd）为4.2 nM。结构建模表明，结合主要由主链氢键介导，MiniP-2的Arg37与LCN2的Asp97之间的稳定盐桥起到补充作用。表面等离子体共振（SPR）竞争实验表明，MiniP-2抑制LCN2与MMP-9的结合，突出了其干扰LCN2病理相互作用的潜力。
这些结果表明，一个完全基于计算生成的工作流程可以在单个设计-构建-测试周期中产生纳摩尔级的LCN2结合剂。MiniP-2是亲和力成熟、结构研究以及作为LCN2介导信号的成像探针或拮抗剂进行体内评估的一个有前景的起点。
