EaStCHEM School of Chemistry, Biomedical Sciences Research Complex, and Centre of Magnetic Resonance, University of St Andrews North Haugh, St Andrews KY16 9ST, UK.
School of Life Sciences, University of Dundee, Medical Sciences Institute, Dundee, DD1 5EH, UK.
Phys Chem Chem Phys. 2021 Feb 19;23(6):3810-3819. doi: 10.1039/d0cp06196d.
Electron paramagnetic resonance (EPR) distance measurements are making increasingly important contributions to studies of biomolecules underpinning health and disease by providing highly accurate and precise geometric constraints. Combining double-histidine (dH) motifs with CuII spin labels shows promise for further increasing the precision of distance measurements, and for investigating subtle conformational changes. However, non-covalent coordination-based spin labelling is vulnerable to low binding affinity. Dissociation constants of dH motifs for CuII-nitrilotriacetic acid were previously investigated via relaxation induced dipolar modulation enhancement (RIDME), and demonstrated the feasibility of exploiting the dH motif for EPR applications at sub-μM protein concentrations. Herein, the feasibility of using modulation depth quantitation in CuII-CuII RIDME to simultaneously estimate a pair of non-identical independent KD values in such a tetra-histidine model protein is addressed. Furthermore, we develop a general speciation model to optimise CuII labelling efficiency, depending upon pairs of identical or disparate KD values and total CuII label concentration. We find the dissociation constant estimates are in excellent agreement with previously determined values, and empirical modulation depths support the proposed model.
电子顺磁共振(EPR)距离测量通过提供高度准确和精确的几何约束,对支持健康和疾病的生物分子研究做出了越来越重要的贡献。通过将双组氨酸(dH)基序与 CuII 自旋标记物结合,可以进一步提高距离测量的精度,并研究细微的构象变化。然而,基于非共价配位的自旋标记物易受到结合亲和力低的影响。先前通过弛豫诱导偶极调制增强(RIDME)研究了 dH 基序与 CuII-氮三乙酸的离解常数,证明了在亚微摩尔蛋白质浓度下利用 dH 基序进行 EPR 应用的可行性。在此,我们探讨了在 CuII-CuII RIDME 中使用调制深度定量来同时估计这种四组氨酸模型蛋白中一对非相同独立 KD 值的可行性。此外,我们开发了一种通用的配体模型,根据相同或不同的 KD 值对和总 CuII 标记浓度来优化 CuII 标记效率。我们发现离解常数的估计值与先前确定的值非常吻合,经验性的调制深度支持所提出的模型。