Institute for Bioscience and Biotechnology Research, National Institute of Standards and Technology, 9600 Gudelsky Dr. Rockville, MD 20850 USA.
Higher Order Structure, Attribute Sciences, Amgen, Inc. One Amgen Center Dr., Thousand Oaks, CA 91320 USA.
J Pharm Sci. 2021 Oct;110(10):3385-3394. doi: 10.1016/j.xphs.2021.06.027. Epub 2021 Jun 21.
The one-dimensional (1D) diffusion edited proton NMR method, Protein Fingerprint by Lineshape Enhancement (PROFILE) has been demonstrated to be suitable for higher order structure (HOS) characterization of protein therapeutics including monoclonal antibodies. Recent reports in the literature have demonstrated its advantages for HOS characterization over traditional methods such as circular dichroism and Fourier-transform infrared spectroscopy. Previously, we have demonstrated that the PROFILE method is complementary with high resolution 2D methyl correlated NMR methods and how both may be deployed as a multi-modal platform to further the utility of NMR for HOS characterization. A major limitation of the PROFILE method remains its need for high signal to noise data due to its reliance on convolution difference processing and linear correlation metrics to assess spectral similarity. Here we present an alternative method for analyzing 1D diffusion edited spectra, which overcomes this limitation by using nonlinear iterative partial least squares (NIPALS) principal component analysis, and which we dub PROtein Fingerprint Observed Using NIPALS Decomposition (PROFOUND). We demonstrate that results from the PROFOUND method are robust with respect to instrument, operator and in the presence of high experimental noise and how it may be employed to provide quantitative assessment of spectral similarity.
一维（1D）扩散编辑质子 NMR 方法，即通过谱线形状增强进行蛋白质指纹分析（PROFILE），已被证明适用于包括单克隆抗体在内的蛋白质治疗药物的高阶结构（HOS）特征描述。文献中的最新报道表明，与传统方法（如圆二色性和傅里叶变换红外光谱）相比，该方法在 HOS 特征描述方面具有优势。此前，我们已经证明 PROFILE 方法与高分辨率 2D 甲基相关 NMR 方法互补，并且可以将两者部署为多模态平台，以进一步提高 NMR 用于 HOS 特征描述的实用性。PROFILE 方法的一个主要限制仍然是由于其依赖卷积差处理和线性相关度量来评估光谱相似性，因此需要高信噪比数据。在这里，我们提出了一种用于分析 1D 扩散编辑光谱的替代方法，该方法通过使用非线性迭代偏最小二乘（NIPALS）主成分分析来克服这一限制，我们将其称为使用 NIPALS 分解进行蛋白质指纹分析（PROFOUND）。我们证明了 PROFOUND 方法的结果在仪器、操作人员和存在高实验噪声的情况下具有稳健性，以及如何利用它来提供光谱相似性的定量评估。
