Center of Basic Molecular Science, Department of Chemistry, Tsinghua University, Beijing 100084, China.
J Chem Inf Model. 2024 Jun 10;64(11):4410-4418. doi: 10.1021/acs.jcim.4c00354. Epub 2024 May 23.
Protein p is a fundamental physicochemical parameter that dictates protein structure and function. However, accurately determining protein site-p values remains a substantial challenge, both experimentally and theoretically. In this study, we introduce a physical organic approach, leveraging a protein structural and physical-organic-parameter-based representation (P-SPOC), to develop a rapid and intuitive model for protein p prediction. Our P-SPOC model achieves state-of-the-art predictive accuracy, with a mean absolute error (MAE) of 0.33 p units. Furthermore, we have incorporated advanced protein structure prediction models, like AlphaFold2, to approximate structures for proteins lacking three-dimensional representations, which enhances the applicability of our model in the context of structure-undetermined protein research. To promote broader accessibility within the research community, an online prediction interface was also established at isyn.luoszgroup.com.
蛋白质 p 是决定蛋白质结构和功能的基本物理化学参数。然而,准确地确定蛋白质的 p 值在实验和理论上都是一个巨大的挑战。在这项研究中,我们引入了一种物理有机方法,利用基于蛋白质结构和物理有机参数的表示(P-SPOC),开发了一种快速直观的蛋白质 p 值预测模型。我们的 P-SPOC 模型实现了最先进的预测准确性,平均绝对误差(MAE)为 0.33 个 p 单位。此外,我们还整合了先进的蛋白质结构预测模型,如 AlphaFold2,来近似缺乏三维结构的蛋白质的结构,这提高了我们的模型在结构不确定的蛋白质研究中的适用性。为了在研究社区内促进更广泛的可访问性,我们还在 isyn.luoszgroup.com 上建立了一个在线预测接口。