Texas A&M University, College Station, TX, 77843, USA.
Baylor University, Waco, TX, 76798, USA.
Small Methods. 2024 Jul;8(7):e2301191. doi: 10.1002/smtd.202301191. Epub 2024 Mar 14.
Amino-acid protein composition plays an important role in biology, medicine, and nutrition. Here, a groundbreaking protein analysis technique that quickly estimates amino acid composition and secondary structure across various protein sizes, while maintaining their natural states is introduced and validated. This method combines multivariate statistics and the thermostable Raman interaction profiling (TRIP) technique, eliminating the need for complex preparations. In order to validate the approach, the Raman spectra are constructed of seven proteins of varying sizes by utilizing their amino acid frequencies and the Raman spectra of individual amino acids. These constructed spectra exhibit a close resemblance to the actual measured Raman spectra. Specific vibrational modes tied to free amino and carboxyl termini of the amino acids disappear as signals linked to secondary structures emerged under TRIP conditions. Furthermore, the technique is used inversely to successfully estimate amino acid compositions and secondary structures of unknown proteins across a range of sizes, achieving impressive accuracy ranging between 1.47% and 5.77% of root mean square errors (RMSE). These results extend the uses for TRIP beyond interaction profiling, to probe amino acid composition and structure.
氨基酸-蛋白质组成在生物学、医学和营养学中具有重要作用。在此,我们引入并验证了一种开创性的蛋白质分析技术,该技术可在保持蛋白质天然状态的同时,快速估计各种大小的蛋白质中的氨基酸组成和二级结构。该方法结合了多元统计和热稳定拉曼相互作用分析(TRIP)技术,无需进行复杂的准备。为了验证该方法,利用七种不同大小的蛋白质的氨基酸频率和单个氨基酸的拉曼光谱构建了拉曼光谱。这些构建的光谱与实际测量的拉曼光谱非常相似。在 TRIP 条件下,当与二级结构相关的信号出现时,与游离氨基酸和羧基末端相关的特定振动模式消失。此外,该技术被反其道而行之,成功地估计了一系列大小的未知蛋白质的氨基酸组成和二级结构,其均方根误差(RMSE)的精度高达 1.47%至 5.77%。这些结果将 TRIP 的用途从相互作用分析扩展到了探测氨基酸组成和结构。