基于粗粒化模型的半经验蛋白质SERS光谱解析算法

基于表面增强拉曼光谱（SERS）直接解析生理环境下蛋白质的结构，对蛋白质功能研究、生物医药等领域的发展有显著的推动作用。然而传统理论计算方法由于难以描述梯度分布局域电场与蛋白质分子复杂的相互作用，尚无法解析蛋白SERS光谱。本项目提出一种半经验蛋白质SERS光谱解析算法，创新性地将蛋白质结构简化为氨基酸的空间排列组合，将氨基酸本身在局域电场中的张量特性作为标准经验值，从而将原本复杂的积分迭代运算转化为简单的矩阵运算，可以克服上述难题，节约计算时间；在前期工作上继续发展蛋白质可控吸附实验方法以获得可靠的蛋白质SERS光谱，与算法相互验证；进而结合分子动力学模拟和概率分析实现蛋白质SERS光谱解析。基于此算法，SERS技术将突破原本局限于小分子体系界面研究的限制，应用于研究界面蛋白质体系动力学过程。也可根据SERS光谱推测蛋白在基底上可能的吸附结构，为结构生物学提供新的研究手段。

Direct analysis of protein structure in the physiological environment by surface-enhanced Raman spectroscopy (SERS) would significantly facilitate protein function research, development of biomedicine, and other fields. However, the traditional theoretical method fails to describe the complex interaction between gradient local electric fields and proteins. So far, interpretation of the SERS spectra of protein has not been resolved. This project proposes a new algorithm for interpreting SERS spectra, which introduces spatial arrangement and combination of amino acids to simplify the conformation of proteins at interfaces. The tensor nature of amino acids in the local electric field under standard orientation is considered the standard empirical value. The integral and iterative operation for calculating SERS spectra can thus be simplified as simple matrix calculations, which overcomes the problem mentioned above and saves a lot of computing time. Based on previous work, this project continues developing methods for controlled immobilization of protein to obtain reliable SERS spectra, which can be mutually verified with the algorithm. Finally, the interpretation can be revealed with combination of molecular dynamics and probability analysis. Based on the proposed algorithm, SERS would not be limited to the small molecular interfacial system and competent for exploring interfacial protein dynamics. Moreover, the possible adsorption structure of a protein can be predicted based on its SERS spectra, which provides a new approach for structural biology.