中国科学技术大学江俊团队报道了基于人工智能的酰胺-II红外光谱模拟监测蛋白质氢键动力学。相关研究成果于2024年1月19日发表于国际顶尖学术期刊《美国化学会杂志》。

蛋白质的结构敏感的酰胺II红外（IR）带提供了关于蛋白质二级结构的氢键的有价值的信息，这对于理解蛋白质动力学和相关功能至关重要。然而，从实验酰胺II光谱中破译蛋白质结构依赖于对溶剂水中数万种代表性构型进行耗时的量子化学计算。目前，准确模拟整个蛋白质的酰胺II光谱仍然是一个挑战。

该文中，研究人员提出了一种基于机器学习（ML）的协议，旨在有效模拟各种蛋白质的酰胺II IR光谱，其精度与实验结果相当。该方案是研究蛋白质动力学的一种经济高效的替代方案，包括鉴定二级结构和监测不同pH条件下和蛋白质折叠过程中蛋白质氢键的动力学。

该方法在蛋白质研究领域提供了一个有价值的工具，重点是使用酰胺II红外光谱研究蛋白质的动态特性，特别是与氢键有关的动态特性。

附：英文原文

Title: Artificial Intelligence-based Amide-II Infrared Spectroscopy Simulation for Monitoring Protein Hydrogen Bonding Dynamics

Author: Sheng Ye, Kai Zhong, Yan Huang, Guozhen Zhang, Changyin Sun, Jun Jiang

Issue&Volume: January 19, 2024

Abstract: The structurally sensitive amide II infrared (IR) bands of proteins provide valuable information about the hydrogen bonding of protein secondary structures, which is crucial for understanding protein dynamics and associated functions. However, deciphering protein structures from experimental amide II spectra relies on time-consuming quantum chemical calculations on tens of thousands of representative configurations in solvent water. Currently, the accurate simulation of amide II spectra for whole proteins remains a challenge. Here, we present a machine learning (ML)-based protocol designed to efficiently simulate the amide II IR spectra of various proteins with an accuracy comparable to experimental results. This protocol stands out as a cost-effective and efficient alternative for studying protein dynamics, including the identification of secondary structures and monitoring the dynamics of protein hydrogen bonding under different pH conditions and during protein folding process. Our method provides a valuable tool in the field of protein research, focusing on the study of dynamic properties of proteins, especially those related to hydrogen bonding, using amide II IR spectroscopy.

DOI: 10.1021/jacs.3c12258

Source: https://pubs.acs.org/doi/abs/10.1021/jacs.3c12258