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AI Predicts "Optical Fingerprint" of Protein

Jun 12, 2019     Email"> PrintText Size

Protein is the cornerstone of life. The function of organism depends on the stable and flexible protein structure. Spectral response signals of proteins can be called fingerprints of protein skeleton which can reveal the precise protein structure through theoretical simulation. However, the structure of proteins is extremely complex and changeable which requires a large number of high-precision theoretical calculations of quantum chemistry.
Therefore, the theoretical interpretation of protein spectra is a long-term difficulty and challenge, which limits the accurate analysis of spectra and the discovery of protein structure. How to avoid too expensive quantum chemical calculations and interpret the optical fingerprints of protein skeleton in the simulation of spectral theory is an important scientific topic. with chemical connotations using the random forest method. The combination of AI and quantum chemistry provides an efficient tool for predicting the optical properties of proteins.

Artificial intelligence (AI) technology has been widely used in various fields to reduce the computational complexity. Recently, Prof. JIANG Jun from Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China (USTC) of the Chinese Academy of Sciences, cooperating with Prof. LUO Yi from USTC and Prof. Shaul Mukamel from University of California, Irvine, established the structure-property relationship between the structure and properties of protein-peptide bonds by using the neural network technology of AI machine learning.

Their finding, published in PNAS, reduced the computational complexity by tens of thousands of times. Also, they successfully predicted the ultraviolet spectra of peptide bonds, and revealed the structure descriptors and structure-property relationships

Researchers first obtained 50,000 groups of peptide bond model molecules with different configurations by molecular dynamics simulation and quantum chemistry calculation at 300 K.

Bond length, bond angle, dihedral angle and charge information are selected as descriptors by machine learning algorithm. The structure-property relationship between the ground state structure of peptide bond and its excited state properties was established by big data training with neural network. Based on the trained machine learning model, the ground state dipole moments and excited state properties of the peptide bonds are predicted. The ultraviolet absorption spectra of the peptide bonds are then predicted.

In order to verify the robustness and transferability of the machine learning model, the ultraviolet absorption spectra of peptide bonds at 200 K and 400 K were predicted based on the machine learning model obtained at 300 K. The results are in good agreement with simulations using the time-dependent density-functional theory (TDDFT).

This is the first time that AI technology has been applied to theoretical calculation and prediction of protein spectroscopy. A large number of data are obtained through theoretical calculation, and AI is used to train and establish the structure-property relationship. The final model is used for prediction, which provides a new idea for simulating the spectrum of proteins.

This study establishes the feasibility and advantages of machine learning to simulate the ultraviolet absorption spectra of protein peptide bond skeleton, and interpretation of optical fingerprints of protein will become easier and more effective.

Prof. JIANG's team has devoted themselves to developing the application of machine learning technology in the field of quantification, making it an important tool to solve quantification problems.

(Editor: LIU Jia)

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