Method for predicting ligand-protein interaction based on quantum calculation

A protein interaction and quantum computing technology, which is applied in quantum computers, calculations, molecular design, etc., can solve problems such as difficult predictions, large volumes of proteins with large calculations, and complex spatial structures.

Active Publication Date: 2022-05-06
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] The purpose of the present invention is to solve the problems of large amount of calculation that cannot be solved by traditional computers and the problems of large protein volume, complex spatial structure, and difficult predictio

Method used

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  • Method for predicting ligand-protein interaction based on quantum calculation
  • Method for predicting ligand-protein interaction based on quantum calculation
  • Method for predicting ligand-protein interaction based on quantum calculation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0061] Provide a prediction method based on quantum computing for ligand-protein interactions, such as figure 1 As shown, the method includes:

[0062] Obtain the 3D structures of ligand drug molecules and protein molecules;

[0063] Use the quantum hidden Markov model to predict the 3D structure of the protein molecule, and obtain the possible binding sites on the protein molecule;

[0064] Docking the ligand drug molecule with the binding site, and using a quantum convolutional neural network to extract features that affect ligand-protein docking;

[0065] The docking process is scored according to the characteristics affecting ligand-protein docking, and the effect of ligand-protein interaction is predicted.

[0066] Specifically, the 3D structure of the drug molecule and the 3D structure of the protein molecule are respectively obtained by methods such as crystallography or spectroscopy for prediction training. Then use the quantum hidden Markov model combined with the ...

Embodiment 2

[0071] Based on Example 1, a method for predicting ligand-protein interaction based on quantum computing is provided, wherein the obtaining of the 3D structure of the ligand drug molecule and the protein molecule includes:

[0072] Obtain the three-dimensional crystal structure of protein molecules by means of X-ray crystallography or NMR spectroscopy;

[0073] Obtain the 3D structures of a large number of ligand drug molecules with the help of a large database of small molecule 3D structures.

[0074] Further, said obtaining the 3D structure of the ligand drug molecule and the protein molecule also includes:

[0075] Repair the errors generated during the protein analysis process, and perform hydrogenation protonation on the three-dimensional crystal structure of the protein molecule before docking, mark the local electrical properties, and wait for the next step;

[0076] Energy minimization is performed on the 3D structure of the ligand drug molecule.

[0077] Further, as...

Embodiment 3

[0100] This embodiment has the same inventive concept as Embodiment 1. On the basis of Embodiment 1, a storage medium is provided on which computer instructions are stored. When the computer instructions are running, a quantum computing-based Steps in a predictive method for ligand-protein interactions.

[0101] Based on this understanding, the technical solution of this embodiment is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium. Several instructions are included to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), ...

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Abstract

The invention discloses a ligand-protein interaction prediction method based on quantum computing, and belongs to the field of quantum computing and computational biology, and the ligand-protein interaction prediction method comprises the following steps: obtaining a 3D structure of a ligand drug molecule and a protein molecule; predicting the 3D structure of the protein molecule by using a quantum hidden Markov model to obtain a binding site possibly existing on the protein molecule; docking ligand drug molecules with binding sites, and extracting characteristics affecting ligand-protein docking by using a quantum convolutional neural network; the docking process is scored according to the characteristics affecting ligand-protein docking, and the ligand-protein interaction effect is obtained through prediction. Related information of molecular docking is described based on the quantum convolutional neural network, the quantum hidden Markov model is used for predicting binding sites possibly existing on receptor protein, synchronous prediction of molecular docking and protein structures is achieved, meanwhile, quantum calculation is combined with the field of biological medicine, and the difficulty of protein 3D structure prediction is solved.

Description

technical field [0001] The invention relates to the fields of quantum computing and computational biology, in particular to a method for predicting ligand-protein interactions based on quantum computing. Background technique [0002] As an emerging technology in recent decades, quantum computing has been applied in fields such as finance, chemistry, biology, pharmaceuticals and materials. In classical machine learning, both convolutional neural networks and hidden Markov models play a very important role and have an unshakable position in many fields. With the emergence of quantum computers, the potential of quantum computing has gradually been discovered, and the quantization of these classical algorithms has both practical significance and practical value. Quantum convolutional neural network has been proposed in recent years, which has great potential in image processing and will not appear barren plateau phenomenon; classical hidden Markov has achieved results in molecu...

Claims

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Application Information

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IPC IPC(8): G16B5/00G16B20/30G16B40/00G06N10/60G16C20/50G16C20/70
CPCG16B5/00G16B20/30G16B40/00G06N10/00G16C20/50G16C20/70Y02A90/10
Inventor 朱钦圣蒋欣睿卢俊邑殷浩吴昊胡勇李晓瑜
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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