Cryoelectron microscope atomic model structure building method and system based on deep learning and application

A cryo-electron microscope and atomic model technology, applied in the field of structural biology, can solve the problems of position deviation of the main chain and lack of side chain density, etc., and achieve the effects of fast reasoning, strong feature learning ability, and strong generalization ability

Pending Publication Date: 2022-01-28
TSINGHUA UNIV
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Problems solved by technology

However, in the density map with lower resolution, due to the missing or low side chain density, t

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  • Cryoelectron microscope atomic model structure building method and system based on deep learning and application
  • Cryoelectron microscope atomic model structure building method and system based on deep learning and application
  • Cryoelectron microscope atomic model structure building method and system based on deep learning and application

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Embodiment Construction

[0091] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0092] In the description of this specification, descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific examples", or "some examples" mean that specific features described in connection with the embodiment or example , structure, material or characteristic is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the spe...

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Abstract

The invention discloses a cryoelectron microscope atomic model structure building method and system based on deep learning and application. The method comprises the steps of: 1, obtaining a cryoelectron microscope density map data set, and carrying out model training and model testing; 2, inputting a cryoelectron microscope density map and a corresponding amino acid sequence; and 3, carrying out feature coding and extraction on the cryoelectron microscope density map and the corresponding amino acid sequence, and building an atomic structure model. According to the measurement method provided by the invention, the generated amino acid atom model has structural biological characteristics, the structural biological rationality of the predicted amino acid atom model is ensured, accurate prediction of the end-to-end all-differentiable amino acid internal atom structure is finally realized, certain superiority is achieved, and the atomic model effect predicted by a plurality of tests is verified. In addition, the improvement effect in model building in middle and low resolution is also very obvious.

Description

technical field [0001] The invention belongs to the technical field of structural biology, and in particular relates to a cryo-electron microscope atomic model structure building method, system and application, in particular to a deep learning-based cryo-electron microscope atomic model structure building method, system and application. Background technique [0002] In traditional machine learning, the execution process of a task often consists of many modules. It is generally divided into multiple independent steps such as data preprocessing, feature extraction, model training, and result post-processing, which are integrated and executed to realize automatic operation. However, the quality of the result of each step will affect each subsequent step, thus affecting the quality of the final result to a certain extent. In addition, for supervised learning, the label used for supervision will contribute to each link, but because the intermediate steps are too independent, a l...

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Application Information

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IPC IPC(8): G16B5/00G16B30/00G06T3/40G06N3/08G06K9/62G06N3/04G06F30/27G06F111/04
CPCG16B5/00G06F30/27G16B30/00G06T3/4007G06N3/08G06F2111/04G06N3/045G06F18/241
Inventor 张强锋徐魁徐静乐
Owner TSINGHUA UNIV
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