CN114580603B - Method for constructing single-particle level energy curved surface based on data of cryoelectron microscope - Google Patents

Method for constructing single-particle level energy curved surface based on data of cryoelectron microscope Download PDF

Info

Publication number
CN114580603B
CN114580603B CN202210484308.2A CN202210484308A CN114580603B CN 114580603 B CN114580603 B CN 114580603B CN 202210484308 A CN202210484308 A CN 202210484308A CN 114580603 B CN114580603 B CN 114580603B
Authority
CN
China
Prior art keywords
particle
dimensional
data
electron density
density map
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202210484308.2A
Other languages
Chinese (zh)
Other versions
CN114580603A (en
Inventor
韩旭
吴赵龙
毛有东
欧阳颀
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Peking University
Original Assignee
Peking University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Peking University filed Critical Peking University
Priority to CN202210484308.2A priority Critical patent/CN114580603B/en
Publication of CN114580603A publication Critical patent/CN114580603A/en
Application granted granted Critical
Publication of CN114580603B publication Critical patent/CN114580603B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/002—Biomolecular computers, i.e. using biomolecules, proteins, cells
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Artificial Intelligence (AREA)
  • General Physics & Mathematics (AREA)
  • Biophysics (AREA)
  • General Engineering & Computer Science (AREA)
  • Evolutionary Computation (AREA)
  • Computing Systems (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Computational Linguistics (AREA)
  • Biomedical Technology (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Molecular Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Chemical & Material Sciences (AREA)
  • Organic Chemistry (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Analysing Materials By The Use Of Radiation (AREA)

Abstract

The invention discloses a method for constructing a single-particle level energy curved surface based on cryoelectron microscope data, and belongs to the crossing field of data science and biology. The energy curved surface of the single particle level is obtained through grouping classification of single particle data, low-dimensional manifold mapping of a three-dimensional electron density map data set, similarity calculation of a single particle image and a corresponding projection image thereof, and training and prediction of a convolutional neural network. The single-particle horizontal energy curved surface obtained by the invention can visually reflect the conformation distribution of biomolecules, expands the multi-conformation analysis mode of the data of the cryoelectron microscope and improves the robustness of the data processing result to high noise.

Description

Method for constructing single-particle level energy curved surface based on data of cryoelectron microscope
Technical Field
The invention provides a method for constructing a single-particle-level energy curved surface by designing a machine learning algorithm through data of a cryoelectron microscope, and belongs to the crossing field of data science and biology.
Background
The technique of electron freezing microscope is one of the important means for analyzing the structure of biological macromolecules and compounds. And (3) rapidly freezing the purified protein, dispersing the protein in a thin ice layer, carrying out data acquisition under an electron microscope to obtain two-dimensional projections of each single particle, and then carrying out three-dimensional reconstruction to obtain an electron density map of a three-dimensional structure. The technology can analyze the high-resolution structure of the biological molecules under the condition close to the natural physiological state, and is beneficial to researching the working mechanism of a complex protein machine.
Biomolecules generally have intrinsic flexibility, so dynamic structural changes and conformational heterogeneity of biomolecules have been one of the key points of research in structural biology. In the crystalline state, the structural changes of the biomolecules are lattice constrained, typically providing only a static structure and limited kinetic parameters. The advantage of cryoelectron microscopy over crystallography is that it is possible to capture various states of biomolecules in solution and record projections from different angles under different conformations. Cryo-electron microscopy data therefore provide the basis for multi-conformation analysis of biomolecules. In the field of data processing of a cryoelectron microscope, some existing algorithms classify multi-conformations through clustering analysis, maximum likelihood analysis and other methods, but variation differences of biomolecular components and conformations need to be checked for reasonableness through other technologies.
Disclosure of Invention
The invention aims to provide a method for constructing an energy curved surface at a single particle level through data of a cryoelectron microscope, which is used for solving the problem of high-precision description of the conformation distribution of biomolecules.
The method of the invention comprises the following steps:
a method for constructing an energy curved surface at a single particle level based on cryoelectron microscope data is disclosed, and the method comprises the following steps as shown in figure 1:
A. dividing the image data of the cryoelectron microscope into a plurality of groups of single-particle images, respectively carrying out three-dimensional classification on each group of single-particle images, and reconstructing to generate a series of three-dimensional electron density map data sets;
B. the method for realizing the low-dimensional manifold embedding of the three-dimensional electron density map data set mapped by adopting the deep manifold learning algorithm comprises the following steps:
B1. extracting the structure detail characteristics of each three-dimensional electron density map by using a depth self-coding network;
B2. processing the three-dimensional electron density map data set and the detail characteristics thereof through manifold learning to obtain the low-dimensional manifold coordinates of each three-dimensional electron density map
Figure 152297DEST_PATH_IMAGE001
；
C. For each single particle image
Figure 122527DEST_PATH_IMAGE002
（
Figure 415099DEST_PATH_IMAGE003
Is an index of a single-particle image,
Figure 383055DEST_PATH_IMAGE004
is a pixel index), calculates its angle projection image corresponding to the three-dimensional electron density map
Figure 807083DEST_PATH_IMAGE005
The realization method comprises the following steps:
C1. taking out each single particle image
Figure 253108DEST_PATH_IMAGE002
Obtaining the projection image of the three-dimensional electron density map at the angle
Figure 649455DEST_PATH_IMAGE005
；
C2. Single particle image
Figure 788312DEST_PATH_IMAGE002
Projection image corresponding thereto
Figure 653631DEST_PATH_IMAGE005
Degree of similarity therebetween
Figure 231243DEST_PATH_IMAGE006
The specific expression of (a) is defined as:
Figure 747675DEST_PATH_IMAGE007
in the formula (I), the compound is shown in the specification,
Figure 57433DEST_PATH_IMAGE008
and
Figure 138610DEST_PATH_IMAGE009
respectively representing single-grain images
Figure 192016DEST_PATH_IMAGE002
Projection image corresponding thereto
Figure 562955DEST_PATH_IMAGE005
The pixel average value of (1). The degree of similarity
Figure 43615DEST_PATH_IMAGE006
Has a value range of [0,1 ]]，
Figure 336056DEST_PATH_IMAGE006
The larger the size of the single particle image
Figure 521049DEST_PATH_IMAGE002
Projection image corresponding thereto
Figure 215336DEST_PATH_IMAGE005
The more similar. For each single particle image
Figure 148788DEST_PATH_IMAGE002
Calculating a single-particle image
Figure 194104DEST_PATH_IMAGE002
Corresponding projected image
Figure 854893DEST_PATH_IMAGE005
Degree of similarity of
Figure 731582DEST_PATH_IMAGE006
；
D. Designing a convolutional neural network, and training the convolutional neural network by using the whole single-particle data set, namely the convolutional neural network is used for learning single-particle image data
Figure 819624DEST_PATH_IMAGE002
Corresponding to the low-dimensional manifold coordinates of the three-dimensional electron density map
Figure 368548DEST_PATH_IMAGE010
Obtaining a single particle image
Figure 301869DEST_PATH_IMAGE002
Low dimensional manifold coordinate mapping of
Figure 236327DEST_PATH_IMAGE011
Objective function of convolutional neural network
Figure 495270DEST_PATH_IMAGE012
Euclidean distance defined as the degree of similarity weighted:
Figure 515179DEST_PATH_IMAGE013
in the formula, weight
Figure 517770DEST_PATH_IMAGE006
Is a single particle image
Figure 119783DEST_PATH_IMAGE002
Corresponding projected image
Figure 284048DEST_PATH_IMAGE005
The similarity degree value of (a) is obtained,
Figure 525674DEST_PATH_IMAGE014
is a dimension index of the low-dimensional manifold coordinates.
E. Predicting each single-particle image by using trained convolutional neural network
Figure 597535DEST_PATH_IMAGE002
Coordinate values on the energy surface
Figure 975427DEST_PATH_IMAGE011
Thereby obtaining an energy surface at a single particle level.
As a preferable scheme, in the step B, before the low-dimensional manifold mapping operation, low-pass filtering preprocessing with the same threshold may be performed on the three-dimensional electron density map data set, so as to improve robustness of low-dimensional manifold embedding reflecting the conformational distribution, and provide a regression label with higher quality for the convolutional neural network.
As a preferable scheme, in the step B2, a t-SNE or UMAP manifold learning algorithm with a good effect may be applied to obtain a low-dimensional manifold embedding of the three-dimensional electron density map data set, so as to improve the regression performance of the convolutional neural network.
Preferably, in the step D, in order to consider the similarity degree between the single-particle image and the angle projection image corresponding to the three-dimensional electron density map, a decoder may be added to the design of the convolutional neural network, and the angle projection image corresponding to the three-dimensional electron density map is regressed through the output of the decoding layer, and the objective function is simultaneously used
Figure 841752DEST_PATH_IMAGE012
Defined as a weighted sum of the distance between the low-dimensional manifold coordinates and the distance between the decoded images.
The invention has the following technical effects:
the invention provides a method for constructing a single-particle-level energy curved surface based on single-particle image data of a cryoelectron microscope, which is used for obtaining the single-particle-level energy curved surface through grouping classification of the single-particle data, low-dimensional manifold mapping of a three-dimensional electron density map data set, similarity calculation of a single-particle image and a corresponding projection image thereof, and training and prediction of a convolutional neural network. The invention can intuitively reflect the conformation distribution of the biological molecules by utilizing the particle-level energy curved surface obtained by the convolutional neural network. According to the invention, the cryoelectron microscope technology and the convolutional neural network are combined with each other, the high-precision visualization is carried out on the conformational space of the biomolecule to be researched, the multi-conformational analysis mode of the cryoelectron microscope data is expanded, and the robustness of the data processing result to high noise is improved.
Drawings
FIG. 1 is a technical flowchart for constructing a single-particle data energy curved surface of a cryoelectron microscope according to the present invention.
FIG. 2 is a schematic diagram of a convolutional neural network of the present invention to obtain coordinates of a single-particle energy surface.
FIG. 3 is a schematic diagram of an energy surface and conformational distribution analysis obtained by an embodiment of the present invention.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: unless specifically stated otherwise, the sample pictures and values provided in these examples do not limit the scope of the invention.
Referring to fig. 2, the present invention uses convolutional neural networks to study the complex conformational distribution of a protein.
Firstly, dividing single particle data of the protein by a cryoelectron microscope into a plurality of groups, and respectively carrying out three-dimensional classification to generate a three-dimensional electron density map data set. The single particle data for this protein, which total over 2,500,000 single particle images, were divided into 25 groups, each group containing about 100,000 single particle images, and after three-dimensional classification, 250 three-dimensional electron density maps were generated.
And secondly, designing a deep manifold learning algorithm to obtain low-dimensional manifold embedding of the three-dimensional electron density map data set. Inputting a depth self-coding network for extracting features through 250 three-dimensional electron density maps, setting the number of rounds to be 50 rounds in the training process, enabling each batch of data to comprise 4 three-dimensional electron density maps, and iteratively updating the parameters of the depth self-coding network by using an Adam optimizer. And after training is finished, using the depth self-coding network parameters with the minimum mean square error of the decoding layer for feature extraction of the three-dimensional electron density map data set. After the characteristics of the three-dimensional electron density map are extracted, mapping the low-dimensional manifold embedding of the three-dimensional electron density map data set through a t-SNE manifold learning algorithm to obtain the low-dimensional manifold coordinate of each three-dimensional electron density map
Figure 305094DEST_PATH_IMAGE001
The low-dimensional coordinates require that the local distance of the high-dimensional data is kept as constant as possible, the dimension of the low-dimensional coordinates is set to be 2, the confusion is set to be 30, and the maximum number of iteration rounds is set to be 1000.
And thirdly, calculating the similarity degree of each single-particle image and the projection image corresponding to the three-dimensional electron density map of the single-particle image. Namely, angle information of each single particle image is taken out, and a projection image of the three-dimensional electron density image at the angle is obtained; single particle image
Figure 180646DEST_PATH_IMAGE002
Projection image corresponding thereto
Figure 678624DEST_PATH_IMAGE005
（
Figure 263320DEST_PATH_IMAGE003
Is an index of a single-particle image,
Figure 479538DEST_PATH_IMAGE004
is an index of a pixel point), the degree of similarity
Figure 627622DEST_PATH_IMAGE006
The specific expression of (a) is defined as:
Figure 980106DEST_PATH_IMAGE007
in the formula (I), the compound is shown in the specification,
Figure 657075DEST_PATH_IMAGE008
and
Figure 626168DEST_PATH_IMAGE009
respectively representing single-grain images
Figure 312364DEST_PATH_IMAGE002
Projection image corresponding thereto
Figure 597983DEST_PATH_IMAGE005
The pixel average value of (2). The degree of similarity
Figure 711433DEST_PATH_IMAGE006
Has a value range of [0,1 ]]，
Figure 636663DEST_PATH_IMAGE006
The larger the size of the single particle image
Figure 392130DEST_PATH_IMAGE002
Projection image corresponding thereto
Figure 719206DEST_PATH_IMAGE005
The more similar. For each single particle image
Figure 269136DEST_PATH_IMAGE002
Calculating its corresponding projection image
Figure 416084DEST_PATH_IMAGE005
Degree of similarity of (2)
Figure 444082DEST_PATH_IMAGE006
；
If it is used
Figure 907556DEST_PATH_IMAGE006
A value of 1 indicates a single-particle image used for the calculation
Figure 362808DEST_PATH_IMAGE002
Projection image corresponding thereto
Figure 590527DEST_PATH_IMAGE005
Is identical to each other if
Figure 156638DEST_PATH_IMAGE006
The value of 0 represents the single-particle image used for calculation
Figure 458306DEST_PATH_IMAGE002
Without any structural information. Degree of similarity of a typical single-particle image to its corresponding projected image
Figure 84459DEST_PATH_IMAGE006
The magnitude of the degree of similarity between two images is measured as a numerical value between 0 and 1.
Fourthly, training a convolution neural network and regressing the single particle passing similarity
Figure 205999DEST_PATH_IMAGE006
Weighted low-dimensional manifold embedding.Objective function of convolutional neural network
Figure 857692DEST_PATH_IMAGE012
Defined as degree of similarity
Figure 13866DEST_PATH_IMAGE006
Weighted euclidean distance:
Figure 138817DEST_PATH_IMAGE013
the objective function is used for learning single-particle image data
Figure 747653DEST_PATH_IMAGE002
Corresponding to the low-dimensional manifold coordinates of the three-dimensional electron density map
Figure 655566DEST_PATH_IMAGE010
Obtaining a single particle image
Figure 197406DEST_PATH_IMAGE002
Low dimensional manifold coordinate mapping of
Figure 165362DEST_PATH_IMAGE011
Wherein the weight is
Figure 271946DEST_PATH_IMAGE006
Is a single particle image
Figure 983550DEST_PATH_IMAGE002
Corresponding projected image
Figure 114317DEST_PATH_IMAGE005
The similarity measure value of (2). Training a convolutional neural network by using the whole single-particle data set; when the low-dimensional coordinates of the three-dimensional electron density map are learned, the similarity degree of the single-particle image and the corresponding three-dimensional electron density map is considered, and the effect expression of the convolution neural network for single-particle low-dimensional manifold mapping is improved. Training of convolutional neural networksIn the process, the maximum round number is set as 50 rounds, the batch data volume is set as 128 single-particle images, and the parameters of the convolutional neural network are updated iteratively until the loss function is converged.
Fifthly, generating each single-particle image by using the trained convolutional neural network
Figure 784333DEST_PATH_IMAGE002
Low dimensional manifold coordinates of
Figure 102182DEST_PATH_IMAGE011
And the method is used for drawing the energy curved surface of the single particle level. Inputting the data of each single-particle image in the cryoelectron microscope data of the protein into a trained convolutional neural network to obtain the low-dimensional manifold coordinate of each single-particle image, namely the coordinate value on an energy curved surface
Figure 945373DEST_PATH_IMAGE011
Thereby obtaining the single-particle level energy curved surface and carrying out high-precision visualization on the complex conformation space of the protein.
Referring to fig. 3, for another protein, the method for constructing the energy surface of the single particle level based on the single particle image data of the cryoelectron microscope is also applied to generate 600 three-dimensional electron density maps, and the conformational distribution characteristics of the protein are researched through the energy surface. The protein is analyzed by drawing an energy surface of a single particle level by using a convolutional neural network, and the analysis shows that if a certain conformation is more stable, the probability of the conformation is higher, the energy surface corresponds to lower energy, and conversely, if the stability of the certain conformation is lower, the probability of the conformation is lower, and the energy surface corresponds to higher energy value.
It is finally noted that the disclosed embodiments are intended to aid in the further understanding of the invention, but that those skilled in the art will appreciate that: various substitutions and modifications are possible without departing from the spirit and scope of this disclosure and the appended claims. Therefore, the invention should not be limited to the embodiments disclosed, but the scope of the invention is defined by the appended claims.

Claims (6)

1. A method for constructing an energy curved surface of a single particle level based on data of a cryoelectron microscope comprises the following steps:
A. dividing the image data of the cryoelectron microscope into a plurality of groups of single-particle images, respectively carrying out three-dimensional classification on each group of single-particle images, and reconstructing to generate a series of three-dimensional electron density map data sets;
B. mapping the low-dimensional manifold embedding of the three-dimensional electron density map data set by adopting a deep manifold learning algorithm;
C. for each single particle image
Figure 914776DEST_PATH_IMAGE001
Calculating the projection image with the angle corresponding to the three-dimensional electron density map
Figure 909277DEST_PATH_IMAGE002
To the extent of the similarity of (a) to (b),
Figure 988092DEST_PATH_IMAGE003
is an index of the single-particle image,
Figure 17228DEST_PATH_IMAGE004
is a pixel point index, and the realization method is as follows:
C1. taking out each single particle image
Figure 523295DEST_PATH_IMAGE001
Obtaining the projection image of the three-dimensional electron density map at the angle
Figure 739513DEST_PATH_IMAGE002
；
C2. Single particle image
Figure 700647DEST_PATH_IMAGE001
Projection image corresponding thereto
Figure 584289DEST_PATH_IMAGE002
Degree of similarity between them
Figure 526837DEST_PATH_IMAGE005
The specific expression of (a) is defined as:
Figure 230351DEST_PATH_IMAGE006
in the formula (I), the compound is shown in the specification,
Figure 650968DEST_PATH_IMAGE007
and
Figure 123538DEST_PATH_IMAGE008
respectively representing single-grain images
Figure 502567DEST_PATH_IMAGE001
Projection image corresponding thereto
Figure 427797DEST_PATH_IMAGE002
The pixel average value of (a);
D. training a convolutional neural network using the entire single-particle dataset, the convolutional neural network being used to learn single-particle image data
Figure 730734DEST_PATH_IMAGE001
Corresponding to the low-dimensional manifold coordinates of the three-dimensional electron density map
Figure 57810DEST_PATH_IMAGE009
Obtaining a single particle image
Figure 342161DEST_PATH_IMAGE001
Low dimensional manifold coordinate mapping of
Figure 20267DEST_PATH_IMAGE010
Of convolutional neural networksObjective function
Figure 782686DEST_PATH_IMAGE011
Euclidean distance defined as a weighted similarity measure:
Figure 495428DEST_PATH_IMAGE012
in the formula, weight
Figure 950680DEST_PATH_IMAGE005
Is a single particle image
Figure 850503DEST_PATH_IMAGE001
Corresponding projected image
Figure 495242DEST_PATH_IMAGE002
The similarity degree value of (2);
Figure 531331DEST_PATH_IMAGE013
is a dimension index of the low-dimensional manifold coordinates;
E. predicting each single-particle image by using trained convolutional neural network
Figure 688643DEST_PATH_IMAGE001
Coordinate value on energy surface
Figure 75762DEST_PATH_IMAGE010
Thereby obtaining an energy surface at a single particle level.
2. The method for constructing an energy surface at a single particle level based on cryo-electron microscopy data as defined in claim 1, wherein step B is performed by:
B1. extracting the structure detail characteristics of each three-dimensional electron density map by using a depth self-coding network;
B2. processing three dimensions through manifold learningObtaining electron density map data set and detail characteristics thereof to obtain low-dimensional manifold coordinates of each three-dimensional electron density map
Figure 179984DEST_PATH_IMAGE014
。
3. The method for constructing a single-particle-level energy surface based on cryo-electron microscopy data as claimed in claim 2, characterized in that in step B2, the three-dimensional electron density map data set is subjected to a low-pass filtering pre-processing with the same threshold.
4. The method for constructing an energy surface at a single particle level based on cryo-electron microscopy data as claimed in claim 2 wherein in step B2, a t-SNE or UMAP manifold learning algorithm is used.
5. The method for constructing a single-particle-level energy surface based on cryo-electron microscopy data as claimed in claim 1 wherein the degree of similarity in step C
Figure 601738DEST_PATH_IMAGE005
Has a value range of [0,1 ]]。
6. The method for constructing the energy curved surface of the single particle level based on the cryo-electron microscopy data as claimed in claim 1, wherein a decoder is added in the step D, the three-dimensional electron density map projection image corresponding to the angle is regressed through the output of the decoding layer, and simultaneously the objective function is processed
Figure 398793DEST_PATH_IMAGE015
Defined as a weighted sum of the distance between the low-dimensional manifold coordinates and the distance between the decoded images.
CN202210484308.2A 2022-05-06 2022-05-06 Method for constructing single-particle level energy curved surface based on data of cryoelectron microscope Active CN114580603B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210484308.2A CN114580603B (en) 2022-05-06 2022-05-06 Method for constructing single-particle level energy curved surface based on data of cryoelectron microscope

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210484308.2A CN114580603B (en) 2022-05-06 2022-05-06 Method for constructing single-particle level energy curved surface based on data of cryoelectron microscope

Publications (2)

Publication Number Publication Date
CN114580603A CN114580603A (en) 2022-06-03
CN114580603B true CN114580603B (en) 2022-07-26

Family

ID=81769121

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210484308.2A Active CN114580603B (en) 2022-05-06 2022-05-06 Method for constructing single-particle level energy curved surface based on data of cryoelectron microscope

Country Status (1)

Country Link
CN (1) CN114580603B (en)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107092790A (en) * 2017-04-19 2017-08-25 上海交通大学 Ice mapping three-dimensional density figure resolution detection method
CN113643230A (en) * 2021-06-22 2021-11-12 清华大学 Continuous learning method and system for identifying biomacromolecule particles of cryoelectron microscope
CN114283217A (en) * 2021-09-23 2022-04-05 腾讯科技（深圳）有限公司 Method, device and equipment for training reconstruction model of three-dimensional electron microscope image

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9373059B1 (en) * 2014-05-05 2016-06-21 Atomwise Inc. Systems and methods for applying a convolutional network to spatial data

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107092790A (en) * 2017-04-19 2017-08-25 上海交通大学 Ice mapping three-dimensional density figure resolution detection method
CN113643230A (en) * 2021-06-22 2021-11-12 清华大学 Continuous learning method and system for identifying biomacromolecule particles of cryoelectron microscope
CN114283217A (en) * 2021-09-23 2022-04-05 腾讯科技（深圳）有限公司 Method, device and equipment for training reconstruction model of three-dimensional electron microscope image

Also Published As

Publication number Publication date
CN114580603A (en) 2022-06-03

Similar Documents

Publication Publication Date Title
CN110443281B (en) Text classification self-adaptive oversampling method based on HDBSCAN (high-density binary-coded decimal) clustering
CN110889852B (en) Liver segmentation method based on residual error-attention deep neural network
CN112001218B (en) Three-dimensional particle class detection method and system based on convolutional neural network
CN113693563B (en) Brain function network classification method based on hypergraph attention network
WO2023217290A1 (en) Genophenotypic prediction based on graph neural network
CN114091628B (en) Three-dimensional point cloud up-sampling method and system based on double branch network
Li et al. Dictionary optimization and constraint neighbor embedding-based dictionary mapping for superdimension reconstruction of porous media
CN115311502A (en) Remote sensing image small sample scene classification method based on multi-scale double-flow architecture
Maddumala A Weight Based Feature Extraction Model on Multifaceted Multimedia Bigdata Using Convolutional Neural Network.
Zhang et al. PM-ARNN: 2D-TO-3D reconstruction paradigm for microstructure of porous media via adversarial recurrent neural network
CN118038032A (en) Point cloud semantic segmentation model based on super point embedding and clustering and training method thereof
CN114580603B (en) Method for constructing single-particle level energy curved surface based on data of cryoelectron microscope
Peng et al. Fully convolutional neural networks for tissue histopathology image classification and segmentation
CN115393378B (en) Low-cost and efficient cell nucleus image segmentation method
CN103336781B (en) A kind of medical image clustering method
Sun et al. Spatial-temporal scientific data clustering via deep convolutional neural network
CN112991402A (en) Cultural relic point cloud registration method and system based on improved differential evolution algorithm
Uddin et al. Practical analysis of macromolecule identity from cryo-electron tomography images using deep learning
Senthilnathan et al. Comparison and validation of stochastic microstructure characterization and reconstruction: Machine learning vs. deep learning methodologies
CN117373564B (en) Method and device for generating binding ligand of protein target and electronic equipment
Liu et al. PointFP: A Feature-Preserving Point Cloud Sampling
Zheng et al. Localization and recognition of single particle image in microscopy micrographs based on region based convolutional neural networks
Hamitouche Machine learning for determining continuous conformational transitions of biomolecular complexes from single-particle cryo-electron microscopy images
WO2023091180A1 (en) System and method for automated microscope image acquisition and 3d analysis
Codex Exploring Subgraph Modeling Techniques for Contextual-Point Relationships in 3D Point Cloud Segmentation: A Comprehensive Review

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant