IL307671A - Deep convolutional neural networks to predict variant pathogenicity using three-dimensional (3d) protein structures - Google Patents
Deep convolutional neural networks to predict variant pathogenicity using three-dimensional (3d) protein structuresInfo
- Publication number
- IL307671A IL307671A IL307671A IL30767123A IL307671A IL 307671 A IL307671 A IL 307671A IL 307671 A IL307671 A IL 307671A IL 30767123 A IL30767123 A IL 30767123A IL 307671 A IL307671 A IL 307671A
- Authority
- IL
- Israel
- Prior art keywords
- amino acid
- amino acids
- voxel
- voxels
- nearest
- Prior art date
Links
- 108090000623 proteins and genes Proteins 0.000 title claims 10
- 102000004169 proteins and genes Human genes 0.000 title claims 10
- 230000007918 pathogenicity Effects 0.000 title claims 3
- 238000013527 convolutional neural network Methods 0.000 title 1
- 150000001413 amino acids Chemical class 0.000 claims 43
- 125000004429 atom Chemical group 0.000 claims 20
- 125000002924 primary amino group Chemical group [H]N([H])* 0.000 claims 11
- 238000000034 method Methods 0.000 claims 7
- 108700028369 Alleles Proteins 0.000 claims 6
- 125000003275 alpha amino acid group Chemical group 0.000 claims 5
- 229910052799 carbon Inorganic materials 0.000 claims 4
- 230000001717 pathogenic effect Effects 0.000 claims 3
- 238000013528 artificial neural network Methods 0.000 claims 2
- 238000002864 sequence alignment Methods 0.000 claims 2
Classifications
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
- G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
- G16B40/20—Supervised data analysis
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B50/00—ICT programming tools or database systems specially adapted for bioinformatics
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- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Medical Informatics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Biology (AREA)
- Biophysics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Biotechnology (AREA)
- General Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Chemical & Material Sciences (AREA)
- Databases & Information Systems (AREA)
- Bioethics (AREA)
- Genetics & Genomics (AREA)
- Epidemiology (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Artificial Intelligence (AREA)
- Molecular Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Analytical Chemistry (AREA)
- Crystallography & Structural Chemistry (AREA)
- Evolutionary Computation (AREA)
- Public Health (AREA)
- Software Systems (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Investigating Or Analysing Biological Materials (AREA)
- Peptides Or Proteins (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Claims (20)
1.Claims 1. A system, comprising: memory storing amino acid-wise distance channels for a plurality of amino acids in an amino acid sequence of a protein, wherein each of the amino acid-wise distance channels has voxel-wise distance values for voxels in a plurality of voxels, and wherein the voxel-wise distance values specify distances from corresponding voxels in the plurality of voxels to atoms of corresponding amino acids in the plurality of amino acids; and a neural network-based variant pathogenicity classifier, running on at least one processor coupled to the memory, wherein the neural network-based variant pathogenicity classifier is trained to: process as input a tensor that includes the amino acid-wise distance channels and an alternative allele amino acid of the protein expressed by a variant, and classify the variant as benign or pathogenic based at least in part on the tensor.
2. The system of claim 1, further comprising a distance channels generator, running on at least one processor coupled to the memory, that centers a voxel grid of the voxels on an alpha-carbon atom of respective residues of the corresponding amino acids and calculates the voxel-wise distance values by specifying a distance between centers of the voxels in the voxel grid and the atoms of the corresponding amino acids.
3. The system of claim 2, wherein the distance channels generator centers the voxel grid on an alpha-carbon atom of a residue of a particular amino acid that corresponds to at least one variant amino acid in the protein.
4. The system of claim 3, further configured to encode, in the tensor, a directionality of the corresponding amino acids and a position of the particular amino acid by multiplying, with a directionality parameter, voxel-wise distance values for preceding amino acids that precede the particular amino acid.
5. The system of claim 3, wherein the distances are nearest-atom distances from corresponding voxel centers in the voxel grid to nearest atoms of the corresponding amino acids.
6. The system of claim 5, wherein the corresponding amino acids have alpha-carbon atoms, wherein the distances are nearest-alpha-carbon atom distances from the corresponding voxel centers to nearest alpha-carbon atoms of the corresponding amino acids.
7. The system of claim 5, wherein the corresponding amino acids have beta-carbon atoms, wherein the distances are nearest-beta-carbon atom distances from the corresponding voxel centers to nearest beta-carbon atoms of the corresponding amino acids.
8. The system of claim 5, wherein the corresponding amino acids have backbone atoms, wherein the distances are nearest-backbone atom distances from the corresponding voxel centers to nearest backbone atoms of the corresponding amino acids.
9. The system of claim 3, further configured to encode, in the tensor, a nearest atom channel that specifies a distance from each voxel to a nearest atom, wherein the nearest atom is selected irrespective of an amino acid to which the nearest atom belongs and atomic elements of the amino acid.
10. The system of claim 1, wherein the tensor further includes evolutionary profiles that specify conservation levels of the corresponding amino acids across a plurality of species with sequences that are homologous to the amino acid sequence of the protein.
11. The system of claim 10, further comprising an evolutionary profiles generator, running on at least one processor coupled to the memory, that, for each of the voxels, uses a multi-sequence alignment to determine pan-amino acid conservation frequencies, selects a nearest atom across the plurality of amino acids and atom categories, selects a pan-amino acid conservation frequencies sequence for a residue of an amino acid that includes the nearest atom, voxelizes the pan-amino acid conservation frequencies for the residue of the amino acid, and makes the pan-amino acid conservation frequencies sequence available as one of the evolutionary profiles.
12. The system of claim 11, wherein the pan-amino acid conservation frequencies sequence is configured for a particular position of the residue as observed in the plurality of species.
13. The system of claim 11, wherein the evolutionary profiles generator, for each of the voxels, uses the multi-sequence alignment to determine per-amino acid conservation frequencies, selects respective nearest atoms in respective ones of the plurality of amino acids, selects respective per-amino acid conservation frequencies for respective residues of the plurality of amino acids that include the respective nearest atoms, voxelizes the per-amino acid conservation frequencies for the respective residues of the plurality of amino acids, and makes the per-amino acid conservation frequencies available as one of the evolutionary profiles.
14. A computer-implemented method, comprising: storing amino acid-wise distance channels for a plurality of amino acids in an amino acid sequence of a protein, wherein each of the amino acid-wise distance channels has voxel-wise distance values for voxels in a plurality of voxels, and wherein the voxel-wise distance values specify distances from corresponding voxels in the plurality of voxels to atoms of corresponding amino acids in the plurality of amino acids; processing as input a tensor that includes the amino acid-wise distance channels and an alternative allele amino acid of the protein expressed by a variant; and classifying the variant as benign or pathogenic based at least in part on the tensor.
15. The computer-implemented method of claim 14, wherein the tensor further includes an absentee atom channel that specifies atoms not found within a predefined radius of a voxel center, wherein the absentee atom channel is one-hot encoded.
16. The computer-implemented method of claim 14, wherein the tensor further includes a one-hot encoding of the alternative allele amino acid that is voxel-wise encoded to each of the amino acid-wise distance channels.
17. The computer-implemented method of claim 14, wherein the tensor further includes a reference allele amino acid in the amino acid sequence of the protein.
18. The computer-implemented method of claim 17, wherein the tensor further includes a one-hot encoding of the reference allele amino acid that is voxel-wise encoded to each of the amino acid-wise distance channels.
19. The computer-implemented method of claim 14, wherein the tensor further includes one or more of: annotation channels for the corresponding amino acids that annotate characteristics of the corresponding amino acids, wherein the annotation channels are one-hot encoded in the tensor; or structure confidence channels for the corresponding amino acids that specify quality of respective structures of the corresponding amino acids.
20. A non-transitory computer readable medium storing instructions that, when executed by at least a processor, cause a system to performing actions comprising: storing amino acid-wise distance channels for a plurality of amino acids in an amino acid sequence of a protein, wherein each of the amino acid-wise distance channels has voxel-wise distance values for voxels in a plurality of voxels, and wherein the voxel-wise distance values specify distances from corresponding voxels in the plurality of voxels to atoms of corresponding amino acids in the plurality of amino acids; processing as input a tensor that includes the amino acid-wise distance channels and an alternative allele amino acid of the protein expressed by a variant; and classifying the variant as benign or pathogenic based at least in part on the tensor.
Applications Claiming Priority (7)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US202163175495P | 2021-04-15 | 2021-04-15 | |
| US17/232,056 US20220336054A1 (en) | 2021-04-15 | 2021-04-15 | Deep Convolutional Neural Networks to Predict Variant Pathogenicity using Three-Dimensional (3D) Protein Structures |
| US202163175767P | 2021-04-16 | 2021-04-16 | |
| US17/468,411 US11515010B2 (en) | 2021-04-15 | 2021-09-07 | Deep convolutional neural networks to predict variant pathogenicity using three-dimensional (3D) protein structures |
| US17/703,958 US20220336057A1 (en) | 2021-04-15 | 2022-03-24 | Efficient voxelization for deep learning |
| US17/703,935 US20220336056A1 (en) | 2021-04-15 | 2022-03-24 | Multi-channel protein voxelization to predict variant pathogenicity using deep convolutional neural networks |
| PCT/US2022/024913 WO2022221589A1 (en) | 2021-04-15 | 2022-04-14 | Deep convolutional neural networks to predict variant pathogenicity using three-dimensional (3d) protein structures |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| IL307671A true IL307671A (en) | 2023-12-01 |
Family
ID=81580106
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| IL307671A IL307671A (en) | 2021-04-15 | 2022-04-14 | Deep convolutional neural networks to predict variant pathogenicity using three-dimensional (3d) protein structures |
Country Status (8)
| Country | Link |
|---|---|
| EP (1) | EP4323990A1 (en) |
| JP (1) | JP2024513994A (en) |
| KR (1) | KR20230171930A (en) |
| AU (1) | AU2022256491A1 (en) |
| BR (1) | BR112023021302A2 (en) |
| CA (1) | CA3215462A1 (en) |
| IL (1) | IL307671A (en) |
| WO (2) | WO2022221587A1 (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116153435B (en) * | 2023-04-21 | 2023-08-11 | 山东大学齐鲁医院 | Polypeptide prediction method and system based on coloring and three-dimensional structure |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10423861B2 (en) * | 2017-10-16 | 2019-09-24 | Illumina, Inc. | Deep learning-based techniques for training deep convolutional neural networks |
| WO2019084559A1 (en) * | 2017-10-27 | 2019-05-02 | Apostle, Inc. | Predicting cancer-related pathogenic impact of somatic mutations using deep learning-based methods |
| CN110245685B (en) * | 2019-05-15 | 2022-03-25 | 清华大学 | Method, system and storage medium for predicting pathogenicity of genome single-site variation |
-
2022
- 2022-04-14 WO PCT/US2022/024911 patent/WO2022221587A1/en active Application Filing
- 2022-04-14 EP EP22721220.6A patent/EP4323990A1/en active Pending
- 2022-04-14 AU AU2022256491A patent/AU2022256491A1/en active Pending
- 2022-04-14 CA CA3215462A patent/CA3215462A1/en active Pending
- 2022-04-14 WO PCT/US2022/024913 patent/WO2022221589A1/en active Application Filing
- 2022-04-14 KR KR1020237034175A patent/KR20230171930A/en unknown
- 2022-04-14 JP JP2023563032A patent/JP2024513994A/en active Pending
- 2022-04-14 BR BR112023021302A patent/BR112023021302A2/en unknown
- 2022-04-14 IL IL307671A patent/IL307671A/en unknown
Also Published As
| Publication number | Publication date |
|---|---|
| BR112023021302A2 (en) | 2023-12-19 |
| CA3215462A1 (en) | 2022-10-20 |
| WO2022221589A1 (en) | 2022-10-20 |
| JP2024513994A (en) | 2024-03-27 |
| KR20230171930A (en) | 2023-12-21 |
| AU2022256491A1 (en) | 2023-10-26 |
| WO2022221587A1 (en) | 2022-10-20 |
| EP4323990A1 (en) | 2024-02-21 |
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