Deep convolutional neural networks to predict variant pathogenicity using three-dimensional (3D) protein structures
US11515010B2 · kind B2 · utility
Assignees
Inventors
Key dates
| Filing date | Sep 7, 2021 |
| Grant date | Nov 29, 2022 |
| Priority date | — |
| Expiry date | Sep 7, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/0455
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The technology disclosed relates to determining pathogenicity of variants. In particular, the technology disclosed relates to generating amino acid-wise distance channels for a plurality of amino acids in a protein. Each of the amino acid-wise distance channels has voxel-wise distance values for voxels in a plurality of voxels. A tensor includes the amino acid-wise distance channels and at least an alternative allele of the protein expressed by a variant. A deep convolutional neural network determines a pathogenicity of the variant based at least in part on processing the tensor. The technology disclosed further augments the tensor with supplemental information like a reference allele of the protein, evolutionary conservation data about the protein, annotation data about the protein, and structure confidence data about the protein.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.