Robust deep learning–based protein sequence design using ProteinMPNN is a research paper published in Science (2022). On theSindex it has a DataRank of 0. It has been cited 1,912 times.
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National Science Foundation
Grant: 2140004
Graduate Research Fellowship Program (GRFP)
National Science Foundation
Grant: 1937533
CIBR: Collaborative Research: CIBR Expanding structure coverage of genomes to facilitate macromolecular assembly determination.
National Institutes of Health
Grant: 2P30GM124169-06
ALS Efficiently Networking Advanced Beam Line Experiments (ALS-ENABLE)
NIGMS NIH HHS
Grant: P30 GM124169
Howard Hughes Medical Institute
Fields of Study
MeSH Terms
Keywords
Additional file 1 of Cross-protein transfer learning substantially improves disease variant prediction
Additional file 1 of Cross-protein transfer learning substantially improves disease variant prediction
Additional file 2 of Cross-protein transfer learning substantially improves disease variant prediction
Additional file 2 of Cross-protein transfer learning substantially improves disease variant prediction
Additional file 1 of Prop3D: A flexible, Python-based platform for machine learning with protein structural properties and biophysical data
Additional file 1 of Prop3D: A flexible, Python-based platform for machine learning with protein structural properties and biophysical data
Additional file 1 of Tpgen: a language model for stable protein design with a specific topology structure
Additional file 1 of Tpgen: a language model for stable protein design with a specific topology structure
ProteinMPNN Gradio Webapp
ProteinMPNN Gradio Webapp