LegallyOverworked
@legallyoverworked.bsky.social
Reposted by LegallyOverworked
The Baker lab seems serious about moving into enzyme design.
Here, they train a flow-matching model that can be conditioned by atomic coordinates and then use it to design enzymes based on active site geometry specified by density functional theory.
www.biorxiv.org/content/10.1...
Here, they train a flow-matching model that can be conditioned by atomic coordinates and then use it to design enzymes based on active site geometry specified by density functional theory.
www.biorxiv.org/content/10.1...
November 15, 2024 at 10:11 PM
The Baker lab seems serious about moving into enzyme design.
Here, they train a flow-matching model that can be conditioned by atomic coordinates and then use it to design enzymes based on active site geometry specified by density functional theory.
www.biorxiv.org/content/10.1...
Here, they train a flow-matching model that can be conditioned by atomic coordinates and then use it to design enzymes based on active site geometry specified by density functional theory.
www.biorxiv.org/content/10.1...
Reposted by LegallyOverworked
Long-context xLSTM models of DNA, proteins, and chemicals.
@smdrnks.bsky.social @phseidl.bsky.social @gklambauer.bsky.social
arxiv.org/abs/2411.04165
@smdrnks.bsky.social @phseidl.bsky.social @gklambauer.bsky.social
arxiv.org/abs/2411.04165
November 20, 2024 at 10:11 PM
Long-context xLSTM models of DNA, proteins, and chemicals.
@smdrnks.bsky.social @phseidl.bsky.social @gklambauer.bsky.social
arxiv.org/abs/2411.04165
@smdrnks.bsky.social @phseidl.bsky.social @gklambauer.bsky.social
arxiv.org/abs/2411.04165
Reposted by LegallyOverworked
CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters kcat, Km and Ki https://www.biorxiv.org/content/10.1101/2024.03.10.584340v1
CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters kcat, Km and Ki https://www.biorxiv.org/content/10.1101/2024.03.10.584340v1
Quantification of enzymatic activities still heavily relies on experimental assays, which can be exp
www.biorxiv.org
March 13, 2024 at 4:49 PM
CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters kcat, Km and Ki https://www.biorxiv.org/content/10.1101/2024.03.10.584340v1
Reposted by LegallyOverworked
DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D Structures https://www.biorxiv.org/content/10.1101/2023.12.09.570923v1
DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D Structures https://www.biorxiv.org/content/10.1101/2023.12.09.570923v1
Turnover numbers (kcat), which indicate an enzyme's catalytic efficiency, have a wide range of appli
www.biorxiv.org
December 11, 2023 at 7:58 PM
DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D Structures https://www.biorxiv.org/content/10.1101/2023.12.09.570923v1
Reposted by LegallyOverworked
Deep learning-based prediction of enzyme optimal pH and design of point mutations to improve acid resistance https://www.biorxiv.org/content/10.1101/2024.11.16.623957v1
Deep learning-based prediction of enzyme optimal pH and design of point mutations to improve acid resistance https://www.biorxiv.org/content/10.1101/2024.11.16.623957v1
An accurate deep learning predictor of enzyme optimal pH is essential to quantitatively describe how
www.biorxiv.org
November 19, 2024 at 12:49 AM
Deep learning-based prediction of enzyme optimal pH and design of point mutations to improve acid resistance https://www.biorxiv.org/content/10.1101/2024.11.16.623957v1
Reposted by LegallyOverworked
DeepLocPro: Use ESM-2 embeddings to predict prokaryotic protein subcellular localization.
I'm kinda surprised that this is the first deep learning method for prokaryotic localization and that the SotA is from 2010!
www.biorxiv.org/content/10.1...
I'm kinda surprised that this is the first deep learning method for prokaryotic localization and that the SotA is from 2010!
www.biorxiv.org/content/10.1...
January 5, 2024 at 8:11 PM
DeepLocPro: Use ESM-2 embeddings to predict prokaryotic protein subcellular localization.
I'm kinda surprised that this is the first deep learning method for prokaryotic localization and that the SotA is from 2010!
www.biorxiv.org/content/10.1...
I'm kinda surprised that this is the first deep learning method for prokaryotic localization and that the SotA is from 2010!
www.biorxiv.org/content/10.1...
Reposted by LegallyOverworked
PreMode predicts mode of action of missense variants by deep graph representation learning of protein sequence and structural context https://www.biorxiv.org/content/10.1101/2024.02.20.581321v1
PreMode predicts mode of action of missense variants by deep graph representation learning of protein sequence and structural context https://www.biorxiv.org/content/10.1101/2024.02.20.581321v1
Accurate prediction of the functional impact of missense variants is fundamentally important for dis
www.biorxiv.org
February 24, 2024 at 12:50 AM
PreMode predicts mode of action of missense variants by deep graph representation learning of protein sequence and structural context https://www.biorxiv.org/content/10.1101/2024.02.20.581321v1
Reposted by LegallyOverworked
DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D Structures https://www.biorxiv.org/content/10.1101/2023.12.09.570923v1
DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D Structures https://www.biorxiv.org/content/10.1101/2023.12.09.570923v1
Turnover numbers (kcat), which indicate an enzyme's catalytic efficiency, have a wide range of appli
www.biorxiv.org
December 11, 2023 at 7:58 PM
DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D Structures https://www.biorxiv.org/content/10.1101/2023.12.09.570923v1