Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
Sign Up to like & getrecommendations! Published in 2019 at "Nature Methods"
DOI: 10.1038/s41592-019-0666-6
Abstract: Predicting interactions between proteins and other biomolecules solely based on structure remains a challenge in biology. A high-level representation of protein structure, the molecular surface, displays patterns of chemical and geometric features that fingerprint a… read more here.
Keywords: protein; geometric deep; deep learning; interaction ... See more keywords
CSM-Potential: mapping protein interactions and biological ligands in 3D space using geometric deep learning
Sign Up to like & getrecommendations! Published in 2022 at "Nucleic Acids Research"
DOI: 10.1093/nar/gkac381
Abstract: Abstract Recent advances in protein structural modelling have enabled the accurate prediction of the holo 3D structures of almost any protein, however protein function is intrinsically linked to the interactions it makes. While a number… read more here.
Keywords: protein protein; csm potential; potential mapping; deep learning ... See more keywords
GeoBind: segmentation of nucleic acid binding interface on protein surface with geometric deep learning.
Sign Up to like & getrecommendations! Published in 2023 at "Nucleic acids research"
DOI: 10.1093/nar/gkad288
Abstract: Unveiling the nucleic acid binding sites of a protein helps reveal its regulatory functions in vivo. Current methods encode protein sites from the handcrafted features of their local neighbors and recognize them via a classification,… read more here.
Keywords: protein surface; acid binding; geobind; deep learning ... See more keywords
Geometric Deep Learning for Protein–Protein Interaction Predictions
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3201543
Abstract: This work introduces novel approaches, based on geometrical deep learning, for predicting protein–protein interactions. A dataset containing both interacting and non-interacting proteins is selected from the Negatome Database. Interactions are predicted from a graph representing… read more here.
Keywords: protein protein; deep learning; geometric deep; convolutional neural ... See more keywords
Structure-based drug design with geometric deep learning
Sign Up to like & getrecommendations! Published in 2022 at "Current opinion in structural biology"
DOI: 10.48550/arxiv.2210.11250
Abstract: Structure-based drug design uses three-dimensional geometric information of macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric deep learning, an emerging concept of neural-network-based machine learning, has been applied to macromolecular structures.… read more here.
Keywords: design; deep learning; drug; structure based ... See more keywords