Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
Predicting interactions between proteins and other biomolecules solely based on structure
remains a challenge in biology. A high-level representation of protein structure, the …
remains a challenge in biology. A high-level representation of protein structure, the …
Protein interaction interface region prediction by geometric deep learning
B Dai, C Bailey-Kellogg - Bioinformatics, 2021 - academic.oup.com
Motivation Protein–protein interactions drive wide-ranging molecular processes, and
characterizing at the atomic level how proteins interact (beyond just the fact that they …
characterizing at the atomic level how proteins interact (beyond just the fact that they …
PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces
LF Krapp, LA Abriata, F Cortés Rodriguez… - Nature …, 2023 - nature.com
Proteins are essential molecular building blocks of life, responsible for most biological
functions as a result of their specific molecular interactions. However, predicting their …
functions as a result of their specific molecular interactions. However, predicting their …
De novo design of protein interactions with learned surface fingerprints
Physical interactions between proteins are essential for most biological processes
governing life. However, the molecular determinants of such interactions have been …
governing life. However, the molecular determinants of such interactions have been …
ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction
Predicting the functional sites of a protein from its structure, such as the binding sites of small
molecules, other proteins or antibodies, sheds light on its function in vivo. Currently, two …
molecules, other proteins or antibodies, sheds light on its function in vivo. Currently, two …
GeoBind: segmentation of nucleic acid binding interface on protein surface with geometric deep learning
P Li, ZP Liu - Nucleic Acids Research, 2023 - academic.oup.com
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 …
vivo. Current methods encode protein sites from the handcrafted features of their local …
DeepRank-GNN: a graph neural network framework to learn patterns in protein–protein interfaces
Motivation Gaining structural insights into the protein–protein interactome is essential to
understand biological phenomena and extract knowledge for rational drug design or protein …
understand biological phenomena and extract knowledge for rational drug design or protein …
Deep geometric representations for modeling effects of mutations on protein-protein binding affinity
Modeling the impact of amino acid mutations on protein-protein interaction plays a crucial
role in protein engineering and drug design. In this study, we develop GeoPPI, a novel …
role in protein engineering and drug design. In this study, we develop GeoPPI, a novel …
DeepRank: a deep learning framework for data mining 3D protein-protein interfaces
Abstract Three-dimensional (3D) structures of protein complexes provide fundamental
information to decipher biological processes at the molecular scale. The vast amount of …
information to decipher biological processes at the molecular scale. The vast amount of …
End-to-end learning on 3d protein structure for interface prediction
Despite an explosion in the number of experimentally determined, atomically detailed
structures of biomolecules, many critical tasks in structural biology remain data-limited …
structures of biomolecules, many critical tasks in structural biology remain data-limited …