Article

Fast and effective protein model refinement by deep graph neural networks

2020-12-11

Abstract excerpt

Protein structure prediction has been greatly improved, but there are still a good portion of predicted models that do not have very high quality. Protein model refinement is one of the methods that may further improve model quality. Nevertheless, it is very challenging to refine a protein model towards better quality. Currently the most successful refinement methods rely on extensive conformation sampling and thu...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: ac1fd021-6d2a-5e64-b5cf-fcf74279b2a1
DOI: 10.1101/2020.12.10.419994

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Fast and effective protein model refinement by deep graph neural networksDOI 10.1101/2020.12.10.419994Read

ContrastQA: A label-guided graph contrastive learning-based approach for protein complex structure quality assessmentSemantic proximity Read

Graph Convolutional Network for predicting secondary structure of RNASemantic proximity Read

Integrating structural homology with deep learning to achieve highly accurate protein-protein interface prediction for the human interactomeSemantic proximity Read

Modeling Protein Structure Using Geometric Vector Field NetworksSemantic proximity Read

Automated model building and protein identification in cryo-EM mapsSemantic proximity Read

Structural analogue-based protein structure domain assembly assisted by deep learningSemantic proximity Read

A Variational Graph Partitioning Approach to Modeling Protein Liquid-liquid Phase SeparationSemantic proximity Read

Hierarchical Deep Learning Framework Integrating Structural Interaction Potentials and Evolutionary Information for Protein-Protein Interaction Affinity PredictionSemantic proximity Read

ProS-GNN: Predicting effects of mutations on protein stability using graph neural networks.Semantic proximity Read

Predicting the effects of mutations on protein solubility using graph convolution network and protein language model representation.Semantic proximity Read

Single-sequence protein structure prediction using supervised transformer protein language modelsSemantic proximity Read

BayeStab: Predicting effects of mutations on protein stability with uncertainty quantification.Semantic proximity Read