Article

Modeling Protein Structure Using Geometric Vector Field Networks

2023-05-09

Abstract excerpt

Proteins serve as the foundation of life. Most diseases and challenges in life sciences are intimately linked to protein structures. In this paper, we propose a novel vector field network (VFN) for modeling protein structure. Unlike previous methods that extract geometric information relying heavily on hand-crafted features, VFN establishes a new geometric representation paradigm through a novel vector field opera...

Topics

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

Identifiers and source

Literature Corpus work: 6bc664a3-f64c-5c2a-bf9a-63413d8cc94d
DOI: 10.1101/2023.05.07.539736

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Modeling Protein Structure Using Geometric Vector Field NetworksDOI 10.1101/2023.05.07.539736Read

Merizo: a rapid and accurate domain segmentation method using invariant point attentionSemantic proximity Read

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

Fast and effective protein model refinement by deep graph neural networksSemantic proximity Read

Ultra-fast and highly sensitive protein structure alignment with segment-level representations and block-sparse optimizationSemantic proximity Read

ENGINE: A Scalable Equivariant Graph Network Framework for Precise Protein Function PredictionSemantic proximity Read

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

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

Genome-scale annotation of protein binding sites via language model and geometric deep learningSemantic proximity Read

COLLAPSE: A representation learning framework for identification and characterization of protein structural sitesSemantic proximity Read

Contrasting Sequence with Structure: Pre-training Graph Representations with PLMsSemantic proximity Read

Unsupervised learning reveals landscape of local structural motifs across protein classesSemantic proximity Read

The Topological Properties of the Protein UniverseSemantic proximity Read