Protein Interface Prediction
4 papers with code • 0 benchmarks • 0 datasets
How and where proteins interface with one another can ultimately impact the proteins' functions along with a range of other biological processes. As such, precise computational methods for protein interface prediction (PIP) come highly sought after as they could yield significant advances in drug discovery and design as well as protein function analysis.
Benchmarks
These leaderboards are used to track progress in Protein Interface Prediction
Most implemented papers
Protein Interface Prediction using Graph Convolutional Networks
fouticus/pipgcn
•
•
NeurIPS 2017
We consider the prediction of interfaces between proteins, a challenging problem with important applications in drug discovery and design, and examine the performance of existing and newly proposed spatial graph convolution operators for this task.
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.
DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction
BioinfoMachineLearning/DIPS-Plus
•
•
In this work, we expand on a dataset recently introduced for this task, the Database of Interacting Protein Structures (DIPS), to present DIPS-Plus, an enhanced, feature-rich dataset of 42, 112 complexes for geometric deep learning of protein interfaces.
Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networks
Geometric deep learning has recently achieved great success in non-Euclidean domains, and learning on 3D structures of large biomolecules is emerging as a distinct research area.
