DLA-Ranker: Evaluating protein docking conformations with many locally oriented cubes.
BEHBAHANI, Y. M.; LAINE, E.; CARBONE, A.
Show abstract
Proteins ensure their biological functions by interacting with each other, and with other molecules. Determining the relative position and orientation of protein partners in a complex remains challenging. Here, we address the problem of ranking candidate complex conformations toward identifying near-native conformations. We propose a deep learning approach relying on a local representation of the protein interface with an explicit account of its geometry. We show that the method is able to recognise certain pattern distributions in specific locations of the interface. We compare and combine it with a physics-based scoring function and a statistical pair potential.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- InterPepScore: A Deep Learning Score for Improving the FlexPepDock Refinement Protocol 96%
- A Gated Graph Transformer for Protein ComplexStructure Quality Assessment and its Performancein CASP15 96%
- QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks 96%
Similar papers in this journal
- ArtiDock: accurate Machine Learning approach to protein-ligand docking optimized for high-throughput virtual screening 95%
- Accurate Conformation Sampling via Protein Structural Diffusion 95%
- RosENet: Improving binding affinity prediction by leveraging molecular mechanics energies with a 3D Convolutional Neural Network 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.