A curated list of Contact Prediction and related area.
- Enhancing protein inter-residue real distance prediction by scrutinising deep learning models. [paper] [code]
- Accurate protein function prediction via graph attention networks with predicted structure information. [paper]
- OntoProtein: Protein Pretraining With Gene Ontology Embedding. [paper] [code] train
- Geometric Transformers for Protein Interface Contact Prediction. [paper] [code] (multimer)
- DNCON2_Inter: predicting interchain contacts for homodimeric and homomultimeric protein complexes using multiple sequence alignments of monomers and deep learning. [paper] [code]
- Inter-protein contact map generated only from intra-monomer by image inpainting. [paper] [code] (multimer)
- When homologous sequences meet structural decoys: Accurate contact prediction by tFold in CASP14—(tFold for CASP14 contact prediction). [paper]
- Deep graph learning of inter-protein contacts. [paper] [code] (multimer)
- A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers. [paper] [code]
- SPOT-Contact-Single: Improving Single-Sequence-Based Prediction of Protein Contact Map using a Transformer Language Model, Large Training Set and Ensembled Deep Learning. [paper]
- Accurate prediction of inter-protein residue–residue contacts for homo-oligomeric protein complexes. [paper]
- Language models enable zero-shot prediction of the effects of mutations on protein function. [paper]
- Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. [paper] [code]
- ProtTrans: Towards Cracking the Language of Lifes Code Through Self-Supervised Deep Learning and High Performance Computing. [paper] [code]
- Learning unknown from correlations: Graph neural network for inter-novel-protein interaction prediction. [paper] [code] train
- Transformer protein language models are unsupervised structure learners. [paper]
- BERTology Meets Biology: Interpreting Attention in Protein Language Models. [paper] [code]
- Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model. [paper]
- Improved protein structure prediction using predicted interresidue orientations. [paper]
- Deep Learning of High-Order Interactions for Protein Interface Prediction. [paper]
- Evaluating Protein Transfer Learning with TAPE. [paper] [code]
- Generative Models for Graph-Based Protein Design. [paper] [code]
- End-to-End Learning on 3D Protein Structure for Interface Prediction. [paper] [code]
- ComplexContact: a web server for inter-protein contact prediction using deep learning. [paper] (multimer)
- Protein Interface Prediction using Graph Convolutional Networks. [paper] [code] [comment]: <> (https://github.com/Yijia-Xiao/Undergrad-protein-pretrain)