Name: Replication Data for: Geometric Transformers for Protein Interface Contact Prediction
Keywords: Computational biology, Protein interfaces, Protein-protein interactions, Transformers, Geometric deep learning, Neural networks, Protein bioinformatics, Graph neural networks

Research datakeyboard_double_arrow_right Dataset 05 Oct 2021Publisher:Zenodo

Authors: Morehead, Alex; Chen, Chen; Cheng, Jianlin;

doi: 10.5281/zenodo.6299835 , 10.5281/zenodo.5546775 , 10.5281/zenodo.5797024 , 10.5281/zenodo.6671582 , 10.5281/zenodo.5546774

Replication Data for: Geometric Transformers for Protein Interface Contact Prediction

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Abstract

This dataset contains replication data for the paper titled "Geometric Transformers for Protein Interface Contact Prediction". The dataset consists of pickled Python dictionaries containing pairs of DGLGraphs that can be used to train and validate protein interface contact prediction models. It also contains our best model checkpoints saved as PyTorch LightningModules. Our GitHub repository, DeepInteract, linked in the "Additional notes" metadata section below provides more details on how we use these files as examples for cross-validation.

This dataset can be used for training and cross-validation of protein interface contact prediction models via our GitHub repository for DeepInteract (https://github.com/BioinfoMachineLearning/DeepInteract).

Related Organizations

University of Missouri Health System
United States
University of Missouri
United States
UNIVERSITY OF MISSOURI-COLUMBIA

Keywords

Computational biology, Protein interfaces, Protein-protein interactions, Transformers, Geometric deep learning, Neural networks, Protein bioinformatics, Graph neural networks

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