Using Steered Molecular Dynamic Tension for Assessing Quality of Computational Protein Structure Models

Domain-PFP: Protein Function Prediction Using Function-Aware Domain Embedding Representations

Nature Communication Biology, 2023

Nabil Ibtehaz1, Yuki Kagaya2 and Daisuke Kihara1,2,*

1 Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
2 Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
* Correspondence Author:
Ibtehaz, N., Kagaya, Y. & Kihara, D. Domain-PFP allows protein function prediction using function-aware domain embedding representations.
Commun Biol 6, 1103 (2023).


Below are the data used in this work.

saved_models.zipTrained DomainGO-prob model weights and Domain-PFP KNN models
probe_embeddings.zipEmbeddings of the proteins from the PROBE benchmark dataset, computed by DomainGO-prob
cafa3_predictions.zipGO term prediction by DomainPFP on the CAFA3 dataset
data.zipData required to reproduce our experiments. Please refer to Experiments for more information
blast_ppi_database.zipDatabase files for function prediction using BLAST and PPI

Source Code

The source codes of Domain-PFP, along with detailed instructions of installation and usage are available in:

Online Platform

An easy to use version of Domain-PFP is available to run on Google Colab Notebook.