Awesome Protein Representation Learning

Code for ICLR 2024 (Spotlight) paper "MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding"

Source code for "Learning protein sequence embeddings using information from structure" - ICLR 2019

Multi-task and masked language model-based protein sequence embedding models.

Published in PLOS ONE. Phage-host interaction prediction tool that uses protein language models to represent the receptor-binding proteins of phages. It presents improvements over using handcrafted sequence properties and eliminates the need to manually extract and select features from phage sequences

Simple python interface for the OpenProtein.AI REST API.

Phage-host interaction prediction tool that incorporates protein structure information in representing receptor-binding proteins (RBPs). It improves performance especially for phages with RBPs that have low sequence similarity to those of known phages

Code for Binding Affinity Prediction with Graph Neural Networks

Generation Co-expression Network Embeddings (CxNEs) for plant genes using Graph Attention Networks (GAT))