@inproceedings{
guo2024enhancing,
title={Enhancing Protein Mutation Effect Prediction through a Retrieval-Augmented Framework},
author={Ruihan Guo and Rui Wang and Ruidong Wu and Zhizhou Ren and Jiahan Li and Shitong Luo and Zuofan Wu and qiang liu and Jian Peng and Jianzhu Ma},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=LgeHswiWef}
}
Predicting the effects of protein mutations is crucial for analyzing protein functions and understanding genetic diseases. 
However, existing models struggle to effectively extract mutation-related local structure motifs from protein databases, which hinders their predictive accuracy and robustness. To tackle this problem, we design a novel retrieval-augmented framework for incorporating similar structure information in known protein structures. We create a vector database consisting of local structure motif embeddings from a pre-trained protein structure encoder, which allows for efficient retrieval of similar local structure motifs during mutation effect prediction. 
Our findings demonstrate that leveraging this method results in the SOTA performance across multiple protein mutation prediction datasets, and offers a scalable solution for studying mutation effects.
Enhancing Protein Mutation Effect Prediction through a Retrieval-Augmented Framework
Ruihan Guo Rui Wang Ruidong Wu Zhizhou Ren Jiahan Li Shitong Luo Zuofan Wu qiang liu Jian Peng Jianzhu Ma