@inproceedings{
chen2024msagpt,
title={{MSAGPT}: Neural Prompting Protein Structure Prediction via {MSA} Generative Pre-Training},
author={Bo Chen and Zhilei Bei and Xingyi Cheng and Pan Li and Jie Tang and Le Song},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=pPeXYByHNd}
}
Multiple Sequence Alignment (MSA) plays a pivotal role in unveiling the evolutionary trajectories of protein families. The accuracy of protein structure predictions is often compromised for protein sequences that lack sufficient homologous information to construct high-quality MSA. Although various methods have been proposed to generate high-quality MSA under these conditions, they fall short in comprehensively capturing the intricate co-evolutionary patterns within MSA or require guidance from external oracle models. Here we introduce MSAGPT, a novel approach to prompt protein structure predictions via MSA generative pre-training in a low-MSA regime. MSAGPT employs a simple yet effective 2D evolutionary positional encoding scheme to model the complex evolutionary patterns. Endowed by this, the flexible 1D MSA decoding framework facilitates zero- or few-shot learning. Moreover, we demonstrate leveraging the feedback from AlphaFold2 (AF2) can further enhance the model’s capacity via Rejective Fine-tuning (RFT) and Reinforcement Learning from AF2 Feedback (RLAF). Extensive experiments confirm the efficacy of MSAGPT in generating faithful and informative MSA (up to +8.5% TM-Score on few-shot scenarios). The transfer learning also demonstrates its great potential for the wide range of tasks resorting to the quality of MSA.
MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training
Bo Chen Zhilei Bei Xingyi Cheng Pan Li Jie Tang Le Song