计算机科学
蛋白质结构预测
深度学习
人工智能
端到端原则
机器学习
蛋白质结构
数据科学
生物
生物化学
作者
Zhenling Peng,Wenkai Wang,Renmin Han,Fa Zhang,Jianyi Yang
标识
DOI:10.1016/j.sbi.2022.102495
摘要
Significant advances have been achieved in protein structure prediction, especially with the recent development of the AlphaFold2 and the RoseTTAFold systems. This article reviews the progress in deep learning-based protein structure prediction methods in the past two years. First, we divide the representative methods into two categories: the two-step approach and the end-to-end approach. Then, we show that the two-step approach is possible to achieve similar accuracy to the state-of-the-art end-to-end approach AlphaFold2. Compared to the end-to-end approach, the two-step approach requires fewer computing resources. We conclude that it is valuable to keep developing both approaches. Finally, a few outstanding challenges in function-orientated protein structure prediction are pointed out for future development.
科研通智能强力驱动
Strongly Powered by AbleSci AI