Accurate and robust protein sequence design with CarbonDesign

doi:10.1038/s42256-024-00838-2

	Accurate and robust protein sequence design with CarbonDesign
	Ren, Milong 1,2; Yu, Chungong 1,2,3; Bu, Dongbo 1,2,3; Zhang, Haicang 1,2,3
	2024-05-01
发表期刊	NATURE MACHINE INTELLIGENCE
卷号	6 期号:5 页码:14
摘要	Protein sequence design is critically important for protein engineering. Despite recent advancements in deep learning-based methods, achieving accurate and robust sequence design remains a challenge. Here we present CarbonDesign, an approach that draws inspiration from successful ingredients of AlphaFold and which has been developed specifically for protein sequence design. At its core, CarbonDesign introduces Inverseformer, which learns representations from backbone structures and an amortized Markov random fields model for sequence decoding. Moreover, we incorporate other essential AlphaFold concepts into CarbonDesign: an end-to-end network recycling technique to leverage evolutionary constraints from protein language models and a multitask learning technique for generating side-chain structures alongside designed sequences. CarbonDesign outperforms other methods on independent test sets including the 15th Critical Assessment of protein Structure Prediction (CASP15) dataset, the Continuous Automated Model Evaluation (CAMEO) dataset and de novo proteins from RFDiffusion. Furthermore, it supports zero-shot prediction of the functional effects of sequence variants, making it a promising tool for applications in bioengineering. Deep learning has led to great advances in predicting protein structure from sequences. Ren and colleagues present here a method for the inverse problem of finding a sequence that results in a desired protein structure, which is inspired by various components of AlphaFold combined with Markov random fields to decode sequences more efficiently.
DOI	10.1038/s42256-024-00838-2
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China (National Science Foundation of China)[32271297] ; National Natural Science Foundation of China (National Science Foundation of China)[62072435] ; National Natural Science Foundation of China ; Project of Youth Innovation Promotion Association CAS[2020YFA0907000] ; Development Program of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:001230286600010
出版者	NATURE PORTFOLIO
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40081
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Bu, Dongbo; Zhang, Haicang
作者单位	1.Chinese Acad Sci, Inst Comp Technol, SKLP, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Cent China Inst Artificial Intelligence, Zhengzhou, Peoples R China
推荐引用方式 GB/T 7714	Ren, Milong,Yu, Chungong,Bu, Dongbo,et al. Accurate and robust protein sequence design with CarbonDesign[J]. NATURE MACHINE INTELLIGENCE,2024,6(5):14.
APA	Ren, Milong,Yu, Chungong,Bu, Dongbo,&Zhang, Haicang.(2024).Accurate and robust protein sequence design with CarbonDesign.NATURE MACHINE INTELLIGENCE,6(5),14.
MLA	Ren, Milong,et al."Accurate and robust protein sequence design with CarbonDesign".NATURE MACHINE INTELLIGENCE 6.5(2024):14.