杠杆(统计)
计算机科学
纳米技术
生化工程
系统工程
数据科学
工程类
人工智能
材料科学
作者
Yiming Wang,Kathleen J. Stebe,César de la Fuente‐Núñez,Ravi Radhakrishnan
标识
DOI:10.1021/acsabm.2c01023
摘要
Computer-aided molecular design and protein engineering emerge as promising and active subjects in bioengineering and biotechnological applications. On one hand, due to the advancing computing power in the past decade, modeling toolkits and force fields have been put to use for accurate multiscale modeling of biomolecules including lipid, protein, carbohydrate, and nucleic acids. On the other hand, machine learning emerges as a revolutionary data analysis tool that promises to leverage physicochemical properties and structural information obtained from modeling in order to build quantitative protein structure–function relationships. We review recent computational works that utilize state-of-the-art computational methods to engineer peptides and proteins for various emerging biomedical, antimicrobial, and antifreeze applications. We also discuss challenges and possible future directions toward developing a roadmap for efficient biomolecular design and engineering.
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