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Yuan S, Bremmer A, Yang X, Li J, Hu Q. Splittable systems in biomedical applications. Biomater Sci 2024; 12:4103-4116. [PMID: 39012216 DOI: 10.1039/d4bm00709c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
Splittable systems have emerged as a powerful approach for the precise spatiotemporal control of biological processes. This concept relies on splitting a functional molecule into inactive fragments, which can be reassembled under specific conditions or stimuli to regain activity. Several binding pairs and orthogonal split fragments are introduced by fusing with other modalities to develop more complex and robust designs. One of the pillars of these splittable systems is modularity, which involves decoupling targeting, activation, and effector functions. Challenges, such as off-target effects and overactivation, can be addressed through precise control. This review provides an overview of the design principles, strategies, and applications of splittable systems across diverse fields including immunotherapy, gene editing, prodrug activation, biosensing, and synthetic biology.
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Affiliation(s)
- Sichen Yuan
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison (UW-Madison), Madison, Wisconsin 53705, USA.
- Wisconsin Center for NanoBioSystems, University of Wisconsin, Madison (UW-Madison), Madison, Wisconsin 53705, USA
- Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin, Madison (UW-Madison), Madison, Wisconsin 53705, USA
| | - Alexa Bremmer
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison (UW-Madison), Madison, Wisconsin 53705, USA.
| | - Xicheng Yang
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison (UW-Madison), Madison, Wisconsin 53705, USA.
| | - Jiayue Li
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison (UW-Madison), Madison, Wisconsin 53705, USA.
| | - Quanyin Hu
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison (UW-Madison), Madison, Wisconsin 53705, USA.
- Wisconsin Center for NanoBioSystems, University of Wisconsin, Madison (UW-Madison), Madison, Wisconsin 53705, USA
- Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin, Madison (UW-Madison), Madison, Wisconsin 53705, USA
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Peng CX, Liang F, Xia YH, Zhao KL, Hou MH, Zhang GJ. Recent Advances and Challenges in Protein Structure Prediction. J Chem Inf Model 2024; 64:76-95. [PMID: 38109487 DOI: 10.1021/acs.jcim.3c01324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine. Despite the remarkable achievements of artificial intelligence in the field, there are still some challenges and limitations. In this Review, we discuss the recent progress and some of the challenges in protein structure prediction. These challenges include predicting multidomain protein structures, protein complex structures, multiple conformational states of proteins, and protein folding pathways. Furthermore, we highlight directions in which further improvements can be conducted.
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Affiliation(s)
- Chun-Xiang Peng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Fang Liang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Yu-Hao Xia
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Kai-Long Zhao
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Ming-Hua Hou
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Gui-Jun Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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Yang Z, Zeng X, Zhao Y, Chen R. AlphaFold2 and its applications in the fields of biology and medicine. Signal Transduct Target Ther 2023; 8:115. [PMID: 36918529 PMCID: PMC10011802 DOI: 10.1038/s41392-023-01381-z] [Citation(s) in RCA: 78] [Impact Index Per Article: 78.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/27/2022] [Accepted: 02/16/2023] [Indexed: 03/16/2023] Open
Abstract
AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.
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Affiliation(s)
- Zhenyu Yang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiaoxi Zeng
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Yi Zhao
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Runsheng Chen
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
- Pingshan Translational Medicine Center, Shenzhen Bay Laboratory, Shenzhen, 518118, China.
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