{"title":"蛋白质工程批判性评估 (CAPE):云上的学生挑战赛","authors":"Lihao Fu, Yuan Gao, Yongcan Chen, Yanjing Wang, Xiaoting Fang, Shujun Tian, Hao Dong, Yijian Zhang, Zichuan Chen, Zechen Wang, Shantong Hu, Xiao Yi* and Tong Si*, ","doi":"10.1021/acssynbio.4c0058810.1021/acssynbio.4c00588","DOIUrl":null,"url":null,"abstract":"<p >The success of AlphaFold in protein structure prediction highlights the power of data-driven approaches in scientific research. However, developing machine learning models to design and engineer proteins with desirable functions is hampered by limited access to high-quality data sets and experimental feedback. The Critical Assessment of Protein Engineering (CAPE) challenge addresses these issues through a student-focused competition, utilizing cloud computing and biofoundries to lower barriers to entry. CAPE serves as an open platform for community learning, where mutant data sets and design algorithms from past contestants help improve overall performance in subsequent rounds. Through two competition rounds, student participants collectively designed >1500 new mutant sequences, with the best-performing variants exhibiting catalytic activity up to 5-fold higher than the wild-type parent. We envision CAPE as a collaborative platform to engage young researchers and promote computational protein engineering.</p>","PeriodicalId":26,"journal":{"name":"ACS Synthetic Biology","volume":"13 11","pages":"3782–3787 3782–3787"},"PeriodicalIF":3.7000,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acssynbio.4c00588","citationCount":"0","resultStr":"{\"title\":\"Critical Assessment of Protein Engineering (CAPE): A Student Challenge on the Cloud\",\"authors\":\"Lihao Fu, Yuan Gao, Yongcan Chen, Yanjing Wang, Xiaoting Fang, Shujun Tian, Hao Dong, Yijian Zhang, Zichuan Chen, Zechen Wang, Shantong Hu, Xiao Yi* and Tong Si*, \",\"doi\":\"10.1021/acssynbio.4c0058810.1021/acssynbio.4c00588\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >The success of AlphaFold in protein structure prediction highlights the power of data-driven approaches in scientific research. However, developing machine learning models to design and engineer proteins with desirable functions is hampered by limited access to high-quality data sets and experimental feedback. The Critical Assessment of Protein Engineering (CAPE) challenge addresses these issues through a student-focused competition, utilizing cloud computing and biofoundries to lower barriers to entry. CAPE serves as an open platform for community learning, where mutant data sets and design algorithms from past contestants help improve overall performance in subsequent rounds. Through two competition rounds, student participants collectively designed >1500 new mutant sequences, with the best-performing variants exhibiting catalytic activity up to 5-fold higher than the wild-type parent. We envision CAPE as a collaborative platform to engage young researchers and promote computational protein engineering.</p>\",\"PeriodicalId\":26,\"journal\":{\"name\":\"ACS Synthetic Biology\",\"volume\":\"13 11\",\"pages\":\"3782–3787 3782–3787\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://pubs.acs.org/doi/epdf/10.1021/acssynbio.4c00588\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Synthetic Biology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://pubs.acs.org/doi/10.1021/acssynbio.4c00588\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Synthetic Biology","FirstCategoryId":"99","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acssynbio.4c00588","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Critical Assessment of Protein Engineering (CAPE): A Student Challenge on the Cloud
The success of AlphaFold in protein structure prediction highlights the power of data-driven approaches in scientific research. However, developing machine learning models to design and engineer proteins with desirable functions is hampered by limited access to high-quality data sets and experimental feedback. The Critical Assessment of Protein Engineering (CAPE) challenge addresses these issues through a student-focused competition, utilizing cloud computing and biofoundries to lower barriers to entry. CAPE serves as an open platform for community learning, where mutant data sets and design algorithms from past contestants help improve overall performance in subsequent rounds. Through two competition rounds, student participants collectively designed >1500 new mutant sequences, with the best-performing variants exhibiting catalytic activity up to 5-fold higher than the wild-type parent. We envision CAPE as a collaborative platform to engage young researchers and promote computational protein engineering.
期刊介绍:
The journal is particularly interested in studies on the design and synthesis of new genetic circuits and gene products; computational methods in the design of systems; and integrative applied approaches to understanding disease and metabolism.
Topics may include, but are not limited to:
Design and optimization of genetic systems
Genetic circuit design and their principles for their organization into programs
Computational methods to aid the design of genetic systems
Experimental methods to quantify genetic parts, circuits, and metabolic fluxes
Genetic parts libraries: their creation, analysis, and ontological representation
Protein engineering including computational design
Metabolic engineering and cellular manufacturing, including biomass conversion
Natural product access, engineering, and production
Creative and innovative applications of cellular programming
Medical applications, tissue engineering, and the programming of therapeutic cells
Minimal cell design and construction
Genomics and genome replacement strategies
Viral engineering
Automated and robotic assembly platforms for synthetic biology
DNA synthesis methodologies
Metagenomics and synthetic metagenomic analysis
Bioinformatics applied to gene discovery, chemoinformatics, and pathway construction
Gene optimization
Methods for genome-scale measurements of transcription and metabolomics
Systems biology and methods to integrate multiple data sources
in vitro and cell-free synthetic biology and molecular programming
Nucleic acid engineering.