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.
Authors
- Yongcan Chen (ORCID: https://orcid.org/0009-0002-3164-8953)
- Yuan Gao (ORCID: https://orcid.org/0000-0001-9714-4219)
- Shujun Tian (ORCID: https://orcid.org/0000-0002-5573-3530)
- Tong Si (ORCID: https://orcid.org/0000-0003-2985-9014)
- Zichuan Chen
- Hao Dong (ORCID: https://orcid.org/0000-0001-7280-7506)
- Xiaoting Fang (ORCID: https://orcid.org/0000-0002-2065-4920)
- Lihao Fu
- Yanjing Wang (ORCID: https://orcid.org/0000-0001-6584-4616)
- Zechen Wang (ORCID: https://orcid.org/0000-0003-4554-133X)
- Xiao Yi (ORCID: https://orcid.org/0000-0003-4025-856X)
- Yijian Zhang (ORCID: https://orcid.org/0009-0005-5552-0681)
- Shantong Hu (ORCID: https://orcid.org/0009-0008-7959-2112)
Institutions
- Shandong University (CN)
- Chinese Academy of Sciences (CN)
- Shenzhen Institutes of Advanced Technology (CN)
- Viva Biotech (China) (CN)
- University of Chinese Academy of Sciences (CN)
- Beijing University of Chemical Technology (CN)
- Nanjing University (CN)
Publication Details
- Journal
- ACS Synthetic Biology
- Published
- 2024-11-07
- DOI
- https://doi.org/10.1021/acssynbio.4c00588
- Citations
- 7
- Primary Topic
- Biomedical and Engineering Education
- Type
- article
- Field-Weighted Citation Impact
- 1.29
Funders
- Shenzhen Institute of Synthetic Biology
- Chinese Academy of Sciences
- National Key Research and Development Program of China