Year: 2024
Type: article
Abstract: 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.
Source: ACS Synthetic Biology
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, Tong Si
Institutions: Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Shenzhen Institute of Synthetic Biology, University of Chinese Academy of Sciences, State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Shandong University, Beijing University of Chemical Technology
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
SDG: Quality education
Open Access status: hybrid
Estimated APC paid: $4,500
Awards: G2837453184, G2919550980, G7405892787
OpenAlex ID: W4404145972