CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
ACS Synth Biol. 2024 Nov 15;13(11):3782-3787. doi: 10.1021/acssynbio.4c00588. Epub 2024 Nov 7.
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.
AlphaFold 在蛋白质结构预测方面的成功突出了数据驱动方法在科学研究中的强大作用。然而，开发具有理想功能的机器学习模型来设计和工程蛋白质受到高质量数据集和实验反馈有限的阻碍。蛋白质工程的关键评估（CAPE）挑战赛通过学生为重点的竞赛解决了这些问题，利用云计算和生物制造厂降低进入门槛。CAPE 是一个开放的社区学习平台，来自过去参赛者的突变数据集和设计算法有助于提高后续轮次的整体性能。通过两轮竞赛，学生参与者共同设计了超过 1500 个新的突变序列，表现最好的变体的催化活性比野生型亲本高 5 倍。我们设想 CAPE 是一个协作平台，吸引年轻研究人员并促进计算蛋白质工程。
