Critical Assessment of Protein Engineering (CAPE): A Student Challenge on the Cloud

IF 3.7 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

ACS Synthetic Biology Pub Date : 2024-11-07 DOI:10.1021/acssynbio.4c0058810.1021/acssynbio.4c00588

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*,

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引用次数: 0

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.

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蛋白质工程批判性评估 (CAPE)：云上的学生挑战赛

AlphaFold 在蛋白质结构预测方面的成功凸显了数据驱动方法在科学研究中的力量。然而，由于获得高质量数据集和实验反馈的途径有限，开发机器学习模型以设计和改造具有理想功能的蛋白质的工作受到了阻碍。蛋白质工程关键评估（CAPE）挑战赛通过以学生为中心的竞赛，利用云计算和生物设施降低准入门槛，解决了这些问题。CAPE 是一个开放的社区学习平台，来自往届参赛者的突变数据集和设计算法有助于提高后续比赛的整体表现。通过两轮比赛，学生参赛者共设计出 1500 个新的突变序列，其中表现最好的变体的催化活性比野生型亲本高出 5 倍。我们设想将 CAPE 作为一个合作平台，让年轻研究人员参与其中，促进计算蛋白质工程的发展。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACS Synthetic Biology 生物-

CiteScore

8.00

自引率

10.60%

发文量

380

审稿时长

6-12 weeks

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.