Using structure to predict function in protein biochemistry: a combined computational and wet lab approach
Using structure to predict function in protein biochemistry: a combined computational and wet lab approach
复制标题
使用结构预测蛋白质生物化学中的功能：计算和湿实验室相结合的方法
DOI：
10.1016/j.bpj.2021.11.2877
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发表时间：
2022
影响因子：
3.4
通讯作者：
Craig, Paul A.
中科院分区：
文献类型：
--
作者：
Koeppe, Julia R.;Craig, Paul A.
我们开发了一个基于真实探究的本科生生物化学实验课程。生物化学真实科学探究实验室（BASIL）使用计算和湿实验室相结合的方法来研究已知结构但功能未知的蛋白质。蛋白质数据库（Protein Data Bank，PDB）中有3800多个未知功能的结构。学生使用序列和结构比对工具的组合来研究这些结构，目的是识别可能的酶。然后，他们使用分子对接来预测什么样的模型底物适合接近拟议的活性位点。学生可以使用标准的湿实验室生物化学技术在实验室中生产目标酶进行表达和纯化，然后使用从对接研究中选择的模型底物进行动力学分析。我们评估他们作为学生的学习和他们作为科学家的研究方法，可视化，生物背景和蛋白质功能机制方面的成长。我们已经成功地将这门课程用于专业和非专业的生物化学实验课程。课程是模块化的，可以作为一个整体使用，也可以将各个部分并入现有的课程-无论是讲座还是实验。课程模块通过GitHub免费提供。我们欢迎有兴趣全部或部分采用课程的新合作者。我们可以通过虚拟会议提供同步支持，通过Slack提供异步支持。该项目得到了NSF IUSE 1709355的部分支持。
We have developed an undergraduate biochemistry lab curriculum based on authentic inquiry. The Biochemistry Authentic Scientific Inquiry Lab (BASIL) uses a combined computational and wet lab approach to study proteins of known structure but unknown function. There are over 3800 structures in the Protein Data Bank (PDB) that have unknown function. Students use a combination of sequence and structure alignment tools to study these structures with the goal of identifying possible enzymes. They then use molecular docking to predict what model substrates fit near a proposed active site. Students can produce the target enzymes in the lab using standard wet-lab biochemistry techniques for expression and purification, and they then perform kinetic assays with model substrates selected from their docking studies. We assess their learning as students and their growth as scientists in terms of research methods, visualization, biological context, and mechanisms of protein function. We have successfully used this curriculum in biochemistry lab courses for majors and non-majors. The curriculum is modular and can be used as a whole or individual parts may be incorporated into an existing course-either lecture or lab. The course modules are available for no charge via GitHub. We welcome new collaborators who are interested in adopting the curriculum in full or in part. We can offer synchronous support via virtual meetings and asynchronous support via Slack. This project is supported in part by NSF IUSE 1709355.