Integrating Computation and Wet Lab Methods in a Biochemistry Lab Course-Based Undergraduate Research Experience (Cure)
Integrating Computation and Wet Lab Methods in a Biochemistry Lab Course-Based Undergraduate Research Experience (Cure)
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将计算和湿实验室方法整合到基于生物化学实验室课程的本科生研究经验中（治愈）
DOI：
10.1016/j.bpj.2019.11.1786
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发表时间：
2020
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.;Ringer McDonald, Ashley;Roberts, Rebecca;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。蛋白质数据库中有超过3800个结构具有未知功能。学生使用结构生物信息学工具将这些结构与已知的酶进行比较并预测功能。计算机模拟模块包括使用PyMOL进行蛋白质可视化、使用Dali和ProMOL进行结构比对、使用BLAST和Pfam进行序列探索以及使用PyRX和Autodock维纳进行配体对接。目标是预测目标酶的可能功能，并为其活性位点识别有希望的底物。然后，学生使用标准的湿实验室生物化学技术来表达和纯化目标酶，并使用从对接研究中选择的底物进行动力学分析。我们正在评估他们作为学生的学习和他们作为科学家在研究方法，可视化，生物背景和蛋白质功能机制方面的成长。我们已经成功地在专业和非专业的生物化学实验室课程中使用了这门课程，并且我们已经调整了实验模块，以便在单学期或跨多门课程的单一课程中实施。我们最近为所有课程材料创建了一个GitHub存储库，我们欢迎希望在自己的校园采用该课程的新合作者。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. Over 3800 structures in the Protein Data Bank have unknown function. Students use structural bioinformatics tools to compare these structures to known enzymes and predict a function. The in silico modules include protein visualization with PyMOL, structural alignment using Dali and ProMOL, sequence exploration with BLAST and Pfam, and ligand docking with PyRX and Autodock Vina. The goal is to predict possible functions for the target enzymes and to identify promising substrates for their active sites. Students then use standard wet-lab biochemistry techniques to express and purify the target enzymes and perform kinetic assays with substrates selected from their docking studies. We are assessing 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, and we have adapted the experimental modules for implementation in a single course in a single term or across multiple courses. We recently created a GitHub repository for all course materials, and we welcome new collaborators who wish to adopt the curriculum on their own campuses. This project is supported in part by NSF IUSE 1709355.