Design of a TSR-based project learning strategy for biochemistry undergraduate teaching and research labs: a case study - Reaux, Camille R.; Meche, Shelby A.; Grider, Jordan M.; Dhanabal, Soundharya; Milon, Tarikul I.; Chen, Feng; Xu, Wu (Frontiers in Education, 2025)
Given the exponential growth of biochemical data and deep effect of computational methods on life sciences, there is a need to rethink undergraduate curricula. A project-oriented learning approach based on the Triangular Spatial Relationship (TSR) algorithm has been developed. The TSR-based method was designed for protein 3D structural comparison, motif discovery and probing molecular interactions. The uniqueness of the method benefits students’ learning of big data and computational methods. Specifically, students learn (i) how to search proteins of interest from the PDB archive, (ii) basic supercomputer skills, (iii) how to prepare datasets, (iv) how to perform protein structure and sequence analyses, (v) how to interpret the results, visualize protein structures and make graphs. Five specific strategies have been developed to achieve students’ highest potentials. (i) This lab exercise is designed as a project-oriented learning approach. (ii) The skills-first and concept-second approach is used. (iii) Students choose the proteins based on their interests. (iv) Students are encouraged to learn from each other to promote student–student interactions. (v) Students are required to write a report and/or present their studies. To assess students’ performance, we have developed an assessment rubric that includes (i) demonstration of supercomputer skills in job script preparation, submission and monitoring, (ii) skills in preparation of datasets, (iii) data analytical skills, (iv) project report, (v) presentation, and (vi) integration of the TSR-based method with other computational methods (e.g., molecular 3D structural visualization and protein sequence analysis). This project has been introduced in undergraduate biochemistry research and teaching labs for 4 years. Most students have learned the basic supercomputer skills as well as structure data analysis skills. Students’ feedback is positive and encouraging. It can be further developed as a module for an integrated computational chemistry lecture course.