Cohort-Based Learning of Computational Protein Design: Engineering a Helical Protein To Improve Its Stability
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Beas Mukherjee [1] ; Soumya De [1]
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[1] Indian Institute of Technology Kharagpur, India
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- Localización: Journal of chemical education, ISSN 0021-9584, Vol. 103, Nº 1, 2026
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
Training in hands-on research is essential for developing core scientific skills in undergraduate students and expanding their career prospects in STEM fields. However, limited internship availabilities, financial constraints, and time demands often make these opportunities inaccessible. To address this gap, we designed and implemented a Course-based Undergraduate Research Experience (CURE) into a biochemistry curriculum through a multiweek computational protein-design project. A cohort of 79 upper-division undergraduate students with prior knowledge of protein chemistry were challenged with the research question: Can rationally designed mutations in an α helix of the model helical protein AvrPto enhance its overall structural stability? Using freely available computational tools such as PyMOL, Discovery Studio Visualizer, AGADIR, AlphaFold 3, Rosetta-suite, and GROMACS, students learned to design novel mutations in helix H1 of the AvrPto protein to enhance its α-helical propensity and study their effects on the overall protein structure, stability, and dynamics. The objective is to help students apply foundational concepts of chemistry to address a complex biological problem. Pedagogically, the study allows students to build transferable skills in protein design, molecular modeling, simulation workflows, and critical scientific reasoning. These skills are invaluable for careers in academia, biotechnology, and pharmaceutical industries. For educators, it provides a scalable framework for integrating authentic research into teaching that is adaptable across various chemistry and biochemistry programs.
