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A bioinformatics-driven course-based undergraduate research experience (CURE) project was developed and implemented in an upper-level biochemistry course to address the need for an accessible, lecture-based framework that integrates contemporary computational protein analysis into undergraduate biochemistry education. Students predicted the physicochemical and structural properties of uncharacterized, putative antimicrobial peptides using freely accessible databases, software, and web-based servers, including GenBank and UniProt databases, ExPASy ProtParam, SWISS-MODEL, AlphaFold 3, and PyMOL. Students were asked to identify key molecular features, formulate hypotheses to inform subsequent purification and functional investigations, and present their outputs in abstract, poster, and video formats. Student perceptions of learning and skill development were collected using a Likert scale survey and open-ended feedback. Strong perceived learning gains were highlighted by the Likert scale data, while positive learning experiences and skill development were prominent themes captured in the qualitative feedback. Group dynamics emerged as an important factor influencing the student experience, with both positive and negative perspectives reported. Altogether, the overall feedback was positive, showcasing that the CURE enhanced students’ understanding of protein biochemistry while developing computational, analytical, and scientific communication skills. Lastly, the CURE project described here provides a framework that can be adapted to various protein families beyond antimicrobial proteins.
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Acedo et al. (2026) studied this question.
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