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Student 10-Minute Presentation

Systematics, Evolution, and Biodiversity

Student

Student Competition

Grad 10-min Competition: SysEB, Evolution and Ecology

Leveraging structural prediction to understand the genomic repertoire of olfactory genes in beetles

Monday, November 11, 2024
11:30 AM – 11:42 AM MST
Location: Phoenix Convention Center, 225 B, PCC
Oral Presentation Student Competition
Advances in artificial intelligence are providing an exciting path for tackling some of our most challenging problems in biology and beyond. Exemplifying these innovations, deep learning models have received wide-spread interest and attention for their power and accuracy for predicting the structure and folding of proteins from their primary amino acid sequence alone. For non-model organisms, these methods hold particular promise for investigating aspects of protein biology and evolution in resource-poor systems without robust biochemical or laboratory resources in-place. Here we applied state-of-the-art deep learning models for structural prediction to explore the diversity of olfactory receptor proteins in beetles. Recently, we sequenced, assembled, and annotated the genome of the Red Milkweed longhorned beetle (Tetraopes tetraopthalmus), and we use this resource here as a genomic case study for predicting the structure of olfactory receptor proteins. Comparisons with the Asian longhorned beetle (Anoplophora glabripennis) provided insights into the evolutionary diversity of olfactory receptors in these two Cerambycids with markedly different degrees of host specialization and other life history traits. Collectively, our results highlight the promise of deep learning for studying the structure and diversity of proteins in found in the genomes of non-model insects.