ArticleBioinformatics advances2025
Energy metric prediction for double insertion mutants via the RoseNet deep learning framework.
Article in Bioinformatics advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Summary: Studying the structural and functional implications of protein mutations is an important task in computational biology and bioinformatics. We leverage our previously proposed RoseNet neural network architecture to predict energy metrics of proteins with double amino acid insertions or deletions (InDels). We train models on previously generated benchmark datasets containing the exhaustive double InDel mutations for three proteins, as well as an additional three proteins for which Availability and implementation: The code used for training and evaluating the models in the study and the data underlying this article are available at https://github.com/hutchresearch/RoseNet.
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