ArticleMethods in molecular biology (Clifton, N.J.)2026
Enhanced Detection of Homology Using Artificial Intelligence in Euglenids.
Article in Methods in molecular biology (Clifton, N.J.), 2026. 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
Identification of similarity between protein sequences is an important component for the assignment of function. With ever-growing databases of genome sequence, this becomes an increasing challenge, and especially in the detection of relationships between distantly related sequences, which is frequently an issue with euglenids. The introduction of artificial intelligence tools to the prediction of protein structure has been, without exaggeration, revolutionary. In particular, AlphaFold3 (AF3), the latest iteration of the AI predictor from DeepMind, a Google subsidiary, offers a potent combination of speed, accuracy, and ease-of-use, all free of charge. Here I will describe a basic workflow for the detection of low similarity between proteins, that is otherwise cryptic, using AF3, discuss how to interpret the predictions, and highlight examples of bizarre predictions or hallucinations.
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