Folding the Unfoldable 2: using AlphaFold and ESMFold to explore spurious proteins

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ID: 317007
2026
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Abstract
Abstract Motivation Spurious protein sequences, resulting from gene prediction errors, theoretically should not yield folded structures. AlphaFold2 was previously shown to predict short spurious sequences with high pLDDT scores and was therefore unlikely to distinguish between real proteins and spurious proteins which are usually short. We evaluate whether newer structure prediction methods (ESMFold and AlphaFold3) similarly predict short sequences with high pLDDT or if they better discriminate between spurious and real proteins. Results All three structure prediction methods (ESMFold, AlphaFold2, and AlphaFold3) predict short spurious sequences from AntiFam with unexpectedly high pLDDT scores, however the discrimination between spurious and real proteins improves beyond 100 amino acids. By analysing sequences with disparate pTM and pLDDT scores, we identified two potentially novel spurious shadow ORFs in Swiss-Prot and one potentially non-spurious AntiFam entry. Using the structure prediction scores, we developed a Gaussian Process Model and evaluated its performance on AlphaFold DB, identifying potential spurious proteins at scale. While limited on its own, this model can increase confidence in spurious protein identification when combined with other methods. Availability and implementation Structure predictions are available at https://doi.org/10.5281/zenodo.20426908. Model implementation and figure generation code are available at https://github.com/0rra/fold_unfold2.
Reference Key
openalex_W7164301502 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ailsa K Orr, Alex Bateman
Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag160
URL
https://doi.org/10.1093/bioadv/vbag160
Keywords Keywords not found

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