Related Experiment Video
Updated: Jun 23, 2026
16:41
A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
Testing the reliability of AI-generated protein structures
Biorxiv : the Preprint Server for Biology
|June 22, 2026
Summary
AlphaFold2 and ColabFold have a low false positive rate in protein structure prediction. Unexpectedly, some high-scoring predictions revealed un-annotated pseudogenes in the human genome.
Area of Science:
- Computational Biology
- Genomics
- Structural Biology
Background:
- Protein structure prediction tools like AlphaFold2 and ColabFold show high accuracy.
- Understanding the limitations and potential false positive rates of these tools is crucial for reliable biological interpretation.
Purpose of the Study:
- To investigate the reliability and false positive rate of AlphaFold2 and ColabFold.
- To assess the likelihood of these tools generating high-scoring structures for non-protein sequences.
Main Methods:
- Generated realistic but non-protein sequences.
- Utilized ColabFold for structure prediction on these sequences.
- Analyzed the frequency of high-scoring predictions for non-protein sequences.
Main Results:
- AlphaFold2 exhibits a small but non-zero false positive rate, estimated at approximately 1 in 435 using a pLDDT score threshold of 70.
- Identified previously unknown and un-annotated pseudogenes in the human genome among high-scoring sequences.
- These findings suggest potential inaccuracies in current human genome annotations regarding 5' untranslated regions of protein-coding genes.
Conclusions:
- AlphaFold2 and ColabFold are highly reliable, with a low false positive rate.
- High-scoring predictions, even from non-coding regions, warrant further investigation for novel biological discoveries.
- The study highlights the potential of structure prediction tools to uncover previously un-annotated genomic elements like pseudogenes.
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