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Knot or not? Identifying unknotted proteins in knotted families with sequence-based Machine Learning model
Maciej Sikora1,2, Eva Klimentova3,4, Dawid Uchal1,5
1Centre of New Technologies, University of Warsaw, Warsaw, Poland.
Protein Science : a Publication of the Protein Society
|June 18, 2024
Summary
This study uses AlphaFold predictions to identify knotted proteins in the UniProt database. A machine learning model predicts protein knots from sequences, revealing conserved knot structures in most families.
Area of Science:
- Structural biology
- Computational biology
- Machine learning
Background:
- Knotted proteins are rare but structurally vital, with ongoing research into their functions.
- AlphaFold provides extensive protein structure predictions, enabling large-scale computational studies.
Purpose of the Study:
- To computationally identify and analyze knotted and unknotted proteins within the UniProt database using AlphaFold predictions.
- To develop a machine learning model for predicting protein knot presence from amino acid sequences.
- To investigate the structural and evolutionary conservation of knots across protein families.
Main Methods:
- Utilized AlphaFold predictions to build a comprehensive dataset of knotted and unknotted proteins from UniProt.
- Developed and validated a machine learning model for sequence-based knot prediction.
- Classified knotted proteins into structural families and analyzed sequence conservation.
Main Results:
- Successfully created a robust dataset of knotted and unknotted proteins.
- Achieved 92% agreement on a test set of 100 proteins for sequence-based knot prediction.
- Identified that all predicted knotted proteins belong to only 17 families.
- Discovered three new families (UCH, DUF4253, DUF2254) with both knotted and unknotted members, potentially due to deletions.
- Found knotted topology is conserved in 11 out of 15 families, even with low sequence similarity.
- Determined that predicted unknotted proteins are structurally accurate but may be nonfunctional fragments.
Conclusions:
- Machine learning models can accurately predict protein knotting from amino acid sequences.
- Knotted protein structures are highly conserved within specific protein families.
- Unknotted proteins predicted by AlphaFold may represent truncated or non-functional variants.
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