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Updated: May 14, 2026
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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
How Not to be Seen: Predicting Unseen Enzyme Functions using Contrastive Learning
Xiang Ma1, Parnal Joshi2, Iddo Friedberg2
1Computer Science, Iowa State University, 50014, IA, USA.
Biorxiv : the Preprint Server for Biology
|May 13, 2026
Summary
Enzyme function prediction from sequence is challenging. EnzPlacer uses contrastive learning to accurately place novel enzyme sequences into known functional contexts, aiding experimental characterization.
Area of Science:
- Bioinformatics
- Enzymology
- Computational Biology
Background:
- Predicting enzyme function from amino acid sequence remains a significant challenge in life sciences.
- The rapid growth of genomic data yields numerous uncharacterized enzyme sequences.
- Accurately contextualizing these sequences within known functional space is crucial for hypothesis generation.
Purpose of the Study:
- To develop a novel computational method for predicting enzyme function from sequence.
- To accurately place uncharacterized enzyme sequences into a narrowed functional context.
- To aid experimentalists in generating falsifiable hypotheses for enzyme characterization.
Main Methods:
- A contrastive learning algorithm named EnzPlacer was developed.
- The algorithm predicts the top three Enzyme Commission (EC) numbers (first, second, and third) for a given protein sequence.
- This approach is effective even when the most specific EC number (fourth) is unknown or not in the training data.
Main Results:
- EnzPlacer accurately predicts the functional context of enzyme sequences.
- The method successfully places proteins into functional families, even without complete functional annotation.
- This provides a valuable tool for prioritizing experimental characterization efforts.
Conclusions:
- EnzPlacer offers a robust solution for predicting enzyme function from sequence data.
- The algorithm enhances the ability to place novel sequences within the known enzyme function landscape.
- This facilitates more targeted and efficient biochemical characterization of enzymes.
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