Evidence map›Paper›PMID 30910961›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2019

Enzyme annotation for orphan and novel reactions using knowledge of substrate reactive sites.

Noushin Hadadi, Homa MohammadiPeyhani, Ljubisa Miskovic, Marianne Seijo, Vassily Hatzimanikatis

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

43 citing papers in PubMed.

  1. The history of enzyme evolution embedded in metabolism.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
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  3. Dual-encoder contrastive learning accelerates enzyme discovery.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  4. Review
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  6. Review
  7. Article
  8. C11orf54 catalyzes L-xylulose formation in human metabolism.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  9. Article
  10. RC-GNN: A predictive model of enzyme-reaction pairs.bioRxiv : the preprint server for biology · 2025
    Article
  11. Article
  12. Article
  13. EquilibraTor streamlines molecular dynamics simulations in a single execution.Computational and structural biotechnology journal · 2025
    Article
  14. Article
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  17. Article
  18. From microbiome composition to functional engineering, one step at a time.Microbiology and molecular biology reviews : MMBR · 2023
    Review
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Noushin HadadiLaboratory of Computational Systems Biology, Institute of Chemistry and Chemical Engineering, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.
Homa MohammadiPeyhaniLaboratory of Computational Systems Biology, Institute of Chemistry and Chemical Engineering, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.
Ljubisa MiskovicLaboratory of Computational Systems Biology, Institute of Chemistry and Chemical Engineering, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.ORCID 0000-0001-7333-8211
Marianne SeijoLaboratory of Computational Systems Biology, Institute of Chemistry and Chemical Engineering, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.
Vassily HatzimanikatisLaboratory of Computational Systems Biology, Institute of Chemistry and Chemical Engineering, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland vassily.hatzimanikatis@epfl.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Thousands of biochemical reactions with characterized activities are "orphan," meaning they cannot be assigned to a specific enzyme, leaving gaps in metabolic pathways. Novel reactions predicted by pathway-generation tools also lack associated sequences, limiting protein engineering applications. Associating orphan and novel reactions with known biochemistry and suggesting enzymes to catalyze them is a daunting problem. We propose the method BridgIT to identify candidate genes and catalyzing proteins for these reactions. This method introduces information about the enzyme binding pocket into reaction-similarity comparisons. BridgIT assesses the similarity of two reactions, one orphan and one well-characterized nonorphan reaction, using their substrate reactive sites, their surrounding structures, and the structures of the generated products to suggest enzymes that catalyze the most-similar nonorphan reactions as candidates for also catalyzing the orphan ones. We performed two large-scale validation studies to test BridgIT predictions against experimental biochemical evidence. For the 234 orphan reactions from the Kyoto Encyclopedia of Genes and Genomes (KEGG) 2011 (a comprehensive enzymatic-reaction database) that became nonorphan in KEGG 2018, BridgIT predicted the exact or a highly related enzyme for 211 of them. Moreover, for 334 of 379 novel reactions in 2014 that were later cataloged in KEGG 2018, BridgIT predicted the exact or highly similar enzymes. BridgIT requires knowledge about only four connecting bonds around the atoms of the reactive sites to correctly annotate proteins for 93% of analyzed enzymatic reactions. Increasing to seven connecting bonds allowed for the accurate identification of a sequence for nearly all known enzymatic reactions.

Indexed as

Databases, ProteinEnzymesMolecular Sequence AnnotationSequence Analysis, ProteinBinding SitesEnzymesnovel (de novo) reactionsorphan reactionsreaction similarityreactive site recognitionsequence similarity

Identifiers

PMID30910961
PMCPMC6462048

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.