Evidence map›Paper›PMID 24998033›Full record

ReviewTrends in biochemical sciences2014

Leveraging structure for enzyme function prediction: methods, opportunities, and challenges.

Matthew P Jacobson, Chakrapani Kalyanaraman, Suwen Zhao, Boxue Tian

Abstract readReview
In one paragraph

Review in Trends in biochemical sciences, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Article
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  4. Leucine Aminopeptidase fromBiomolecules · 2026
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  7. Chemometric Models of Differential Amino Acids at the NaMolecules (Basel, Switzerland) · 2020
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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

4 authors.

Matthew P JacobsonDepartment of Pharmaceutical Chemistry, School of Pharmacy, University of California, San Francisco, CA 94158, USA; California Institute for Quantitative Biomedical Research, University of California, San Francisco, CA 94158, USA. Electronic address: matt.jacobson@ucsf.edu.
Chakrapani KalyanaramanDepartment of Pharmaceutical Chemistry, School of Pharmacy, University of California, San Francisco, CA 94158, USA; California Institute for Quantitative Biomedical Research, University of California, San Francisco, CA 94158, USA.
Suwen ZhaoDepartment of Pharmaceutical Chemistry, School of Pharmacy, University of California, San Francisco, CA 94158, USA; California Institute for Quantitative Biomedical Research, University of California, San Francisco, CA 94158, USA.
Boxue TianDepartment of Pharmaceutical Chemistry, School of Pharmacy, University of California, San Francisco, CA 94158, USA; California Institute for Quantitative Biomedical Research, University of California, San Francisco, CA 94158, USA.

Funding

Core H: MicrobiologyU54GM093342 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI CRONAN, JOHN E. · 2010 to 2015
$34.6M
NIGMS NIH HHS U54 GM093342
6 · The paper itself

Abstract

The rapid growth of the number of protein sequences that can be inferred from sequenced genomes presents challenges for function assignment, because only a small fraction (currently <1%) has been experimentally characterized. Bioinformatics tools are commonly used to predict functions of uncharacterized proteins. Recently, there has been significant progress in using protein structures as an additional source of information to infer aspects of enzyme function, which is the focus of this review. Successful application of these approaches has led to the identification of novel metabolites, enzyme activities, and biochemical pathways. We discuss opportunities to elucidate systematically protein domains of unknown function, orphan enzyme activities, dead-end metabolites, and pathways in secondary metabolism.

Indexed as

Models, MolecularAnimalsComputer SimulationHumansProtein ConformationProteinsStructure-Activity RelationshipSubstrate SpecificityProteinsdockingenzyme function predictionhomology modelingmetabolic pathwaysprotein structures

Identifiers

PMID24998033
PMCPMC4117707

What OpenQuestion holds

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Registered trials

None linked

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.