Evidence map›Paper›PMID 35123996›Full record

ArticleJournal of molecular biology2022

A Similarity-Based Method for Predicting Enzymatic Functions in Yeast Uncovers a New AMP Hydrolase.

Nir Cohen, Amit Kahana, Maya Schuldiner

Open access · hybridAbstract read
In one paragraph

Article in Journal of molecular biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.7field-weighted citation impact, top 34% of its field
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

5 citing papers in PubMed, 8 citations in OpenAlex.

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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

3 authors at 2 institutions in 2 countries.

Nir CohenDepartment of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel.
Amit KahanaDepartment of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel. Electronic address: https://twitter.com/AmitKahana.
Maya SchuldinerDepartment of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel. Electronic address: maya.schuldiner@weizmann.ac.il.
Weizmann Institute of Science · ILTwitter (United States) · US

Funding

European Research Council 864068
6 · The paper itself

Abstract

Despite decades of research and the availability of the full genomic sequence of the baker's yeast Saccharomyces cerevisiae, still a large fraction of its genome is not functionally annotated. This hinders our ability to fully understand cellular activity and suggests that many additional processes await discovery. The recent years have shown an explosion of high-quality genomic and structural data from multiple organisms, ranging from bacteria to mammals. New computational methods now allow us to integrate these data and extract meaningful insights into the functional identity of uncharacterized proteins in yeast. Here, we created a database of sensitive sequence similarity predictions for all yeast proteins. We use this information to identify candidate enzymes for known biochemical reactions whose enzymes are unidentified, and show how this provides a powerful basis for experimental validation. Using one pathway as a test case we pair a new function for the previously uncharacterized enzyme Yhr202w, as an extra-cellular AMP hydrolase in the NAD degradation pathway. Yhr202w, which we now term Smn1 for Scavenger MonoNucleotidase 1, is a highly conserved protein that is similar to the human protein E5NT/CD73, which is associated with multiple cancers. Hence, our new methodology provides a paradigm, that can be adopted to other organisms, for uncovering new enzymatic functions of uncharacterized proteins.

Indexed as

Adenosine MonophosphateNucleotidasesSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsHumansSequence Analysis, ProteinAdenosine MonophosphateNucleotidasesSaccharomyces cerevisiae ProteinsYHR202W protein, S cerevisiaeAnalogYeastmetabolomicsSaccharomyces cerevisiaeSmn1Yhr202w

Identifiers

PMID35123996
PMCPMC9005783
OpenAlexW4210508568

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.