Evidence map›Paper›PMID 37503040›Full record

ArticlebioRxiv : the preprint server for biology2023

Deciphering Phage-Host Specificity Based on the Association of Phage Depolymerases and Bacterial Surface Glycan with Deep Learning.

Yiyan Yang, Keith Dufault-Thompson, Wei Yan, Tian Cai, Lei Xie, Xiaofang Jiang

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. 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

6 authors at 2 institutions in 1 country.

Yiyan YangNational Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA.
Keith Dufault-ThompsonNational Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA.
Wei YanNational Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA.
Tian CaiPh.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY 10016, USA.
Lei XiePh.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY 10016, USA.
Xiaofang JiangNational Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA.
National Institutes of Health · USThe Graduate Center, CUNY · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Phage tailspike proteins are depolymerases that target diverse bacterial surface glycans with high specificity, determining the host-specificity of numerous phages. To address the challenge of identifying tailspike proteins due to their sequence diversity, we developed SpikeHunter, an approach based on the ESM-2 protein language model. Using SpikeHunter, we successfully identified 231,965 tailspike proteins from a dataset comprising 8,434,494 prophages found within 165,365 genomes of five common pathogens. Among these proteins, 143,035 tailspike proteins displayed strong associations with serotypes. Moreover, we observed highly similar tailspike proteins in species that share closely related serotypes. We found extensive domain swapping in all five species, with the C-terminal domain being significantly associated with host serotype highlighting its role in host range determination. Our study presents a comprehensive cross-species analysis of tailspike protein to serotype associations, providing insights applicable to phage therapy and biotechnology.

Identifiers

PMID37503040
PMCPMC10370184
OpenAlexW4381053493

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.