Evidence map›Paper›PMID 32978129›Full record

ArticleApplied and environmental microbiology2020

Improved Virus Isoelectric Point Estimation by Exclusion of Known and Predicted Genome-Binding Regions.

Joe Heffron, Brooke K Mayer

Open access · greenAbstract read
In one paragraph

Article in Applied and environmental microbiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.8field-weighted citation impact, top 16% 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

9 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
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  3. Review
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  7. Review
  8. Article
  9. Virus Isoelectric Point Estimation: Theories and Methods.Applied and environmental microbiology · 2021
    Review
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

2 authors at 1 institution in 1 country.

Joe HeffronDepartment of Civil, Construction and Environmental Engineering, Marquette University, Milwaukee, Wisconsin, USA joseph.heffron@marquette.edu brooke.mayer@marquette.edu.ORCID 0000-0002-5110-9271
Brooke K MayerDepartment of Civil, Construction and Environmental Engineering, Marquette University, Milwaukee, Wisconsin, USA joseph.heffron@marquette.edu brooke.mayer@marquette.edu.ORCID 0000-0002-4858-2834
Marquette University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Knowledge of the isoelectric points (pIs) of viruses is beneficial for predicting virus behavior in environmental transport and physical/chemical treatment applications. However, the empirically measured pIs of many viruses have thus far defied simple explanation, let alone prediction, based on the ionizable amino acid composition of the virus capsid. Here, we suggest an approach for predicting the pI of nonenveloped viruses by excluding capsid regions that stabilize the virus polynucleotide via electrostatic interactions. This method was applied first to viruses with known polynucleotide-binding regions (PBRs) and/or three-dimensional (3D) structures. Then, PBRs were predicted in a group of 32 unique viral capsid proteome sequences via conserved structures and sequence motifs. Removing predicted PBRs resulted in a significantly better fit to empirical pI values. After modification, mean differences between theoretical and empirical pI values were reduced from 2.1 ± 2.4 to 0.1 ± 1.7 pH units.

Indexed as

Genome, ViralCapsidIsoelectric PointModels, BiologicalVirologyVirusescapsidDNA bindingDNA-binding proteinselectrostaticelectrostatic modelmodelingpIpoint of zero chargepolynucleotidepredictionRNA bindingRNA-binding proteins

Identifiers

PMID32978129
PMCPMC7657617
OpenAlexW3089145911

What OpenQuestion holds

Textmetadata
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.