Evidence map›Paper›PMID 23049817›Full record

ArticlePloS one2012

In silico classification of proteins from acidic and neutral cytoplasms.

Yaping Fang, C Russell Middaugh, Jianwen Fang

Abstract readComparative Study
In one paragraph

Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Yaping FangApplied Bioinformatics Laboratory, The University of Kansas, Lawrence, Kansas, USA.
C Russell Middaugh
Jianwen Fang

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein acidostability is a common problem in biopharmaceutical and other industries. However, it remains a great challenge to engineer proteins for enhanced acidostability because our knowledge of protein acidostabilization is still very limited. In this paper, we present a comparative study of proteins from bacteria with acidic (AP) and neutral cytoplasms (NP) using an integrated statistical and machine learning approach. We construct a set of 393 non-redundant AP-NP ortholog pairs and calculate a total of 889 sequence based features for these proteins. The pairwise alignments of these ortholog pairs are used to build a residue substitution propensity matrix between APs and NPs. We use Gini importance provided by the Random Forest algorithm to rank the relative importance of these features. A scoring function using the 10 most significant features is developed and optimized using a hill climbing algorithm. The accuracy of the score function is 86.01% in predicting AP-NP ortholog pairs and is 76.65% in predicting non-ortholog AP-NP pairs, suggesting that there are significant differences between APs and NPs which can be used to predict relative acidostability of proteins. The overall trends uncovered in the study can be used as general guidelines for designing acidostable proteins. To best of our knowledge, this work represents the first systematic comparative study of the acidostable proteins and their non-acidostable orthologs.

Indexed as

AlgorithmsAcetobacterAcidithiobacillusAmino AcidsArtificial IntelligenceBacterial ProteinsComputational BiologyComputer SimulationCytoplasmHydrogen-Ion ConcentrationModels, MolecularProtein StabilitySulfolobusThermoplasmaAmino AcidsBacterial Proteins

Identifiers

PMID23049817
PMCPMC3458925

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.