Evidence map›Paper›PMID 16584558›Full record

ArticleMicrobial cell factories2006

Insertional protein engineering for analytical molecular sensing.

Rosa María Ferraz, Andrea Vera, Anna Arís, Antonio Villaverde

Open access · goldAbstract read
In one paragraph

Article in Microbial cell factories, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
3.3field-weighted citation impact, top 9% 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

7 citing papers in PubMed, 29 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

4 authors at 2 institutions in 1 country.

Rosa María FerrazInstitut de Biotecnologia i de Biomedicina and Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona, Bellaterra, 08193 Barcelona, Spain. RosaMaria.Ferraz@uab.es
Andrea Vera
Anna Arís
Antonio Villaverde
Universitat Autònoma de Barcelona · ESUniversitat Politècnica de Catalunya · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The quantitative detection of low analyte concentrations in complex samples is becoming an urgent need in biomedical, food and environmental fields. Biosensors, being hybrid devices composed by a biological receptor and a signal transducer, represent valuable alternatives to non biological analytical instruments because of the high specificity of the biomolecular recognition. The vast range of existing protein ligands enable those macromolecules to be used as efficient receptors to cover a diversity of applications. In addition, appropriate protein engineering approaches enable further improvement of the receptor functioning such as enhancing affinity or specificity in the ligand binding. Recently, several protein-only sensors are being developed, in which either both the receptor and signal transducer are parts of the same protein, or that use the whole cell where the protein is produced as transducer. In both cases, as no further chemical coupling is required, the production process is very convenient. However, protein platforms, being rather rigid, restrict the proper signal transduction that necessarily occurs through ligand-induced conformational changes. In this context, insertional protein engineering offers the possibility to develop new devices, efficiently responding to ligand interaction by dramatic conformational changes, in which the specificity and magnitude of the sensing response can be adjusted up to a convenient level for specific analyte species. In this report we will discuss the major engineering approaches taken for the designing of such instruments as well as the relevant examples of resulting protein-only biosensors.

Identifiers

PMID16584558
PMCPMC1459189
OpenAlexW1716323963

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.