Evidence map›Paper›PMID 27037818›Full record

ArticleMedical & biological engineering & computing2016

Prediction of intrinsically disordered regions in proteins using signal processing methods: application to heat-shock proteins.

Vuk Vojisavljevic, Elena Pirogova

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Article in Medical & biological engineering & computing, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.3field-weighted citation impact, top 32% 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

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

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.

Vuk VojisavljevicBiomedical Engineering, School of Engineering, RMIT University, Melbourne, VIC, 3001, Australia.
Elena PirogovaBiomedical Engineering, School of Engineering, RMIT University, Melbourne, VIC, 3001, Australia. elena.pirogova@rmit.edu.au.
RMIT University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heat-shock protein (HSP)-based immunotherapy is believed to be a promising area of development for cancer treatment as such therapy is characterized by a unique approach to every tumour. It was shown that by inhibition of HSPs it is possible to induce apoptotic cell death in cancer cells. Interestingly, there are a great number of disordered regions in proteins associated with cancer, cardiovascular and neurodegenerative diseases, signalling, and diabetes. HSPs and some specific enzymes were shown to have these disordered regions in their primary structures. The experimental studies of HSPs confirmed that their intrinsically disordered (ID) regions are of functional importance. These ID regions play crucial roles in regulating the specificity of interactions between dimer complexes and their interacting partners. Because HSPs are overexpressed in cancer, predicting the locations of ID regions and binding sites in these proteins will be important for developing novel cancer therapeutics. In our previous studies, signal processing methods have been successfully used for protein structure-function analysis (i.e. for determining functionally important amino acids and the locations of protein active sites). In this paper, we present and discuss a novel approach for predicting the locations of ID regions in the selected cancer-related HSPs.

Indexed as

Signal Processing, Computer-AssistedAlgorithmsAmino Acid SequenceAnimalsBinding SitesHeat-Shock ProteinsHumansIntrinsically Disordered ProteinsMiceHeat-Shock ProteinsIntrinsically Disordered ProteinsActive/binding siteCancerHSPsIntrinsic disorderSignal processing

Identifiers

PMID27037818
OpenAlexW2333870265

What OpenQuestion holds

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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.