Evidence map›Paper›PMID 30541545›Full record

ArticleBMC structural biology2018

An improved protein structure evaluation using a semi-empirically derived structure property.

Manoj Kumar Pal, Tapobrata Lahiri, Garima Tanwar, Rajnish Kumar

Open access · hybridAbstract read
In one paragraph

Article in BMC structural biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact, top 88% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. Illuminating the "Twilight Zone": Advances in Difficult Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023
    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

4 authors at 1 institution in 1 country.

Manoj Kumar PalDepartment of Applied Science, Indian Institute of Information Technology, Biomedical Informatics Lab, Room no 4302, CC2 Building, Allahabad, UP, 211012, India.
Tapobrata LahiriDepartment of Applied Science, Indian Institute of Information Technology, Biomedical Informatics Lab, Room no 4302, CC2 Building, Allahabad, UP, 211012, India. tlahiri@iiita.ac.in.ORCID 0000-0002-0290-4201
Garima TanwarDepartment of Applied Science, Indian Institute of Information Technology, Biomedical Informatics Lab, Room no 4302, CC2 Building, Allahabad, UP, 211012, India.
Rajnish KumarDepartment of Applied Science, Indian Institute of Information Technology, Biomedical Informatics Lab, Room no 4302, CC2 Building, Allahabad, UP, 211012, India.
Indian Institute of Information Technology Allahabad · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn the backdrop of challenge to obtain a protein structure under the known limitations of both experimental and theoretical techniques, the need of a fast as well as accurate protein structure evaluation method still exists to substantially reduce a huge gap between number of known sequences and structures. Among currently practiced theoretical techniques, homology modelling backed by molecular dynamics based optimization appears to be the most popular one. However it suffers from contradictory indications of different validation parameters generated from a set of protein models which are predicted against a particular target protein. For example, in one model Ramachandran Score may be quite high making it acceptable, whereas, its potential energy may not be very low making it unacceptable and vice versa. Towards resolving this problem, the main objective of this study was fixed as to utilize a simple experimentally derived output, Surface Roughness Index of concerned protein of unknown structure as an intervening agent that could be obtained using ordinary microscopic images of heat denatured aggregates of the same protein.

resultIt was intriguing to observe that direct experimental knowledge of the concerned protein, however simple it may be, might give insight on acceptability of its particular structural model out of a confusion set of models generated from database driven comparative technique for structure prediction. The result obtained from a widely varying structural class of proteins indicated that speed of protein structure evaluation can be further enhanced without compromising with accuracy by recruiting simple experimental output.

conclusionIn this work, a semi-empirical methodological approach was provided for improving protein structure evaluation. It showed that, once structure models of a protein were obtained through homology technique, the problem of selection of a best model out of a confusion set of Pareto-optimal structures could be resolved by employing a structure agent directly obtainable through experiment with the same protein as experimental ingredient. Overall, in the backdrop of getting a reasonably accurate protein structure of pathogens causing epidemics or biological warfare, such approach could be of use as a plausible solution for fast drug design.

Indexed as

Models, MolecularCytochromes cHemoglobinsProtein ConformationProteinsSerum AlbuminCytochromes cHemoglobinsProteinsSerum AlbuminConfusion set of modelsProtein structure validationSelection of best structure modelSemi-empirical methodSurface roughness index

Identifiers

PMID30541545
PMCPMC6291994
OpenAlexW2904609071

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LicenceCC BY
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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.