Evidence map›Paper›PMID 29500695›Full record

ArticleJournal of molecular modeling2018

Diversity and motif conservation in protein 3D structural landscape: exploration by a new multivariate simulation method.

Rajani R Joshi

Abstract read
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In one paragraph

Article in Journal of molecular modeling, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

1 author.

Rajani R JoshiDepartment of Mathematics, Indian Institute of Technology Bombay, Mumbai, India. rrj@iitb.ac.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, diversity and conservation in the 'landscape' of random variation of protein tertiary structures are explored for quantitative feature-vector models of major types of functionally important 3D structural motifs. For this, I have deployed a recently developed nonparametric regression (NPR)-based multidimensional copula method of simulation. Apart from improved accuracy of multidimensional random sample generation, the simulation provides additional insight into diversity in the protein structural landscape in terms of random variation in the feature-vector. It shows the relative importance of several features, with biological implications, in conservation of motifs. Mapping of this landscape in distance-preserving 2D eigenspace also shows consistency in demarcation of different motif classes and preservation of their characteristic patterns in this 2D space.

Indexed as

Amino Acid MotifsModels, MolecularProtein Structure, TertiaryAlgorithmsAmino Acid SequenceConserved SequenceEvolution, MolecularGenetic VariationProteinsProteinsCopulasMulti-dimensional feature vectorMultidimensional scalingNonparametric regressionProtein tertiary structural motifsRandom number generation

Identifiers

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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.