Evidence map›Paper›PMID 30513992›Full record

ArticleGenes2018

Reoptimized UNRES Potential for Protein Model Quality Assessment.

Eshel Faraggi, Pawel Krupa, Magdalena A Mozolewska, Adam Liwo, Andrzej Kloczkowski

Abstract read
In one paragraph

Article in Genes, 2018. 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. Review
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

5 authors.

Eshel FaraggiResearch and Information Systems, LLC, Indianapolis, IN 46240, USA. efaraggi@iupui.edu.
Pawel KrupaBattelle Center for Mathematical Medicine, Nationwide Children's Hospital, Columbus, OH 43215, USA. pkrupa@ifpan.edu.pl.ORCID 0000-0002-9710-7837
Magdalena A MozolewskaBattelle Center for Mathematical Medicine, Nationwide Children's Hospital, Columbus, OH 43215, USA. magdalena.mozolewska@gmail.com.
Adam LiwoFaculty of Chemistry, University of Gdańsk, Wita Stwosza 63, 80-308 Gdańsk, Poland. adam@sun1.chem.univ.gda.pl.
Andrzej KloczkowskiBattelle Center for Mathematical Medicine, Nationwide Children's Hospital, Columbus, OH 43215, USA. Andrzej.Kloczkowski@nationwidechildrens.org.

Funding

Narodowe Centrum Nauki DEC-2013/10/M/ST4/00640Narodowe Centrum Nauki DEC-2015/17/N/ST4/03935Narodowe Centrum Nauki DEC-2015/17/N/ST4/03937National Science Foundation CNS-0521433National Science Foundation CNS-0723054
6 · The paper itself

Abstract

Ranking protein structure models is an elusive problem in bioinformatics. These models are evaluated on both the degree of similarity to the native structure and the folding pathway. Here, we simulated the use of the coarse-grained UNited RESidue (UNRES) force field as a tool to choose the best protein structure models for a given protein sequence among a pool of candidate models, using server data from the CASP11 experiment. Because the original UNRES was optimized for Molecular Dynamics simulations, we reoptimized UNRES using a deep feed-forward neural network, and we show that introducing additional descriptive features can produce better results. Overall, we found that the reoptimized UNRES performs better in selecting the best structures and tracking protein unwinding from its native state. We also found a relatively poor correlation between UNRES values and the model's Template Modeling Score (TMS). This is remedied by reoptimization. We discuss some cases where our reoptimization procedure is useful. The reoptimized version of UNRES (OUNRES) is available at http://mamiris.com and http://www.unres.pl.

Indexed as

GENNmodel rankingoptimizationOUNRESprotein energySederUNRES

Identifiers

PMID30513992
PMCPMC6315818

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.