Evidence map›Paper›PMID 27151199›Full record

ArticleNucleic acids research2016

RCD+: Fast loop modeling server.

José Ramón López-Blanco, Alejandro Jesús Canosa-Valls, Yaohang Li, Pablo Chacón

Abstract read
In one paragraph

Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing 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

31 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
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  9. Article
  10. Local Normal Mode Analysis for Fast Loop Conformational Sampling.Journal of chemical information and modeling · 2022
    Article
  11. Accurate positioning of functional residues with robotics-inspired computational protein design.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Machine Learning-Enabled Pipeline for Large-Scale Virtual Drug Screening.Journal of chemical information and modeling · 2021
    Article
  18. Current approaches to flexible loop modeling.Current research in structural biology · 2021
    Review
  19. Article
  20. 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.

José Ramón López-BlancoDepartment of Biological Chemical Physics, Rocasolano Physical Chemistry Institute C.S.I.C., Serrano 119, 28006 Madrid, Spain.
Alejandro Jesús Canosa-VallsDepartment of Biological Chemical Physics, Rocasolano Physical Chemistry Institute C.S.I.C., Serrano 119, 28006 Madrid, Spain.
Yaohang LiDepartment of Computer Science, Old Dominion University, Norfolk, VA 23529, USA.
Pablo ChacónDepartment of Biological Chemical Physics, Rocasolano Physical Chemistry Institute C.S.I.C., Serrano 119, 28006 Madrid, Spain pablo@chaconlab.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Modeling loops is a critical and challenging step in protein modeling and prediction. We have developed a quick online service (http://rcd.chaconlab.org) for ab initio loop modeling combining a coarse-grained conformational search with a full-atom refinement. Our original Random Coordinate Descent (RCD) loop closure algorithm has been greatly improved to enrich the sampling distribution towards near-native conformations. These improvements include a new workflow optimization, MPI-parallelization and fast backbone angle sampling based on neighbor-dependent Ramachandran probability distributions. The server starts by efficiently searching the vast conformational space from only the loop sequence information and the environment atomic coordinates. The generated closed loop models are subsequently ranked using a fast distance-orientation dependent energy filter. Top ranked loops are refined with the Rosetta energy function to obtain accurate all-atom predictions that can be interactively inspected in an user-friendly web interface. Using standard benchmarks, the average root mean squared deviation (RMSD) is 0.8 and 1.4 Å for 8 and 12 residues loops, respectively, in the challenging modeling scenario in where the side chains of the loop environment are fully remodeled. These results are not only very competitive compared to those obtained with public state of the art methods, but also they are obtained ∼10-fold faster.

Indexed as

AlgorithmsModels, MolecularSoftwareComputersProbabilityProteinsTime FactorsWorkflowProteins

Identifiers

PMID27151199
PMCPMC4987936

What OpenQuestion holds

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LicenceCC BY-NC
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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.