Evidence map›Paper›PMID 38060268›Full record

ArticleBioinformatics (Oxford, England)2023

Local Disordered Region Sampling (LDRS) for ensemble modeling of proteins with experimentally undetermined or low confidence prediction segments.

Zi Hao Liu, João M C Teixeira, Oufan Zhang, Thomas E Tsangaris, Jie Li, Claudiu C Gradinaru, Teresa Head-Gordon, Julie D Forman-Kay

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.3field-weighted citation impact, top 11% 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

13 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. Decoding Order and Disorder in Proteins by NMR Spectroscopy.Journal of the American Chemical Society · 2025
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 2 countries.

Zi Hao LiuMolecular Medicine Program, Hospital for Sick Children, Toronto, ON M5G 0A4, Canada.ORCID 0000-0002-8357-8507
João M C TeixeiraMolecular Medicine Program, Hospital for Sick Children, Toronto, ON M5G 0A4, Canada.
Oufan ZhangPitzer Center for Theoretical Chemistry, University of California, Berkeley, Berkeley, CA 94720, United States.
Thomas E TsangarisDepartment of Physics, University of Toronto, Toronto, ON M5S 1A7, Canada.ORCID 0000-0003-3683-1183
Jie LiPitzer Center for Theoretical Chemistry, University of California, Berkeley, Berkeley, CA 94720, United States.
Claudiu C GradinaruDepartment of Physics, University of Toronto, Toronto, ON M5S 1A7, Canada.
Teresa Head-GordonPitzer Center for Theoretical Chemistry, University of California, Berkeley, Berkeley, CA 94720, United States.ORCID 0000-0003-0025-8987
Julie D Forman-KayMolecular Medicine Program, Hospital for Sick Children, Toronto, ON M5G 0A4, Canada.
University of Toronto · CAUniversity of California, Berkeley · USHospital for Sick Children · CA

Funding

Calculating Ensembles of Discrete Dynamic Complexes and Condensed States of Intrinsically Disordered ProteinsR01GM127627 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI Teresa L. Head-Gordon · 2018 to 2026
$2.1M
NIGMS NIH HHS R01 GM127627NIH HHS 5R01GM127627-04
6 · The paper itself

Abstract

summaryThe Local Disordered Region Sampling (LDRS, pronounced loaders) tool is a new module developed for IDPConformerGenerator, a previously validated approach to model intrinsically disordered proteins (IDPs). The IDPConformerGenerator LDRS module provides a method for generating all-atom conformations of intrinsically disordered protein regions at N- and C-termini of and in loops or linkers between folded regions of an existing protein structure. These disordered elements often lead to missing coordinates in experimental structures or low confidence in predicted structures. Requiring only a pre-existing PDB or mmCIF formatted structural template of the protein with missing coordinates or with predicted confidence scores and its full-length primary sequence, LDRS will automatically generate physically meaningful conformational ensembles of the missing flexible regions to complete the full-length protein. The capabilities of the LDRS tool of IDPConformerGenerator include modeling phosphorylation sites using enhanced Monte Carlo-Side Chain Entropy, transmembrane proteins within an all-atom bilayer, and multi-chain complexes. The modeling capacity of LDRS capitalizes on the modularity, the ability to be used as a library and via command-line, and the computational speed of the IDPConformerGenerator platform. AVAILABILITY AND IMPLEMENTATION: The LDRS module is part of the IDPConformerGenerator modeling suite, which can be downloaded from GitHub at https://github.com/julie-forman-kay-lab/IDPConformerGenerator. IDPConformerGenerator is written in Python3 and works on Linux, Microsoft Windows, and Mac OS versions that support DSSP. Users can utilize LDRS's Python API for scripting the same way they can use any part of IDPConformerGenerator's API, by importing functions from the "idpconfgen.ldrs_helper" library. Otherwise, LDRS can be used as a command line interface application within IDPConformerGenerator. Full documentation is available within the command-line interface as well as on IDPConformerGenerator's official documentation pages (https://idpconformergenerator.readthedocs.io/en/latest/).

Indexed as

Intrinsically Disordered ProteinsSoftwareDocumentationGene LibraryMembrane ProteinsIntrinsically Disordered ProteinsMembrane Proteins

Identifiers

PMID38060268
PMCPMC10733734
OpenAlexW4389452578

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.