Evidence map›Paper›PMID 28975675›Full record

ArticleProteins2018

Biological and functional relevance of CASP predictions.

Tianyun Liu, Shirbi Ish-Shalom, Wen Torng, Aleix Lafita, Christian Bock, Matthew Mort, David N Cooper, Spencer Bliven, Guido Capitani, Sean D Mooney and 1 more

Abstract read
In one paragraph

Article in Proteins, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Conservation of binding properties in protein models.Computational and structural biotechnology journal · 2021
    Article
  5. Article
  6. Article
  7. Article
  8. 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

11 authors.

Tianyun LiuDepartment of Bioengineering, Stanford University, Stanford, California.ORCID 0000-0003-4912-383X
Shirbi Ish-ShalomBiomedical Informatics Training Program, Stanford University, Stanford, California.
Wen TorngDepartment of Bioengineering, Stanford University, Stanford, California.
Aleix LafitaLaboratory of Biomolecular Research, Paul Scherrer Institute, Villigen, Switzerland.
Christian BockDepartment of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington.
Matthew MortInstitute of Medical Genetics, Cardiff University, United Kingdom.
David N CooperInstitute of Medical Genetics, Cardiff University, United Kingdom.
Spencer BlivenLaboratory of Biomolecular Research, Paul Scherrer Institute, Villigen, Switzerland.
Guido CapitaniLaboratory of Biomolecular Research, Paul Scherrer Institute, Villigen, Switzerland.
Sean D MooneyDepartment of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington.
Russ B AltmanDepartment of Bioengineering, Stanford University, Stanford, California.

Funding

Physics-based Simulation of Biological Structures(RMI)U54GM072970 · NIGMS · STANFORD UNIVERSITY · PI PANDE, VIJAY S · 2004 to 2013
$33.5M
Pilot Study: POPULATION PHARMACOLOGYU54HL117798 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI FITZGERALD, GARRET A · 2012 to 2016
$18.5M
Text Mining for High-fidelity Curation and Discovery of Gene-drug-phenotype RelationshipsR01LM005652 · NLM · STANFORD UNIVERSITY · PI ALTMAN, RUSS BIAGIO · 2000 to 2018
$6.2M
Undergraduate Summer Research Experiences Support for Combining systems biology and structural biology to find new therapeuticsR01GM102365 · NIGMS · STANFORD UNIVERSITY · PI ALTMAN, RUSS BIAGIO · 2012 to 2021
$3.0M
NHLBI NIH HHS U54 HL117798NIGMS NIH HHS R01 GM102365NIGMS NIH HHS U54 GM072970NLM NIH HHS R01 LM005652
6 · The paper itself

Abstract

Our goal is to answer the question: compared with experimental structures, how useful are predicted models for functional annotation? We assessed the functional utility of predicted models by comparing the performances of a suite of methods for functional characterization on the predictions and the experimental structures. We identified 28 sites in 25 protein targets to perform functional assessment. These 28 sites included nine sites with known ligand binding (holo-sites), nine sites that are expected or suggested by experimental authors for small molecule binding (apo-sites), and Ten sites containing important motifs, loops, or key residues with important disease-associated mutations. We evaluated the utility of the predictions by comparing their microenvironments to the experimental structures. Overall structural quality correlates with functional utility. However, the best-ranked predictions (global) may not have the best functional quality (local). Our assessment provides an ability to discriminate between predictions with high structural quality. When assessing ligand-binding sites, most prediction methods have higher performance on apo-sites than holo-sites. Some servers show consistently high performance for certain types of functional sites. Finally, many functional sites are associated with protein-protein interaction. We also analyzed biologically relevant features from the protein assemblies of two targets where the active site spanned the protein-protein interface. For the assembly targets, we find that the features in the models are mainly determined by the choice of template.

Indexed as

Models, MolecularModels, StatisticalProtein ConformationBinding SitesBiological ProductsCatalytic DomainComputational BiologyHumansLigandsProtein BindingProteinsBiological ProductsLigandsProteinsbinding siteCASPfunctional relevancemicroenvironmentprotein structure prediction

Identifiers

PMID28975675
PMCPMC5820171

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.