Evidence map›Paper›PMID 20853887›Full record

ArticleJournal of chemical information and modeling2010

Comprehensive structural and functional characterization of the human kinome by protein structure modeling and ligand virtual screening.

Michal Brylinski, Jeffrey Skolnick

Abstract read
In one paragraph

Article in Journal of chemical information and modeling, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
7.2field-weighted citation impact, top 3% 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

15 citing papers in PubMed, 39 citations in OpenAlex.

  1. FRAGSITE2: A structure and fragment-based approach for virtual ligand screening.Protein science : a publication of the Protein Society · 2024
    Article
  2. Article
  3. Article
  4. Review
  5. FINDSITEJournal of chemical information and modeling · 2018
    Article
  6. Article
  7. Article
  8. Review
  9. Review
  10. Kinome-wide activity modeling from diverse public high-quality data sets.Journal of chemical information and modeling · 2013
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. 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

2 authors at 1 institution in 1 country.

Michal BrylinskiCenter for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA.
Jeffrey Skolnick
Georgia Institute of Technology · US

Funding

Interaction Pattern Based Predictor of Protein StruturesR01GM048835 · NIGMS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI SKOLNICK, JEFFREY · 1994 to 2015
$4.0M
THEORY OF FOLDING TRANSITIONS IN A-HELICAL PROTEINSR01GM037408 · NIGMS · WASHINGTON UNIVERSITY · PI SKOLNICK, JEFFREY · 1987 to 2014
$3.9M
NIGMS NIH HHS GM-37408NIGMS NIH HHS GM-48835NIGMS NIH HHS R01 GM037408NIGMS NIH HHS R01 GM048835
6 · The paper itself

Abstract

The growing interest in the identification of kinase inhibitors, promising therapeutics in the treatment of many diseases, has created a demand for the structural characterization of the entire human kinome. At the outset of the drug development process, the lead-finding stage, approaches that enrich the screening library with bioactive compounds are needed. Here, protein structure based methods can play an important role, but despite structural genomics efforts, it is unlikely that the three-dimensional structures of the entire kinome will be available soon. Therefore, at the proteome level, structure-based approaches must rely on predicted models, with a key issue being their utility in virtual ligand screening. In this study, we employ the recently developed FINDSITE/Q-Dock ligand homology modeling approach, which is well-suited for proteome-scale applications using predicted structures, to provide extensive structural and functional characterization of the human kinome. Specifically, we construct structure models for the human kinome; these are subsequently subject to virtual screening against a library of more than 2 million compounds. To rank the compounds, we employ a hierarchical approach that combines ligand- and structure-based filters. Modeling accuracy is carefully validated using available experimental data with particularly encouraging results found for the ability to identify, without prior knowledge, specific kinase inhibitors. More generally, the modeling procedure results in a large number of predicted molecular interactions between kinases and small ligands that should be of practical use in the development of novel inhibitors. The data set is freely available to the academic community via a user-friendly Web interface at http://cssb.biology.gatech.edu/kinomelhm/ as well as at the ZINC Web site ( http://zinc.docking.org/applications/2010Apr/Brylinski-2010.tar.gz ).

Indexed as

Drug DesignBinding SitesHumansLigandsModels, MolecularProtein ConformationProtein Kinase InhibitorsProtein KinasesProteomicsLigandsProtein Kinase InhibitorsProtein Kinases

Identifiers

PMID20853887
PMCPMC2963673
OpenAlexW2082425789

What OpenQuestion holds

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LicenceTDM
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.