Evidence map›Paper›PMID 20205445›Full record

ArticleJournal of chemical information and modeling2010

Protein pockets: inventory, shape, and comparison.

Ryan G Coleman, Kim A Sharp

Abstract readComparative Study
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 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing 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

38 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Protein Structure Inspired Drug Discovery.bioRxiv : the preprint server for biology · 2024
    Article
  10. SiteFerret: Beyond Simple Pocket Identification in Proteins.Journal of chemical theory and computation · 2023
    Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. An enumerative algorithm for de novo design of proteins with diverse pocket structures.Proceedings of the National Academy of Sciences of the United States of America · 2020
    Article
  19. Review
  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

2 authors.

Ryan G ColemanDepartment of Biochemistry and Biophysics, The Johnson Research Foundation, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
Kim A Sharp

Funding

Training Program in Computational GenomicsT32HG000046 · NHGRI · UNIVERSITY OF PENNSYLVANIA · PI JUNHYONG KIM, Mingyao Li · 1999 to 2026
$9.5M
Vibrational and Optical Probes of Electric Fields in ProteinsP01GM048130 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI HOCHSTRASSER, ROBIN MAIN · 1993 to 2007
$7.3M
STRUCTURAL BIOLOGY TRAINING PROGRAMT32GM008275 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI WAND, A. JOSHUA · 1988 to 2018
$6.0M
NHGRI NIH HHS T32 HG000046NHGRI NIH HHS T32HG000046NIGMS NIH HHS GM008275NIGMS NIH HHS GM48130NIGMS NIH HHS P01 GM048130NIGMS NIH HHS T32 GM008275
6 · The paper itself

Abstract

The shape of the protein surface dictates what interactions are possible with other macromolecules, but defining discrete pockets or possible interaction sites remains difficult. First, there is the problem of defining the extent of the pocket. Second, one has to characterize the shape of each pocket. Third, one needs to make quantitative comparisons between pockets on different proteins. An elegant solution to these problems is to sort all surface and solvent points by travel depth and then collect a hierarchical tree of pockets. The connectivity of the tree is determined via the deepest saddle points between each pair of neighboring pockets. The resulting pocket surfaces tessellate the entire protein surface, producing a complete inventory of pockets. This method of identifying pockets also allows one to easily compute important shape metrics, including the problematic pocket volume, surface area, and mouth size. Pockets are also annotated with their lining residue lists and polarity and with other residue-based properties. Using this tree and the various shape metrics pockets can be merged, grouped, or filtered for further analysis. Since this method includes the entire surface, it guarantees that any pocket of interest will be found among the output pockets, unlike all previous methods of pocket identification. The resulting hierarchy of pockets is easy to visualize and aids users in higher level analysis. Comparison of pockets is done by using the shape metrics, avoiding the complex shape alignment problem. Example applications show that the method facilitates pocket comparison along mutational or time-dependent series. Pockets from families of proteins can be examined using multiple pocket tree alignments to see how ligand binding sites or how other pockets have changed with evolution. Our method is called CLIPPERS for complete liberal inventory of protein pockets elucidating and reporting on shape.

Indexed as

Computational BiologyAdenylate KinaseBinding SitesModels, MolecularMutationProtein ConformationProteinsProtein Tyrosine Phosphatase, Non-Receptor Type 1SoftwareAdenylate KinaseProteinsProtein Tyrosine Phosphatase, Non-Receptor Type 1

Identifiers

PMID20205445
PMCPMC2859996

What OpenQuestion holds

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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.