Evidence map›Paper›PMID 18781587›Full record

ArticleProteins2009

In pursuit of virtual lead optimization: pruning ensembles of receptor structures for increased efficiency and accuracy during docking.

Erin S D Bolstad, Amy C Anderson

Abstract read
In one paragraph

Article in Proteins, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Knowledge-Based Methods To Train and Optimize Virtual Screening Ensembles.Journal of chemical information and modeling · 2016
    Article
  4. Induced fit docking, and the use of QM/MM methods in docking.Drug discovery today. Technologies · 2013
    Review
  5. Protein flexibility in docking and surface mapping.Quarterly reviews of biophysics · 2012
    Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. 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.

Erin S D BolstadDepartment of Pharmaceutical Sciences, University of Connecticut, Storrs, Connecticut 06269, USA.
Amy C Anderson

Funding

Targeting DHFR to Design Antimicrobial AgentsR01GM067542 · NIGMS · UNIVERSITY OF CONNECTICUT STORRS · PI ANDERSON, AMY C. · 2003 to 2012
$2.2M
NIGMS NIH HHS GM067542NIGMS NIH HHS R01 GM067542
6 · The paper itself

Abstract

Representing receptors as ensembles of protein conformations during docking is a powerful method to approximate protein flexibility and increase the accuracy of the resulting ranked list of compounds. Unfortunately, docking compounds against a large number of ensemble members can increase computational cost and time investment. In this article, we present an efficient method to evaluate and select the most contributive ensemble members prior to docking for targets with a conserved core of residues that bind a ligand moiety. We observed that ensemble members that preserve the geometry of the active site core are most likely to place ligands in the active site with a conserved orientation, generally rank ligands correctly and increase interactions with the receptor. A relative distance approach is used to quantify the preservation of the three-dimensional interatomic distances of the conserved ligand-binding atoms and prune large ensembles quickly. In this study, we investigate dihydrofolate reductase as an example of a protein with a conserved core; however, this method for accurately selecting relevant ensemble members a priori can be applied to any system with a conserved ligand-binding core, including HIV-1 protease, kinases, and acetylcholinesterase. Representing a drug target as a pruned ensemble during in silico screening should increase the accuracy and efficiency of high-throughput analyses of lead analogs.

Indexed as

LigandsAmino Acid SequenceCluster AnalysisComputational BiologyComputer SimulationConserved SequenceDrug DiscoveryModels, MolecularProtein BindingProtein ConformationProteinsSequence Homology, Amino AcidTetrahydrofolate DehydrogenaseLigandsProteinsTetrahydrofolate Dehydrogenase

Identifiers

PMID18781587
PMCPMC2649978

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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.