Evidence map›Paper›PMID 29333532›Full record

ArticleJournal of molecular medicine and clinical applications2017

Molecular Docking: From Lock and Key to Combination Lock.

Ashutosh Tripathi, Vytas A Bankaitis

Abstract read
In one paragraph

Article in Journal of molecular medicine and clinical applications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing 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

35 citing papers in PubMed.

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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.

Ashutosh TripathiDepartment of Molecular and Cellular Medicine, College of Medicine, Texas A&M Health Sciences Center, College Station, Texas, USA.
Vytas A BankaitisDepartment of Molecular and Cellular Medicine, College of Medicine, Texas A&M Health Sciences Center, College Station, Texas, USA.

Funding

Role of the Phospholipids in Golgi Secretory FunctionR01GM044530 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BANKAITIS, VYTAS A · 1991 to 2019
$5.6M
Functional Anatomy of Mammalian Phosphatidylinositol Transfer ProteinsR01GM112591 · NIGMS · TEXAS A&M UNIVERSITY HEALTH SCIENCE CTR · PI BANKAITIS, VYTAS A · 2015 to 2018
$1.7M
NIGMS NIH HHS R01 GM044530NIGMS NIH HHS R01 GM112591
6 · The paper itself

Abstract

Accurate modeling of protein ligand binding is an important step in structure-based drug design, is a useful starting point for finding new lead compounds or drug candidates. The 'Lock and Key' concept of protein-ligand binding has dominated descriptions of these interactions, and has been effectively translated to computational molecular docking approaches. In turn, molecular docking can reveal key elements in protein-ligand interactions-thereby enabling design of potent small molecule inhibitors directed against specific targets. However, accurate predictions of binding pose and energetic remain challenging problems. The last decade has witnessed more sophisticated molecular docking approaches to modeling protein-ligand binding and energetics. However, the complexities that confront accurate modeling of binding phenomena remain formidable. Subtle recognition and discrimination patterns governed by three-dimensional features and microenvironments of the active site play vital roles in consolidating the key intermolecular interactions that mediates ligand binding. Herein, we briefly review contemporary approaches and suggest that future approaches treat protein-ligand docking problems in the context of a 'combination lock' system.

Indexed as

Binding energyCavity detectionDockingFragment-based designMolecular recognitionPharmacophoreScoringStructure-based Drug designVirtual screening

Identifiers

PMID29333532
PMCPMC5764188

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.