Evidence map›Paper›PMID 25608737›Full record

ArticleScientific reports2015

Cloud computing approaches for prediction of ligand binding poses and pathways.

Morgan Lawrenz, Diwakar Shukla, Vijay S Pande

Abstract read
In one paragraph

Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

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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

20 citing papers in PubMed.

  1. In Silico Isomerization Produces Apt Negative Data for VHTS Validation.Journal of chemical information and modeling · 2026
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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

3 authors.

Morgan LawrenzDepartment of Chemistry, Stanford University, Stanford, CA 94305.
Diwakar Shukla1] Department of Chemistry, Stanford University, Stanford, CA 94305 [2] SIMBIOS NIH Center for Biomedical Computation, Stanford University, Stanford, CA 94305.
Vijay S Pande1] Department of Chemistry, Stanford University, Stanford, CA 94305 [2] SIMBIOS NIH Center for Biomedical Computation, Stanford University, Stanford, CA 94305.

Funding

Physics-based Simulation of Biological Structures(RMI)U54GM072970 · NIGMS · STANFORD UNIVERSITY · PI ALTMAN, RUSS BIAGIO · 2004 to 2013
$33.5M
Retropositional or Repressor Machinery of SINEs &LINEsR15GM062828 · NIGMS · EASTERN MICHIGAN UNIVERSITY · PI KASS, DAVID H · 2002 to 2002
$132k
NIGMS NIH HHS R01GM62828NIGMS NIH HHS R15 GM062828NIGMS NIH HHS U54 GM072970
6 · The paper itself

Abstract

We describe an innovative protocol for ab initio prediction of ligand crystallographic binding poses and highly effective analysis of large datasets generated for protein-ligand dynamics. We include a procedure for setup and performance of distributed molecular dynamics simulations on cloud computing architectures, a model for efficient analysis of simulation data, and a metric for evaluation of model convergence. We give accurate binding pose predictions for five ligands ranging in affinity from 7 nM to > 200 μM for the immunophilin protein FKBP12, for expedited results in cases where experimental structures are difficult to produce. Our approach goes beyond single, low energy ligand poses to give quantitative kinetic information that can inform protein engineering and ligand design.

Indexed as

Cloud ComputingLigandsProtein EngineeringBinding SitesCrystallography, X-RayMolecular Dynamics SimulationProtein BindingTacrolimus Binding Protein 1AThermodynamicsLigandsTacrolimus Binding Protein 1A

Identifiers

PMID25608737
PMCPMC4302315

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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.