Cloud computing approaches for prediction of ligand binding poses and pathways

Morgan Lawrenz, Diwakar Shukla, Vijay S. Pande

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number7918
JournalScientific reports
Volume5
DOIs
StatePublished - Jan 22 2015
Externally publishedYes

ASJC Scopus subject areas

  • General

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