Journal
SCIENTIFIC REPORTS
Volume 5, Issue -, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/srep07918
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Funding
- Ruth Kirschstein fellowship for postdoctoral researchers
- Biomedical Data Science Initiative postdoc scholar program of Stanford School of Medicine
- Simbios, NIH Center for Biomedical Computation (NIH U54 Roadmap) [GM072970]
- NIH [R01GM62828]
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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 mu M 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.
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