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Conformational Ensembles from Experimental Data
and Computer Simulations
Monday Speaker Abstracts
25
Objectively and Automatically Building Multi-conformer Ligand Models in Electron
Densities
Gydo Van Zundert
1
, Daniel Keedy
2
, Pooja Suresh
2
, Amelie Heliou
3
, Kenneth Borrelli
1
, Tyler
Day
1
, James Fraser
2
, Henry Van den Bedem
1
.
1
Schrodinger, New York, NY, USA,
2
UCSF, San Francisco, CA, USA,
3
Inria, Palaiseau, France,
4
SLAC National Accelerator Laboratory, Menlo Park, CA, USA.
Structure-based drug design is often challenged by an inadequate understanding of the
conformational dynamics of ligands and their receptors. X-ray-crystallography is generally the
method of choice for resolving the structure of macromolecular molecules and investigating the
binding pose of ligands. While the electron density represents a time-averaged representation of
the underlying conformational ensemble, in the majority of cases the data are interpreted to
represent a single conformation at unit occupancy. Temperature factors inadequately account for
atom position ambiguity and thermal motion from their averaged positions. Multiple, alternative
ligand conformations are under-represented, even in high resolution datasets. Moreover, the
impact of alternative conformations for ligands remains underexplored. The presence of different
binding poses for ligands would have important consequences for rational drug design and a
fundamental understanding of the underlying binding mechanisms.
Here, we show that evidence for alternative ligand poses is common in the PDB, including for
pharmaceutically highly relevant targets. In addition, we introduce a fast, automated method for
building multi-conformer ligand models in electron densities by hierarchically sampling and
building the ligand’s degrees of freedom. We rely on powerful, state-of-the-art solvers to identify
a minimal set of conformations to collectively explain the density and for determining the
individual occupancies. This new tool provides an objective view on the ligand’s structural
heterogeneity, while paving the way for a deep investigation of its impact on rational drug
design.