Abstract
The calculation of hydrogen positions is a common preprocessing step when working with crystal structures of protein-ligand complexes. An explicit description of hydrogen atoms is generally needed in order to analyze the binding mode of particular ligands or to calculate the associated binding energies. Due to the large number of degrees of freedom resulting from different chemical moieties and the high degree of mutual dependence this problem is anything but trivial. In addition to an efficient algorithm to take care of the complexity resulting from complicated hydrogen bonding networks, a robust chemical model is needed to describe effects such as tautomerism and ionization consistently. We present a novel method for the placement of hydrogen coordinates in protein-ligand complexes which takes tautomers and protonation states of both protein and ligand into account. Our method generates the most probable hydrogen positions on the basis of an optimal hydrogen bonding network using an empirical scoring function. The high quality of our results could be verified by comparison to the manually adjusted Astex diverse set and a remarkably low rate of undesirable hydrogen contacts compared to other tools.
Highlights
Crystal structures of protein-ligand complexes play an important role in the drug development process
We present a novel method for the placement of hydrogen coordinates in protein-ligand complexes
In order to demonstrate the insufficiencies of this approach, we counted all substructures contained in the Ligand Expo database [23], for which we were able to identify sensible alternative tautomers or protonation states
Summary
Crystal structures of protein-ligand complexes play an important role in the drug development process. They provide valuable insights into where and how molecules interact with their respective target proteins and are the basis for further optimization strategies. The statistical analysis of large collections of crystal structures is a common means to gain general knowledge about molecular interactions and geometry. These results are often used to derive parameters for various computational methods.
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