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Three-dimensional protein structures are a key requisite for structure-based drug discovery. For many highly relevant targets, medicinal chemists are confronted with large numbers of target structures in their apo-forms or in complex with a wealth of different ligands. To exploit the full potential of such structure ensembles, in terms of aggregated knowledge that informs design, it is desirable to extract a manageable number of structures that provide a maximum of ligand design opportunities. Most commonly used structure comparison methods are largely based on atom positions and geometry-based metrics; medicinal chemists, however, seek ligand design opportunities and are interested in methods that allow such information to be distilled from structural data and guide them in an intuitive way. Here we present an approach for identifying nonobvious ligand design opportunities in protein conformation ensembles based on the information content in grid maps that represent, for example, binding hotspots. We use four different examples to show how this method can provide information orthogonal to established coordinate-based similarity methods. Furthermore, we demonstrate that ligand design opportunities can change substantially with very small structural variations. We expect that this approach will advance the identification of ligand design opportunities hidden in large collections of protein-ligand complex data that would otherwise have been missed.

Polysaccharides (carbohydrates) are key regulators of a large number of cell biological processes. However, precise biochemical or genetic manipulation of these often complex structures is laborious and hampers experimental structure-function studies. Molecular Dynamics (MD) simulations provide a valuable alternative tool to generate and test hypotheses on saccharide function. Yet, currently used MD force fields often overestimate the aggregation propensity of polysaccharides, affecting the usability of those simulations. Here we tested MARTINI, a popular coarse-grained (CG) force field for biological macromolecules, for its ability to accurately represent molecular forces between saccharides. To this end, we calculated a thermodynamic solution property, the second virial coefficient of the osmotic pressure (B 22 ). Comparison with light scattering experiments revealed a nonphysical aggregation of a prototypical polysaccharide in MARTINI, pointing at an imbalance of the nonbonded solute-solute, solute-water, and water-water interactions. This finding also applies to smaller oligosaccharides which were all found to aggregate in simulations even at moderate concentrations, well below their solubility limit. Finally, we explored the influence of the Lennard-Jones (LJ) interaction between saccharide molecules and propose a simple scaling of the LJ interaction strength that makes MARTINI more reliable for the simulation of saccharides.