A key component to success in structure-based drug design is reliable information on protein-ligand interactions. Recent development in NMR techniques has accelerated this process by overcoming some of the limitations of X-ray crystallography and computational protein-ligand docking. In this work we present a new scoring protocol based on NMR-derived interligand INPHARMA NOEs to guide the selection of computationally generated docking modes. We demonstrate the performance in a range of scenarios, encompassing traditionally difficult cases such as docking to homology models and ligand dependent domain rearrangements. Ambiguities associated with sparse experimental information are lifted by searching a consensus solution based on simultaneously fitting multiple ligand pairs. This study provides a previously unexplored integration between molecular modeling and experimental data, in which interligand NOEs represent the key element in the rescoring algorithm. The presented protocol should be widely applicable for protein-ligand docking also in a different context from drug design and highlights the important role of NMR-based approaches to describe intermolecular ligand-receptor interactions.

Structure-based drug design (SBDD) relies on the availability of high-quality structures that describe protein-ligand interactions. INPHARMA is an NMR-based method that allows the determination of ligand binding poses to accuracy higher than 2 Å. In this work, we demonstrate that INPHARMA can be used to find novel ligand scaffolds as inhibitors of a model system protein, the cyclin dependent kinase (Cdk-2). The workflow is as follows: first, we determine the binding poses for six low-affinity fragments of Cdk-2 and use them to derive a structure-based pharmacophore. Two of the ligands show an unexpected binding mode, which differs from the one observed in crystal structures of other kinases. Second, we use the INPHARMA-generated pharmacophore for virtual screening of the ZINC database; one of the hit compounds is found to bind Cdk-2 in the low M range and shows selectivity for Cdk-2 against kinases of other families. Our results demonstrate that IN PHARMA is an efficient structurebased tool in solution to evolve low affinity fragments into hit compounds.

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