Xavier Fradera

Similarity-driven flexible ligand docking.

A similarity‐driven approach to flexible ligand docking is presented. Given a reference ligand or a pharmacophore positioned in the protein active site, the method allows inclusion of a similarity term during docking. Two different algorithms have been implemented, namely, a similarity‐penalized docking (SP‐DOCK) and a similarity‐guided docking (SG‐DOCK). The basic idea is to maximally exploit the structural information about the ligand binding mode present in cases where ligand‐bound protein structures are available, information that is usually ignored in standard docking procedures. SP‐DOCK and SG‐DOCK have been derived as modified versions of the program DOCK 4.0, where the similarity program MIMIC acts as a module for the calculation of similarity indices that correct docking energy scores at certain steps of the calculation. SP‐DOCK applies similarity corrections to the set of ligand orientations at the end of the ligand incremental construction process, penalizing the docking energy and, thus, having only an effect on the relative ordering of the final solutions. SG‐DOCK applies similarity corrections throughout the entire ligand incremental construction process, thus affecting not only the relative ordering of solutions but also actively guiding the ligand docking. The performance of SP‐DOCK and SG‐DOCK for binding mode assessment and molecular database screening is discussed. When applied to a set of 32 thrombin ligands for which crystal structures are available, SG‐DOCK improves the average RMSD by ca. 1 Å when compared with DOCK. When those 32 thrombin ligands are included into a set of 1,000 diverse molecules from the ACD, DIV, and WDI databases, SP‐DOCK significantly improves the retrieval of thrombin ligands within the first 10% of each of the three databases with respect to DOCK, with minimal additional computational cost. In all cases, comparison of SP‐DOCK and SG‐DOCK results with those obtained by DOCK and MIMIC is performed. Proteins 2000;40:623–636.

Ligand-induced changes in the binding sites of proteins

Classical molecular interaction potentials, in conjunction with other theoretical techniques, are used to analyze the dependence of the binding sites of representative proteins on the bound ligand. It is found that the ligand bound introduces in general small structural perturbations at the binding site of the protein. However, such small structural changes can lead to important alterations in the recognition pattern of the protein. The impact of these findings in docking procedures is discussed.

Incorporating protein flexibility into docking and structure-based drug design

The use of structure in drug design has become widespread, mainly thanks to recent advances in crystallography. Nevertheless, biological macromolecules are intrinsically flexible and it is increasingly evident that their function depends critically on both their structure and dynamics. In this review the authors discuss the implications of protein flexibility for drug design and review recent progress in incorporating protein flexibility into docking and structure-based drug design.

Unsupervised guided docking of covalently bound ligands

SummaryAn approach for docking covalently bound ligands in protein enzymes or receptors was implemented in MacDOCK, a similarity-driven docking program based on DOCK 4.0. This approach was tested with a small number of covalent ligand–protein structures, using both native and non-native protein structures. In all cases, MacDOCK was able to generate orientations consistent with the known covalent binding mode of these complexes, with a performance similar to that of other docking programs. This method was also applied to search for known covalent thrombin inhibitors in a medium-sized molecular database (ca. 11,000 compounds). Detection of functional groups suitable for covalent docking was carried out automatically. A significant enrichment in known active molecules in the first 5% of the database was obtained, showing that MacDOCK can be used efficiently for the virtual screening of covalently bound ligands.

Identification and in Vivo Evaluation of Liver X Receptor β-Selective Agonists for the Potential Treatment of Alzheimer’s Disease

Herein, we describe the development of a functionally selective liver X receptor β (LXRβ) agonist series optimized for Emax selectivity, solubility, and physical properties to allow efficacy and safety studies in vivo. Compound 9 showed central pharmacodynamic effects in rodent models, evidenced by statistically significant increases in apolipoprotein E (apoE) and ATP-binding cassette transporter levels in the brain, along with a greatly improved peripheral lipid safety profile when compared to those of full dual agonists. These findings were replicated by subchronic dosing studies in non-human primates, where cerebrospinal fluid levels of apoE and amyloid-β peptides were increased concomitantly with an improved peripheral lipid profile relative to that of nonselective compounds. These results suggest that optimization of LXR agonists for Emax selectivity may have the potential to circumvent the adverse lipid-related effects of hepatic LXR activity.

The Discovery of 3-((4-Chloro-3-methoxyphenyl)amino)-1-((3R,4S)-4-cyanotetrahydro-2H-pyran-3-yl)-1H-pyrazole-4-carboxamide, a Highly Ligand Efficient and Efficacious Janus Kinase 1 Selective Inhibitor with Favorable Pharmacokinetic Properties

The discovery of a potent selective low dose Janus kinase 1 (JAK1) inhibitor suitable for clinical evaluation is described. As part of an overall goal to minimize dose, we pursued a medicinal chemistry strategy focused on optimization of key parameters that influence dose size, including lowering human Clint and increasing intrinsic potency, bioavailability, and solubility. To impact these multiple parameters simultaneously, we used lipophilic ligand efficiency as a key metric to track changes in the physicochemical properties of our analogs, which led to improvements in overall compound quality. In parallel, structural information guided advancements in JAK1 selectivity by informing on new vector space, which enabled the discovery of a unique key amino acid difference between JAK1 (Glu966) and JAK2 (Asp939). This difference was exploited to consistently produce analogs with the best balance of JAK1 selectivity, efficacy, and projected human dose, ultimately culminating in the discovery of compound 28.