Designing tight-binding ligands is a primary objective of small-molecule drug discovery. Over the past few decades, free-energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low-cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (∼5× in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free-energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.

Designing tight binding ligands is a primary objective of small molecule drug discovery. Over the past few decades, free energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (~5X in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized based on other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.

with thirty-five deubiquitylase enzymes, we identified Usp8 as the specific DUB involved in deubiquitination of the endocytosed KCa3.1. This result was confirmed in HEK cells, by measuring membrane KCa3.1 ubiquitination and degradation rate in the presence of the wild type or catalytically inactive mutant of Usp8. Thus, overexpression of wild type Usp8 accelerates channel deubiquitination, while the mutant Usp8 strongly enhanced accumulation of ubiquitinated KCa3.1. Interestingly, in both cases the rate of channel degradation was delayed. In conclusion, we demonstrate that poly-ubiquitination mediates the targeting of membrane KCa3.1 to the lysosomes and also that Usp8 regulates the rate of KCa3.1 degradation by deubiquitinating KCa3.1 before delivery to lysosomes.

GlyH-101 is a small molecule (MW: 493) that carries a single negative charge (pKa: 5.5) under physiological conditions (∼ pH 7.4) and blocks the cystic fibrosis transmembrane conductance regulator (CFTR) chloride channel by entering from the extracellular side and binding to a site or sites within the pore (Muanprasat et al., J. Gen. Physiol. 124: 125-137). However, the precise binding sites for this molecule have yet to be identified. We used virtual ligand docking software, "Glide" (Schrodinger Inc.) to identify potential GlyH-101 binding sites within molecular models of CFTR derived by means of molecular dynamics simulation from a homology model based on Sav 1866 (Alexander et al., Biochemistry 48: 10078-10088).

The last decade has seen the discovery by means of high throughput screening of a wide range of small-molecule modulators of the CFTR chloride channel. These compounds act by altering anion conduction, channel gating and/or trafficking of the CFTR protein. However, binding sites for these molecules on CFTR or other cellular constituents have yet to be identified. GlyH-101 is a CFTR modulator that blocks the channel by entering from the extracellular side and binding to a site within the pore. In an effort to identify possible GlyH-101 binding sites within the pore of the CFTR channel, we applied the small-molecule docking program, "Glide" (Schrodinger, Inc.), to a series of molecular models of CFTR, derived by means of molecular dynamics simulation from a homology model based on the prokaryotic ABC transporter, Sav1866 (Dawson and Locher, Nature 443: 180-185, 2006; Alexander et al., Biochemistry in press, 2009). One of the potential GlyH-101 binding sites identified by Glide lies in close proximity to two residues in the sixth transmembrane segment (TM6), F337 and T338, where substituted cysteines are "protected" by GlyH-101 from reaction with thiol-directed probes (Norimatsu et al., Biophys. Journal 96: 468a-469a, 2009). These results suggest an approach to identifying the binding site(s) for GlyH-101 and other small molecules within the CFTR protein. Supported by NIH, Cystic Fibrosis Foundation, American Lung Association, the Wellcome Trust, and the BBSRC.