Outcome of the First wwPDB/CCDC/D3R Ligand Validation Workshop.

Crystallographic studies of ligands bound to biological macromolecules (proteins and nucleic acids) represent an important source of information concerning drug-target interactions, providing atomic level insights into the physical chemistry of complex formation between macromolecules and ligands. Of the more than 115,000 entries extant in the Protein Data Bank (PDB) archive, ∼75% include at least one non-polymeric ligand. Ligand geometrical and stereochemical quality, the suitability of ligand models for in silico drug discovery and design, and the goodness-of-fit of ligand models to electron-density maps vary widely across the archive. We describe the proceedings and conclusions from the first Worldwide PDB/Cambridge Crystallographic Data Center/Drug Design Data Resource (wwPDB/CCDC/D3R) Ligand Validation Workshop held at the Research Collaboratory for Structural Bioinformatics at Rutgers University on July 30-31, 2015. Experts in protein crystallography from academe and industry came together with non-profit and for-profit software providers for crystallography and with experts in computational chemistry and data archiving to discuss and make recommendations on best practices, as framed by a series of questions central to structural studies of macromolecule-ligand complexes. What data concerning bound ligands should be archived in the PDB? How should the ligands be best represented? How should structural models of macromolecule-ligand complexes be validated? What supplementary information should accompany publications of structural studies of biological macromolecules? Consensus recommendations on best practices developed in response to each of these questions are provided, together with some details regarding implementation. Important issues addressed but not resolved at the workshop are also enumerated.

An integrated suite of modeling tools that empower scientists in structure- and property-based drug design.

Structure- and property-based drug design is an integral part of modern drug discovery, enabling the design of compounds aimed at improving potency and selectivity. However, building molecules using desktop modeling tools can easily lead to poor designs that appear to form many favorable interactions with the protein's active site. Although a proposed molecule looks good on screen and appears to fit into the protein site X-ray crystal structure or pharmacophore model, doing so might require a high-energy small molecule conformation, which would likely be inactive. To help scientists make better design decisions, we have built integrated, easy-to-use, interactive software tools to perform docking experiments, de novo design, shape and pharmacophore based database searches, small molecule conformational analysis and molecular property calculations. Using a combination of these tools helps scientists in assessing the likelihood that a designed molecule will be active and have desirable drug metabolism and pharmacokinetic properties. Small molecule discovery success requires project teams to rapidly design and synthesize potent molecules with good ADME properties. Empowering scientists to evaluate ideas quickly and make better design decisions with easy-to-access and easy-to-understand software on their desktop is now a key part of our discovery process.

SGX523 is an exquisitely selective, ATP-competitive inhibitor of the MET receptor tyrosine kinase with antitumor activity in vivo.

The MET receptor tyrosine kinase has emerged as an important target for the development of novel cancer therapeutics. Activation of MET by mutation or gene amplification has been linked to kidney, gastric, and lung cancers. In other cancers, such as glioblastoma, autocrine activation of MET has been demonstrated. Several classes of ATP-competitive inhibitor have been described, which inhibit MET but also other kinases. Here, we describe SGX523, a novel, ATP-competitive kinase inhibitor remarkable for its exquisite selectivity for MET. SGX523 potently inhibited MET with an IC50 of 4 nmol/L and is >1,000-fold selective versus the >200-fold selectivity of other protein kinases tested in biochemical assays. Crystallographic study revealed that SGX523 stabilizes MET in a unique inactive conformation that is inaccessible to other protein kinases, suggesting an explanation for the selectivity. SGX523 inhibited MET-mediated signaling, cell proliferation, and cell migration at nanomolar concentrations but had no effect on signaling dependent on other protein kinases, including the closely related RON, even at micromolar concentrations. SGX523 inhibition of MET in vivo was associated with the dose-dependent inhibition of growth of tumor xenografts derived from human glioblastoma and lung and gastric cancers, confirming the dependence of these tumors on MET catalytic activity. Our results show that SGX523 is the most selective inhibitor of MET catalytic activity described to date and is thus a useful tool to investigate the role of MET kinase in cancer without the confounding effects of promiscuous protein kinase inhibition.