ProBiS-ligands: a web server for prediction of ligands by examination of protein binding sites

Modeling enzyme-ligand binding in drug discovery

Enzymes are one of the most important groups of drug targets, and identifying possible ligand-enzyme interactions is of major importance in many drug discovery processes. Novel computational methods have been developed that can apply the information from the increasing number of resolved and available ligand-enzyme complexes to model new unknown interactions and therefore contribute to answer open questions in the field of drug discovery like the identification of unknown protein functions, off-target binding, ligand 3D homology modeling and induced-fit simulations.

PLIP: fully automated protein-ligand interaction profiler

The characterization of interactions in protein-ligand complexes is essential for research in structural bioinformatics, drug discovery and biology. However, comprehensive tools are not freely available to the research community. Here, we present the protein-ligand interaction profiler (PLIP), a novel web service for fully automated detection and visualization of relevant non-covalent protein-ligand contacts in 3D structures, freely available at projects.biotec.tu-dresden.de/plip-web. The input is either a Protein Data Bank structure, a protein or ligand name, or a custom protein-ligand complex (e.g. from docking). In contrast to other tools, the rule-based PLIP algorithm does not require any structure preparation. It returns a list of detected interactions on single atom level, covering seven interaction types (hydrogen bonds, hydrophobic contacts, pi-stacking, pi-cation interactions, salt bridges, water bridges and halogen bonds). PLIP stands out by offering publication-ready images, PyMOL session files to generate custom images and parsable result files to facilitate successive data processing. The full python source code is available for download on the website. PLIP's command-line mode allows for high-throughput interaction profiling.

Combined rational design and a high throughput screening platform for identifying chemical inhibitors of a Ras-activating enzyme

The Ras family small GTPases regulate multiple cellular processes, including cell growth, survival, movement, and gene expression, and are intimately involved in cancer pathogenesis. Activation of these small GTPases is catalyzed by a special class of enzymes, termed guanine nucleotide exchange factors (GEFs). Herein, we developed a small molecule screening platform for identifying lead hits targeting a Ras GEF enzyme, SOS1. We employed an ensemble structure-based virtual screening approach in combination with a multiple tier high throughput experimental screen utilizing two complementary fluorescent guanine nucleotide exchange assays to identify small molecule inhibitors of GEF catalytic activity toward Ras. From a library of 350,000 compounds, we selected a set of 418 candidate compounds predicted to disrupt the GEF-Ras interaction, of which dual wavelength GDP dissociation and GTP-loading experimental screening identified two chemically distinct small molecule inhibitors. Subsequent biochemical validations indicate that they are capable of dose-dependently inhibiting GEF catalytic activity, binding to SOS1 with micromolar affinity, and disrupting GEF-Ras interaction. Mutagenesis studies in conjunction with structure-activity relationship studies mapped both compounds to different sites in the catalytic pocket, and both inhibited Ras signaling in cells. The unique screening platform established here for targeting Ras GEF enzymes could be broadly useful for identifying lead inhibitors for a variety of small GTPase-activating GEF reactions.

Directed evolution 2.0: improving and deciphering enzyme properties

A KnowVolution: knowledge gaining directed evolution including four phases is proposed in this feature article, which generates improved enzyme variants and molecular understanding.