Application of structure-based drug design to G-protein coupled receptors (GPCRs) has received a major stimulus by the recent availability of several novel x-ray crystal structures of this important class of proteins. In this overview of in-house research and examples from the literature, Applications Scientist Thijs Beuming presents a variety of computational techniques applied to the study of GPCRs, including homology modeling, induced fit docking, structural interaction fingerprints, and molecular dynamics simulations.

In this seminar, Schrödinger Application Scientist Noeris Salam discusses the development and validation of a novel method to develop energetically optimized, structure-based pharmacophores for use in rapid in silico screening. The method combines pharmacophore perception and database screening with protein-ligand energetic terms computed by the Glide XP scoring function to rank the importance of pharmacophore features. The application of this method, including the results of database screens for known actives, is presented for 30 pharmaceutically relevant crystal structures. The method is compared to three other approaches: (1) pharmacophore hypotheses derived from a systematic assessment of receptor-ligand contacts, (2) Glide SP docking, and (3) 2D ligand fingerprint similarity. The method developed here shows better enrichments than the other three methods and yields a greater diversity of actives than the contact-based pharmacophores or the 2D ligand similarity. Docking produces the most cases (28/30) with enrichments greater than 10.0 in the top 1% of the database and on average produces the greatest diversity of active molecules. The combination of energy terms from a structure-based analysis with the speed of a ligand-based pharmacophore search results in a method that leverages the strengths of both approaches to produce high enrichments with a good diversity of active molecules.

Dr. Salam will also present past work by Schrödinger Applications Scientists on the energetic analysis of docked fragment poses to produce structure-based pharmacophores that incorporate key features for molecular recognition.

KNIME has established itself as the leading open-source data pipelining tool, and provides an ideal platform for researchers looking to automate complex computational workflows and data analysis. Schrödinger KNIME Extensions provides over 100 "nodes" that allow researchers to incorporate Schrödinger software into these workflows. Schrödinger also distributes a variety of pre-assembled, editable KNIME workflows that can be used to perform not only common modeling experiments, but also recently published algorithms that have garnered significant interest. In this seminar, Schrödinger Applications Scientist Noeris Salam presents an overview of Schrödinger KNIME Extensions, describes some of the most significant enhancements in the recently released KNIME 2.0, and explains how to create workflows using the KNIME interface.

Many drug-like molecules can access multiple protonation states at physiological pH. These states must be considered in in silico docking and scoring approaches, as they can have distinct binding modes and calculated affinities with receptors. While it is straightforward to enumerate the states, prioritizing them such that low population states are penalized (thereby reducing the frequency of false positives) is challenging. Furthermore, different populations of protonation states must be considered for molecules that interact with a metal in the binding site.

In this seminar, Epik product manager John Shelley discusses the development of a method for calculating free energy penalties for protonation states. When these penalties are applied to the docking score in Glide, a significant increase in database enrichment is observed. Dr. Shelley describes how Epik assigns protonation states and penalties, and presents validation results involving a diverse set of targets, including metal-containing binding sites.

Schrödinger has developed its Induced Fit Docking methodology to accurately predict ligand binding modes in circumstances where changes to the receptor shape are needed to accommodate the ligand. The method involves a combination of Glide docking using a softened potential, followed by a Prime protein structure refinement, and finishes with a Glide energy evaluation to obtain the top-scoring ligand-receptor pair. Validation studies have been performed on targets for which standard docking methods fail, and show that Induced Fit Docking can be successfully applied to a wide variety of relevant targets.