Fragment Binding Pose Predictions Using Unbiased Simulations and Markov-State Models

Predicting the costructure of small-molecule ligands and their respective target proteins has been a long-standing problem in drug discovery. For weak binding compounds typically identified in fragment-based screening (FBS) campaigns, determination of the correct binding site and correct binding mode is usually done experimentally via X-ray crystallography. For many targets of pharmaceutical interest, however, establishing an X-ray system which allows for sufficient throughput to support a drug discovery project is not possible. In this case, exploration of fragment hits becomes a very laborious and consequently slow process with the generation of protein/ligand cocrystal structures as the bottleneck of the entire process. In this work, we introduce a computational method which is able to reliably predict binding sites and binding modes of fragment-like small molecules using solely the structure of the apoprotein and the ligand's chemical structure as input information. The method is based on molecular dynamics simulations and Markov-state models and can be run as a fully automated protocol requiring minimal human intervention. We describe the application of the method to a representative subset of different target classes and fragments from historical FBS efforts at Boehringer Ingelheim and discuss its potential integration into the overall fragment-based drug discovery workflow

Stearylated cycloarginine nanosystems for intracellular delivery – simulations, formulation and proof of concept

Novel cationic agent liposomes performed better in silico translating in higher cellular uptake with reduced toxicity.</p

Drugit: Crowd-sourcing molecular design of non-peptidic VHL binders

Given the role of human intuition in current drug design efforts, crowd-sourced \u27citizen scientist\u27 games have the potential to greatly expand the pool of potential drug designers. Here, we introduce ‘Drugit\u27, the small molecule design mode of the online ‘citizen science’ game Foldit. We demonstrate its utility for design with a use case to identify novel binders to the von Hippel Lindau E3 ligase. Several thousand molecule suggestions were obtained from players in a series of 10 puzzle rounds. The proposed molecules were then evaluated by in silico methods and by an expert panel and selected candidates were synthesized and tested. One of these molecules, designed by a player, showed dose-dependent shift perturbations in protein-observed NMR experiments. The co-crystal structure in complex with the E3 ligase revealed that the observed binding mode matched in major parts the player’s original idea. The completion of one full design cycle is a proof of concept for the Drugit approach and highlights the potential of involving citizen scientists in early drug discovery