Exploring ligand stability in protein crystal structures using binding pose metadynamics

Identification of correct protein-ligand binding poses is important in structure-based drug design and crucial for the evaluation of protein-ligand binding affinity. Protein-ligand coordinates are commonly obtained from crystallography experiments that provide a static model of an ensemble of conformations. Binding pose metadynamics (BPMD) is an enhanced sampling method that allows for an efficient assessment of ligand stability in solution. Ligand poses that are unstable under the bias of the metadynamics simulation are expected to be infrequently occupied in the energy landscape, thus making minimal contributions to the binding affinity. Here, the robustness of the method is studied using crystal structures with ligands known to be incorrectly modeled, as well as 63 structurally diverse crystal structures with ligand fit to electron density from the Twilight database. Results show that BPMD can successfully differentiate compounds whose binding pose is not supported by the electron density from those with well-defined electron density

Optimal water networks in protein cavities with GAsol and 3D-RISM

Motivation: Water molecules in protein binding sites play essential roles in biological processes. The popular 3D-RISM prediction method can calculate the solvent density distribution within minutes, but is difficult to convert it into explicit water molecules. Results: We present GAsol, a tool that is capable of finding the network of water molecules that best fits a particular 3D-RISM density distribution in a fast and accurate manner and that outperforms other available tools by finding the globally optimal solution thanks to its genetic algorithm. Availability: https://github.com/accsc/GAsol. BSD 3-clauses license

Structure-guided design of a domain-selective bromodomain and extra terminal N-terminal bromodomain chemical probe

Small molecule mediated disruption of the protein-protein interactions between acetylated histone tails and the tandem bromodomains of the bromodomain and extra terminal (BET) family of proteins is an important mechanism of action for the potential modulation of immuno-inflammatory and oncology disease. High quality chemical probes have proven invaluable in elucidating profound BET bromodomain biology, with seminal publications of both pan- and domain-selective BET family bromodomain inhibitors enabling academic and industrial research. To enrich the toolbox of structurally differentiated N-terminal bromodomain (BD1) BET family chemical probes, this work describes an analysis of the GSK BRD4 bromodomain dataset through a lipophilic efficiency lens, which enabled identification of a BD1 domain biased benzimidazole series. Structure guided growth targeting a key Asp/His BD1/BD2 switch enabled delivery of GSK023, a high-quality chemical probe with 300–1000-fold BET BD1 domain selectivity and a phenotypic cellular fingerprint consistent with BET bromodomain inhibition

Sintesi e studi computazionali per lo sviluppo di nuovi ligandi selettivi per il recettore beta degli estrogeni

Il progetto di tesi si occupa sia della sintesi di ligandi con già nota selettività verso il recettore beta degli estrogeni sia dell'ottenimento di nuovi possibili agonisti beta selettivi mediante l'utilizzo di studi computazionali

Predicting the Risk of Phospholipidosis with in Silico Models and an Image-Based in Vitro Screen

The drug-induced accumulation of phospholipids in lysosomes of various tissues is predominantly observed in regular repeat dose studies, often after prolonged exposure, and further investigated in mechanistic studies prior to candidate nomination. The finding can cause delays in the discovery process inflicting high costs to the affected projects. This article presents an in vitro imaging-based method for early detection of phospholipidosis liability and a hybrid approach for early detection and risk mitigation of phospolipidosis utilizing the in vitro readout with in silico model prediction. A set of reference compounds with phospolipidosis annotation was used as an external validation set yielding accuracies between 77.6% and 85.3% for various in vitro and in silico models, respectively. By means of a small set of chemically diverse known drugs with in vivo phospholipidosis annotation, the advantages of combining different prediction methods to reach an overall improved phospholipidosis prediction will be discussed

Predicting the Risk of Phospholipidosis with in Silico Models and an Image-Based in Vitro Screen

The drug-induced accumulation of phospholipids in lysosomes of various tissues is predominantly observed in regular repeat dose studies, often after prolonged exposure, and further investigated in mechanistic studies prior to candidate nomination. The finding can cause delays in the discovery process inflicting high costs to the affected projects. This article presents an in vitro imaging-based method for early detection of phospholipidosis liability and a hybrid approach for early detection and risk mitigation of phospolipidosis utilizing the in vitro readout with in silico model prediction. A set of reference compounds with phospolipidosis annotation was used as an external validation set yielding accuracies between 77.6% and 85.3% for various in vitro and in silico models, respectively. By means of a small set of chemically diverse known drugs with in vivo phospholipidosis annotation, the advantages of combining different prediction methods to reach an overall improved phospholipidosis prediction will be discussed