Biology subjects

Rarey, M.

Publications and source records attributed to Rarey, M..

3 recordsLinked to original sources

bioRxiv2026

Enabling automatic generation of protein-ligand complex datasets with atomistic detail

Predicting protein-ligand bioactivities is known to be challenging yet crucial in any drug discovery project. In a protein structure-based scenario, supervised machine-learning models have been highly competitive for at least 30 years. Regardless of the machine-learning method used, dataset size and quality are key aspects in model training and validation. In general, datasets are the foundation upon which accurate performance estimates can be obtained. While well-curated repositories exist for bioactivity and protein structure data, combining these two types of data is particularly challenging. With ActivityFinder, we recently introduced a fully-automated process for linking these data sources relying on protein sequence and molecular structure only. By combining ActivityFinder with previously developed tools for structure quality estimation and property calculation, we created StrAcTable, an automatically constructed dataset of annotated protein-ligand complexes. The automated procedure allows for continued and sustainable growth. StrAcTable includes detailed descriptions of the quality of matching between ChEMBL and PDB, of the macromolecular structure, small-molecule ligands bound, and bioactivity data from ChEMBL. Based on ChEMBL Version 35, the StrAcTable contains 20 063 protein-ligand complexes with bioactivity values, enabling an efficient construction of training and validation datasets for structure-based molecular design method development.

bioinformatics↗

bioRxiv2024

DREAMER: Exploring Common Mechanisms of Adverse Drug Reactions and Disease Phenotypes through Network-Based Analysis

Adverse drug reactions (ADRs) are a major concern in clinical healthcare, significantly affecting patient safety and drug development. The need for a deeper understanding of ADR mechanisms is crucial for improving drug safety profiles in drug design and drug repurposing. This study introduces DREAMER (Drug adverse REAction Mechanism ExplaineR), a novel network-based method for exploring the mechanisms underlying adverse drug reactions and disease phenotypes at a molecular level by leveraging a comprehensive knowledge graph obtained from various datasets. By considering drugs and diseases that cause similar phenotypes, and investigating their commonalities regarding their impact on specific modules of the protein-protein interaction network, DREAMER can robustly identify protein sets associated with the biological mechanisms underlying ADRs and unravel the causal relationships that contribute to the observed clinical outcomes. Applying DREAMER to 649 ADRs, we identified proteins associated with the mechanism of action for 67 ADRs across multiple organ systems, e.g., ventricular arrhythmia, metabolic acidosis, and interstitial pneumonitis. In particular, DREAMER highlights the importance of GABAergic signaling and proteins of the coagulation pathways for personality disorders and intracranial hemorrhage, respectively. We further demonstrate the application of DREAMER in drug repurposing and propose sotalol (targeting KCNH2), ranolazine (targeting SCN5A, currently under clinical trial), and diltiazem (indicated drug targeting CACNA1C and SCN3A) as candidate drugs to be repurposed for cardiac arrest. In summary, DREAMER effectively detects molecular mechanisms underlying phenotypes emphasizing the importance of network-based analyses with integrative data for enhancing drug safety and accelerating the discovery of novel therapeutic strategies.

systems biology↗

bioRxiv2024

SARS-CoV-2 methyltransferase nsp10-16 in complex with natural and drug-like purine analogs for guiding structure-based drug discovery

Non-structural protein 10 (nsp10) and non-structural protein 16 (nsp16) are part of the RNA synthesis complex, which is crucial for the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Nsp16 exhibits 2-O-methyltransferase activity during viral messenger RNA capping and is active in a heterodimeric complex with enzymatically inactive nsp10. It has been shown that inactivation of the nsp10-16 protein complex interferes severely with viral replication, making it a highly promising drug target. As information on ligands binding to the nsp10-16 complex (nsp10-16) is still scarce, we screened the active site for potential binding of drug-like and fragment-like compounds using X-ray crystallography. The screened set of 234 compounds consists of derivatives of the natural substrate S-adenosyl methionine (SAM) and adenine derivatives, of which some have been described previously as methyltransferase inhibitors and nsp16 binders. A docking study guided the selection of many of these compounds. Here we report structures of binders to the SAM site of nsp10-16 and for two of them, toyocamycin and sangivamycin, we present additional crystal structures in the presence of a second substrate, Cap0-analog/Cap0-RNA. The identified hits were tested for binding to nsp10-16 in solution and antiviral activity in cell culture. Our data provide important structural information on various molecules that bind to the SAM substrate site which can be used as novel starting points for selective methyltransferase inhibitor designs.

biochemistry↗