Biology subjects

Latek, D.

Publications and source records attributed to Latek, D..

5 recordsLinked to original sources

bioRxiv2026

Microswitch-Guided Sampling for the Detection of Ligand Signaling Bias in GPCR Systems

Biased signaling occurs when a ligand preferentially activates a specific signaling pathway at a certain receptor within a particular cellular environment. As pharmaceuticals, functionally selective (biased) compounds can engage beneficial signaling pathways while avoiding those linked to adverse effects, resulting in safer and targeted therapeutics. However, existing experimental methods for characterizing ligand bias are often time-consuming and costly, and they hardly elucidate the structural basis of biased signaling. Here, we present microswitch-guided sampling (MGS), a computational method that uses conformational changes in molecular switches to assess ligand bias in G protein-coupled receptor systems. Using initial 2 s all-atom molecular dynamics simulations of the {beta}1-adrenergic receptor ({beta}1AR), the chemokine receptor CXCR3, and the -opioid receptor (OR) in complex with either G protein or {beta}-arrestin and known biased ligands, we identified microswitches whose conformational changes were linked to activation of a specific signaling pathway. We then sampled simulation trajectories for representative complex conformations selected at these microswitch-change time points and used them as starting points for 500 ns ligand-swapped simulations, in which we exchanged biased ligands between complexes. We observed clear differences in conformational changes between complexes bound to pathway-activating and non-activating ligands in at least two of three replicas across all examined systems, demonstrating that MGS can distinguish G protein-biased from {beta}-arrestin-biased ligands. These findings show that MGS is an effective tool for assessing signaling pathway bias in GPCR systems and suggest that this methodology can be applied to other GPCR targets to act as a fast, ns-scale computational assay to detect functional selectivity with molecular dynamics.

pharmacology and toxicology↗

bioRxiv2024

GPCRVS - AI-driven decision support system for GPCR virtual screening

SummaryG protein-coupled receptors (GPCRs) constitute the largest and most frequently used family of molecular drug targets. The simplicity of GPCR drug design results from their common seven-transmembrane helix topology and well-understood signaling pathways. GPCRs are extremely sensitive to slight changes in the chemical structure of compounds, which allows for the reliable design of highly selective and specific drugs. Only recently has the number of GPCR structures, both in their active and inactive conformations together with their active ligands, become sufficient to comprehensively apply machine learning in decision support systems to predict compound activity for drug design. Here, we describe GPCRVS, an efficient machine learning web service for the online assessment of compound activity against several GPCR targets, including peptide and protein-binding GPCRs, the most difficult for virtual screening tasks. As a decision support system, GPCRVS evaluates compounds in terms of their activity range, the pharmacological effect they exert on the receptor, and the binding modes they could possibly demonstrate for certain types of GPCR receptors. GPCRVS can be applied to compounds ranging from small molecules to short peptides uploaded as common chemical format files. The activity class assignment and the binding affinity prediction are provided in a reference to similar predictions precomputed for already known active ligands of each GPCR target. This multiclass classification in GPCRVS, handling often incomplete and fuzzy biological data, was validated on ChEMBL-retrieved data sets for secretin-like and chemokine GPCR receptors. Availability and ImplementationGPCRVS is freely available at: https://gpcrvs.chem.uw.edu.pl

pharmacology and toxicology↗

bioRxiv2023

Keras/TensorFlow in Drug Design for Immunity Disorders

Homeostasis of the host immune system is regulated by white blood cells with a variety of cell surface receptors for cytokines. Chemotactic cytokines (chemokines) activate their receptors to evoke the chemotaxis of immune cells in homeostatic migrations or inflammatory conditions towards inflamed tissue or pathogens. Dysregulation of the immune system leading to disorders such as allergies, autoimmune diseases, or cancer requires efficient, fast-acting drugs to minimize the long-term effects of chronic inflammation. Here, we performed structure-based virtual screening (SBVS) assisted by the Keras/TensorFlow neural network (NN) to find novel compound scaffolds acting on three chemokine receptors: CCR2, CCR3 and one CXC receptor CXCR3. Keras/TensorFlow NN was used here not as a typically used binary classifier, but as an efficient multi-class classifier that can discard not only inactive compounds but also low or medium-activity compounds. Several compounds proposed by SBVS and NN were tested in 100 ns all-atom molecular dynamics simulations to confirm their binding affinity. To improve the basic binding affinity of the compounds, new chemical modifications were proposed. The modified compounds were compared with known antagonists of these three chemokine receptors. Known CXCR3 were among the top predicted compounds and thus benefits of using Keras/TensorFlow in drug discovery have been shown in addition to structure-based approaches. Furthermore, we showed that Keras/TensorFlow NN can accurately predict the receptor subtype selectivity of compounds, for which SBVS often fails. We cross-tested chemokine receptor datasets retrieved from ChEMBL and curated datasets for cannabinoid receptors available at: http://db-gpcr-chem.uw.edu.pl. The NN model trained on the cannabinoid receptor datasets retrieved from ChEMBL was the most accurate in the receptor subtype selectivity prediction. Among NN models trained on the chemokine receptor datasets, the CXCR3 model showed the highest accuracy in differentiating the receptor subtype for a given compound dataset.

immunology↗

bioRxiv2022

Helix 8 in chemotactic receptors of the complement system.

Host response to infection involves activation of the complement system leading to producing of anaphylotoxins C3a and C5a. A complement factor C5a exerts its effect through activation of C5aR1, chemotactic receptor 1, and triggers the G protein-coupled signaling cascade. Orthosteric and allosteric antagonists of C5aR1 are a novel strategy for anti-inflammatory therapies. Here, we discuss recent crystal structures of inactive C5aR1 in terms of an inverted orientation of helix H8, unobserved in other GPCR structures. Analysis of mutual interactions of subunits in the C5aR1 - G protein complex has provided new insights into the activation mechanism of this distinct receptor. By comparison of C5aR1 and its homolog C5aR2 we explained differences between their signaling pathways on the molecular level. A comparison of microsecond MD trajectories started from active and inactive receptor conformations also enabled to elucidate details of local and global changes in the transmembrane domain induced by interactions with the G subunit and to explain the impact of inverted H8 on the receptor activation.

immunology↗

bioRxiv2019

GPCRmd uncovers the dynamics of the 3D-GPCRome

G protein-coupled receptors (GPCRs) are involved in numerous physiological processes and are the most frequent targets of approved drugs. The explosion in the number of new 3D molecular structures of GPCRs (3D-GPCRome) during the last decade has greatly advanced the mechanistic understanding and drug design opportunities for this protein family. While experimentally-resolved structures undoubtedly provide valuable snapshots of specific GPCR conformational states, they give only limited information on their flexibility and dynamics associated with function. Molecular dynamics (MD) simulations have become a widely established technique to explore the conformational landscape of proteins at an atomic level. However, the analysis and visualization of MD simulations requires efficient storage resources and specialized software, hence limiting the dissemination of these data to specialists in the field. Here we present the GPCRmd (http://gpcrmd.org/), an online platform that incorporates web-based visualization capabilities as well as a comprehensive and user-friendly analysis toolbox that allows scientists from different disciplines to visualize, analyse and share GPCR MD data. GPCRmd originates from a community-driven effort to create the first open, interactive, and standardized database of GPCR MD simulations. We demonstrate the power of this resource by performing comparative analyses of multiple GPCR simulations on two mechanisms critical to receptor function: internal water networks and sodium ion interaction.

biophysics↗