Biology subjects

Denzinger, K.

Publications and source records attributed to Denzinger, K..

2 recordsLinked to original sources

bioRxiv2025

Virtual Screening-Guided Discovery of Small Molecule CHI3L1 Inhibitors with Functional Activity in Glioblastoma Spheroids

Chitinase-3-like protein 1 (CHI3L1), a glycoprotein implicated in inflammation, fibrosis, and cancer, has emerged as a potential therapeutic target for glioblastoma (GBM). CHI3L1 contributes to tumor progression and immune evasion by promoting STAT3 signaling and mesenchymal transition. To identify small molecule CHI3L1 inhibitors, a structure-based 3D pharmacophore model was developed and applied to virtually screen over 4.4 million compounds from the Enamine collection. Following multi-tiered filtering, 35 candidates were selected for experimental evaluation. Binding validation via microscale thermophoresis (MST) confirmed dose-dependent CHI3L1 interactions for two compounds, 8 and 39, with dissociation constants (Kd) of 6.8 {micro}M and 22 {micro}M, respectively. These affinities were further supported by surface plasmon resonance (SPR), which yielded Kd values of 5.69 {micro}M for compound 8 and 17.09 {micro}M for compound 39. In 3D GBM spheroid models, compound 8 significantly reduced spheroid viability and attenuated phospho-STAT3 levels, consistent with CHI3L1 pathway disruption. These findings identify two promising scaffolds and support the utility of pharmacophore-guided virtual screening for discovering functionally active ligands targeting CHI3L1 in GBM. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/667816v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@291464org.highwire.dtl.DTLVardef@f9d224org.highwire.dtl.DTLVardef@1535eeaorg.highwire.dtl.DTLVardef@7c596e_HPS_FORMAT_FIGEXP M_FIG Table of Contents artwork C_FIG

pharmacology and toxicology↗

bioRxiv2024

CACHE Challenge #1: targeting the WDR domain of LRRK2, a Parkinson's Disease associated protein.

The CACHE challenges are a series of prospective benchmarking exercises meant to evaluate progress in the field of computational hit-finding. Here we report the results of the inaugural CACHE #1 challenge in which 23 computational teams each selected up to 100 commercially available compounds that they predicted would bind to the WDR domain of the Parkinsons disease target LRRK2, a domain with no known ligand and only an apo structure in the PDB. The lack of known binding data and presumably low druggability of the target is a challenge to computational hit finding methods. Seventy-three of the 1955 procured molecules bound LRRK2 in an SPR assay with KD lower than 150 M and were advanced to a hit expansion phase where computational teams each selected up to 50 analogs each. Binding was observed in two orthogonal assays with affinities ranging from 18 to 140 M for seven chemically diverse series. The seven successful computational workflows varied in their screening strategies and techniques. Three used molecular dynamics to produce a conformational ensemble of the targeted site, three included a fragment docking step, three implemented a generative design strategy and five used one or more deep learning steps. CACHE #1 reflects a highly exploratory phase in computational drug design where participants sometimes adopted strikingly diverging screening strategies. Machine-learning accelerated methods achieved similar results to brute force (e.g. exhaustive) docking. First-in-class, experimentally confirmed compounds were rare and weakly potent, indicating that recent advances are not sufficient to effectively address challenging targets.

biochemistry↗