Novel GH-20 β-N-acetylglucosaminidase inhibitors: Virtual screening, molecular docking, binding affinity, and anti-tumor activity

Cytisine-like alkaloids from the seeds of Ormosia hosiei Hemsl. et Wils

Two new cytisine-like alkaloids, hositisines C (1) and D (2), were isolated from the seeds of Ormosia hosiei along with four known compounds, (-)-tinctorine (3), β-adenosine (4), 2'-deoxyadenosine (5), and 7, 2', 4'-trihydroxy-5-methoxyisoflavanone (6). Their structures were established using extensive spectroscopic techniques (UV, IR, CD, HRESIMS, 1 D and 2 D NMR). In the cytotoxic activity, compounds 1-3 and 5-fluorouracil (positive control) displayed inhibitory effects against HepG2 cells, exhibiting IC₅₀ values of 44.52 ± 7.83 μM, 111.49 ± 12.76 μM, 127.72 ± 18.67 μM, and 16.37 ± 3.82 μM.

Structure-based virtual screening for potent inhibitors of GH-20 β-N-acetylglucosaminidase: classical and machine learning scoring functions, and molecular dynamics simulations

GH-20 β-N-acetylglucosaminidases (GlcNAcases) are promising targets in the development of antimicrobial agents against Vibrio infections in humans and aquatic animals. In this study, we set up structure-based virtual screening to identify potential GH-20 GlcNAcase inhibitors from the Reaxys commercial database, using VhGlcNAcase from V. campbellii type strain ATCC® BAA 1116 as the protein target and Redoxal as the reference ligand. Using ChemPLP and RF-Score-VS machine learning scoring functions, eight lead compounds were identified and further evaluated for protein interaction preference and pharmacological properties. Protein-ligand analysis demonstrated that all selected compounds interacted exclusively at subsite - 1 with five hydrophobic residues W487, W505, W546, W582 and V544 at site S1, and with two polar residues, D437 and E438, at site 3. For subsite + 1, the most common residues were R274 and E584 at site 2 and I397 and Q398 at site 4. Based on the data obtained from binding free energy changes (ΔG°_binding), pharmacological property analysis and molecular dynamic simulations, two ChemPLP compounds, 338175 and 1146525, and one RF-Score-VS compound, 337447, were considered as the likely lead compounds. The most promising compound, 1146525, could serve as a scaffold for the future design of novel antimicrobial agents against Vibrio infections.

Improving structure-based virtual screening performance via learning from scoring function components

Scoring functions (SFs) based on complex machine learning (ML) algorithms have gradually emerged as a promising alternative to overcome the weaknesses of classical SFs. However, extensive efforts have been devoted to the development of SFs based on new protein-ligand interaction representations and advanced alternative ML algorithms instead of the energy components obtained by the decomposition of existing SFs. Here, we propose a new method named energy auxiliary terms learning (EATL), in which the scoring components are extracted and used as the input for the development of three levels of ML SFs including EATL SFs, docking-EATL SFs and comprehensive SFs with ascending VS performance. The EATL approach not only outperforms classical SFs for the absolute performance (ROC) and initial enrichment (BEDROC) but also yields comparable performance compared with other advanced ML-based methods on the diverse subset of Directory of Useful Decoys: Enhanced (DUD-E). The test on the relatively unbiased actives as decoys (AD) dataset also proved the effectiveness of EATL. Furthermore, the idea of learning from SF components to yield improved screening power can also be extended to other docking programs and SFs available.