Identification of Influenza PAN Endonuclease Inhibitors via 3D-QSAR Modeling and Docking-Based Virtual Screening

Discovery of novel CXCR4 inhibitors for the treatment of inflammation by virtual screening and biological evaluation

C-X-C chemokine receptor type 4 (CXCR4) exerts considerable influence on the pathogenesis of inflammatory disorders and offers a potent avenue for drug intervention. This research utilizes a hybrid virtual screening methodology constructed using computer-aided drug design to discover novel CXCR4 inhibitors for the treatment of inflammation. First, a compound library was screened by Lipinski's five rules and adsorption, distribution, metabolism, excretion and toxicity properties. Second, the HypoGen algorithm was used in constructing a 3D-QSAR pharmacophore model and verify it layer by layer, and the obtained optimal pharmacophore 1 (Hypo 1) was used as a 3D query for compound screening. Then, hit compounds were obtained through molecular docking (Libdock and CDOCKER). The toxicity of the compounds to MDA-MB-231 cells was evaluated in vitro, and their binding affinity to the target was evaluated according to how they compete with 12G5 antibody for CXCR4 on the surfaces of the MDA-MB-231 cells. Compound Hit14 showed the strongest binding affinity among the hit compounds and inhibited cell migration and invasion in Matrigel invasion and wound healing assay at a concentration of 100 nM, demonstrating a better effect than AMD3100. Western Blot experiments further showed that Hit14 blocked the CXCR4/CXCL12-mediated phosphorylation of Akt. Meanwhile, cellular thermal displacement assay analysis showed that CXCR4 protein bound to Hit14 had high thermal stability. Finally, through in vivo experiments, we found that Hit14 inhibited mouse ear inflammation and reduced ear swelling and damage. Therefore, Hit14 is a promising drug for the further development of CXCR4 inhibitors for inflammation treatment.

Integrated Virtual Screening and Validation toward Potential HPPD Inhibition Herbicide

4-Hydroxyphenylpyruvate dioxygenase (EC 1.13.11.27, HPPD) is one of the most widely studied herbicide targets and has gained significant attention. To identify potential effective HPPD inhibitors, a rational multistep virtual screening workflow was built, which included CBP models (based on the receptor-ligand interactions in the crystal complex), Hypogen models with activity prediction ability (according to the derivation of structure-activity relationships from a set of molecules with reported activity values), and a consensus docking procedure (consisting of LibDock, Glide, and CDOCKER). About 1 million molecules containing diketone or β-keto-enol substructures were filtered by Lipinski's rules, CBP model, and Hypogen model. A total of 12 compounds with similar docking postures were generated by consensus docking. Eventually, four molecules were screened based on the specific binding pattern and affinity of the HPPD inhibitor. The biological evaluation in vivo displayed that compounds III-1 and III-2 exhibited comparable herbicidal activity to isoxaflutole and possessed superior safety on various crops (wheat, rice, sorghum, and maize). The ADMET prediction (absorption, distribution, metabolism, excretion, and toxicity) showed that compound III possessed relatively good toxicological results. This work provides a theoretical basis and valuable reference for the virtual screening and molecular design of novel HPPD inhibition herbicides.

Antihypertensive Effect, ACE Inhibitory Activity, and Stability of Umami Peptides from Yeast Extract

Bioactive peptides from foods have garnered considerable attention as viable supplements for hypertensive patients. Herein, the antihypertensive effect and mechanism of umami peptides from yeast extract were investigated based on the pharmacophore model, simulated digestion, spontaneously hypertensive rat (SHR) model, and molecular docking. Notably, umami peptide LLLLPKP exhibited favorable angiotensin I-converting enzyme (ACE) inhibitory activity (IC50 = 10.22 μM) in vitro and regulated blood pressure in the SHR model with excellent durability. Remarkably, LLLLPKP showed the highest Fitvalue (4.022) of the pharmacophore model, indicating its similar pharmacological effects as ACE inhibitors. During the simulated gastrointestinal digestion, the ACE inhibition rate of LLLLPKP was merely reduced by 5.89%, but it was enzymatically cleaved into 14 peptide segments. The C-terminal sequence comprising L (4), P (5), K (6), and P (7) exhibited robust stability and a notable presence within the peptide segments postdigestion. Meanwhile, according to molecular docking, these four residues within LLLLPKP were responsible for all interactions with key sites within active pockets S1 and S2 and the active pocket of Zn2+. In light of these findings, LLLLPKP is a highly promising antihypertensive peptide. Developing this umami peptide with antihypertensive effects holds substantial importance for the long-term treatment of hypertension.

Carboxylic Acid Isostere Derivatives of Hydroxypyridinones as Core Scaffolds for Influenza Endonuclease Inhibitors

Among the most important influenza virus targets is the RNA-dependent RNA polymerase acidic N-terminal (PA_N) endonuclease, which is a critical component of the viral replication machinery. To inhibit the activity of this metalloenzyme, small-molecule inhibitors employ metal-binding pharmacophores (MBPs) that coordinate to the dinuclear Mn²⁺ active site. In this study, several metal-binding isosteres (MBIs) were examined where the carboxylic acid moiety of a hydroxypyridinone MBP is replaced with other groups to modulate the physicochemical properties of the compound. MBIs were evaluated for their ability to inhibit PA_N using a FRET-based enzymatic assay, and their mode of binding in PA_N was determined using X-ray crystallography.

Recent Advances in Application of Computer-Aided Drug Design in Anti-Influenza A Virus Drug Discovery

Influenza A is an acute respiratory infectious disease caused by the influenza A virus, which seriously threatens global human health and causes substantial economic losses every year. With the emergence of new viral strains, anti-influenza drugs remain the most effective treatment for influenza A. Research on traditional, innovative small-molecule drugs faces many challenges, while computer-aided drug design (CADD) offers opportunities for the rapid and effective development of innovative drugs. This literature review describes the general process of CADD, the viral proteins that play an essential role in the life cycle of the influenza A virus and can be used as therapeutic targets for anti-influenza drugs, and examples of drug screening of viral target proteins by applying the CADD approach. Finally, the main limitations of current CADD strategies in anti-influenza drug discovery and the field's future directions are discussed.