Ensemble-based virtual screening: identification of a potential allosteric inhibitor of Bcr-Abl

Novel Scaffold Agonists of the α2A Adrenergic Receptor Identified via Ensemble-Based Strategy

The α2A adrenergic receptor (α2A-AR) serves as a critical molecular target for sedatives and analgesics. However, α2A-AR ligands with an imidazole ring also interact with an imidazoline receptor as well as other proteins and lead to undesirable effects, motivating us to develop more novel scaffold α2A-AR ligands. For this purpose, we employed an ensemble-based ligand discovery strategy, integrating long-term molecular dynamics (MD) simulations and virtual screening, to identify new potential α2A-AR agonists with novel scaffold. Our results showed that compounds SY-15 and SY-17 exhibited significant biological effects in the preliminary evaluation of protein kinase A (PKA) redistribution assays. They also reduced levels of intracellular cyclic adenosine monophosphate (cAMP) in a dose-dependent manner. Upon treatment of the cells with 100 μM concentrations of SY-15 and SY-17, there was a respective decrease in the intracellular cAMP levels by 63.43% and 53.83%. Subsequent computational analysis was conducted to elucidate the binding interactions of SY-15 and SY-17 with the α2A-AR. The binding free energies of SY-15 and SY-17 calculated by MD simulations were -45.93 and -71.97 kcal/mol. MD simulations also revealed that both compounds act as bitopic agonists, occupying the orthosteric site and a novel exosite of the receptor simultaneously. Our findings of integrative computational and experimental approaches could offer the potential to enhance ligand affinity and selectivity through dual-site occupancy and provide a novel direction for the rational design of sedatives and analgesics.

Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives

Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.

Bcr-Abl Allosteric Inhibitors: Where We Are and Where We Are Going to

The fusion oncoprotein Bcr-Abl is an aberrant tyrosine kinase responsible for chronic myeloid leukemia and acute lymphoblastic leukemia. The auto-inhibition regulatory module observed in the progenitor kinase c-Abl is lost in the aberrant Bcr-Abl, because of the lack of the N-myristoylated cap able to bind the myristoyl binding pocket also conserved in the Bcr-Abl kinase domain. A way to overcome the occurrence of resistance phenomena frequently observed for Bcr-Abl orthosteric drugs is the rational design of allosteric ligands approaching the so-called myristoyl binding pocket. The discovery of these allosteric inhibitors although very difficult and extremely challenging, represents a valuable option to minimize drug resistance, mostly due to the occurrence of mutations more frequently affecting orthosteric pockets, and to enhance target selectivity with lower off-target effects. In this perspective, we will elucidate at a molecular level the structural bases behind the Bcr-Abl allosteric control and will show how artificial intelligence can be effective to drive the automated de novo design towards off-patent regions of the chemical space.

Docking simulation and antibiotic discovery targeting the MlaC protein in Gram-negative bacteria

To maintain the lipid asymmetry of the cell envelope in Gram-negative bacteria, the MlaC protein serves as a lipid transfer factor and delivers phospholipids from the outer to the inner membrane. A strategy of antibiotic discovery is to design a proper compound that can tightly bind to the MlaC protein and inhibit the MlaC function. In this study, we performed virtual screening on multiple MlaC structures obtained from molecular dynamics simulations to identify potential MlaC binders. Our results suggested that clorobiocin is a compound that could bind to the MlaC protein. Through the comparison of the bound geometry between clorobiocin and novobiocin, we pointed out that the methyl-pyrrole group of the noviose sugar in clorobiocin forms hydrophobic interactions with amino acids in the phospholipid binding pocket, which allows the compound to bind deep in the active site. This also explains why clorobiocin shows a tighter binding affinity than novobiocin. Our study highlights a practical path of antibiotic development against Gram-negative bacteria.

Synthesis and pharmacological evaluation of novel epidermal growth factor receptor inhibitors against prostate tumor cells

The aim of the present study was to investigate the activities of novel synthetic epidermal growth factor receptor (EGFR) inhibitors (ZINC05463076, ZINC2102846 and ZINC19901103) against prostate tumors, in vitro models and investigate the potential underlying mechanisms. A panel of prostate tumor cell lines (LNCaP, DU-145, PC-3 and LNCaP-AI cells) were used to evaluate antitumor activity of ZINC05463076, ZINC2102846, and ZINC19901103 in vitro. Cell growth and clonal formation were determined by MTT assay and Soft agar colony formation assay, respectively. An EGFR kinase assay following treatment of the compounds was performed by ELISA. Cell cycle-regulating proteins, including cyclin-dependent kinase (CDK)1, CKD2, CKD4 and inhibitory effects of these compounds on downstream signaling were analyzed by western blotting. Flow cytometry was performed to investigate apoptosis and cell cycle phases of the treated cells. It was revealed that all compounds synthesized in the present study demonstrated significant EGFR inhibition abilities, compared with approved EGFR inhibitor drug gefitinib. Treatment of LNCaP, DU-145, PC3 and LNCaP-AI cells with these compounds revealed cell proliferation inhibition and colony formation suppression dose-dependently in vitro. The agents impaired phosphorylation of EGFR and extracellular signal-regulated kinase 1/2 and suppressed their downstream signaling. In addition, these novel synthetic agents decreased the expression level of survivin, which may induce G₁ cell cycle phase arrest and cell apoptosis in PCa cells subsequently. Collectively, ZINC05463076, ZINC2102846 and ZINC19901103 exhibited significant antitumor activity in human prostate tumors in vitro, by inhibiting EGFR and promoting apoptosis, which suggested a rationale for clinical development in prostate tumor therapy.