Molecular Diversity最新文献

Cyclotides are a class of plant-derived cyclic peptides having a distinctive structure with a cyclic cystine knot (CCK) motif. They are stable molecules that naturally play a role in plant defense. Till date, more than 750 cyclotides have been reported among diverse plant taxa belonging to Cucurbitaceae, Violaceae, Rubiaceae, Solanaceae, and Fabaceae. These native cyclotides exhibit several bioactivities, such as anti-bacterial, anti-HIV, anti-fungal, pesticidal, cytotoxic, and hemolytic activities which have immense significance in agriculture and therapeutics. The general mode of action of cyclotides is related to their structure, where their hydrophobic face penetrates the cell membrane and disrupts it to exhibit anti-microbial, cytotoxic, or hemolytic activities. Thus, the structure-activity relationship is of significance in cyclotides. Further, owing to their, small size, stability, and potential to interact and cross the membrane barrier of cells, they make promising choices for developing peptide-based biologics. However, challenges, such as production complexity, pharmacokinetic limitations, and off-target effects hinder their development. Advancements in cyclotide engineering, such as peptide grafting, ligand conjugation, and nanocarrier integration, heterologous production along with computational design optimization, can help overcome these challenges. Given the potential of these cyclic peptides, the present review focuses on the diversity, bioactivities, and structure-activity relationships of cyclotides, and advancements in cyclotides engineering emphasizing their unique attributes for diverse medical and biotechnological applications.

Disruption of the mycobacterial redox homeostasis leads to irreversible stress induction and cell death. Hydroquinone scaffolds, as a new type of redox cycling anti-tuberculosis chemotypes, exhibit potent bactericidal activity against non-replicating, nutrient-deprived phenotypically drug-resistant bacteria. Evidences from microbiological, biochemical, and genetic studies indicate that the redox-driven mode of action relies on the reduction of quinones by type II NADH dehydrogenase (NDH2), generating reactive oxygen species (ROS) of bactericidal level. This study demonstrates that (S)-Peniphenone D possesses significant resistance to Mycobacterium marinum (M. marinum) infection, as it enables redox cycling within M. marinum cells, ROS production, and reduction of intracellular NADH levels. The results suggest that hydroquinone compounds, due to their distinctive biological activities, could serve as novel sources for antibacterial drugs, particularly in developing scaffolds for new anti-tuberculosis agents.

Molecular Property Prediction (MPP) is a fundamental task in important research fields such as chemistry, materials, biology, and medicine, where traditional computational chemistry methods based on quantum mechanics often consume substantial time and computing power. In recent years, machine learning has been increasingly used in computational chemistry, in which graph neural networks have shown good performance in molecular property prediction tasks, but they have some limitations in terms of generalizability, interpretability, and certainty. In order to address the above challenges, a Multiscale Molecular Structural Neural Network (MMSNet) is proposed in this paper, which obtains rich multiscale molecular representations through the information fusion between bonded and non-bonded "message passing" structures at the atomic scale and spatial feature information "encoder-decoder" structures at the molecular scale; a multi-level attention mechanism is introduced on the basis of theoretical analysis of molecular mechanics in order to enhance the model's interpretability; the prediction results of MMSNet are used as label values and clustered in the molecular library by the K-NN (K-Nearest Neighbors) algorithm to reverse match the spatial structure of the molecules, and the certainty of the model is quantified by comparing virtual screening results across different K-values. Experimental results in the authoritative small molecule dataset QM9 and the macromolecular protein database PDBbind demonstrate that MMSNet has optimal prediction accuracy, model complexity, and generalizability compared with more than ten existing state-of-the-art (SOTA) models in a variety of different types of prediction tasks; it has a great potential for downstream tasks such as chemical research, drug discovery, and material design.

A series of new arecoline derivatives containing amino acid fragments were synthesized, and their fungicidal activities were investigated. All synthesized compounds were characterized by ¹H NMR, ¹³CNMR, and HRMS. Preliminary bioactivity assays demonstrated that Compounds 3k, 3n, 3p, 3q, 3r, and 3s exhibited significant antifungal activity against Botryosphaeria cactivora, Botryosphaeria dothidea, and Fusarium pseudograminearum at a concentration of 100 μg/mL. Among them, Compound 3s displayed the highest inhibitory activity against Botryosphaeria dothidea (96.63%), surpassing the commercial fungicide chlorothalonil (91.30%). To explore the underlying mechanisms of the compounds, preliminary investigations into the antifungal mechanism involved molecular docking study, scanning electron microscopy and fluorescence microscopy observations, assessments of membrane permeability, and measurements of malondialdehyde content were carried out, respectively. The findings demonstrated that Compound 3s effectively inhibits fungal hyphal growth by compromising the integrity of the hyphal cell membrane. These results indicate that arecoline derivatives containing amino acid benzyl esters have potential as promising fungicides.

The current research focused on the synthesis of two series of pyrazole derivatives and evaluation of their insecticidal effectiveness. In the first series, seven pyrazole Schiff bases 3a-g were successfully synthesized with yields (79-95%) by condensing phenylfuran-2-carbaldehyde with substituted pyrazole rings. In the second series, eleven amino acid-pyrazole conjugates 6a-k were synthesized utilizing acetic acid, sulfuric acid, morpholine, and EDC. HCl achieving yields of 59% to 94%. The synthesized compounds were assessed for their chemotherapeutic efficacy against locusts and termites by calculating LC₅₀ values, thereby determining their potential as anti-termite and anti-locust agents. Among the eighteen synthesized pyrazole compounds, the Schiff base pyrazole molecules 3f (LC₅₀ = 0.001 μg/mL) and 3d (LC₅₀ = 0.006 μg/mL) demonstrated excellent anti-termite activity compared to the reference drug fipronil (LC₅₀ = 0.038 μg/mL). Pyrazole derivative 6 h with LC₅₀ = 47.68 μg/mL exhibited superior anti-locust activity than the reference drug fipronil (LC₅₀ = 63.09 μg/mL). Additionally, compound 3b, containing NO₂ functionality at the meta position, exhibited notable and significant anti-locust activity with an LC₅₀ values of 100.00 μg/mL. However, the highest mortality was caused by the glycine conjugate of fipronil 6 h of the 2nd series with an LC₅₀ value of 47.68 μg/mL, which also proved to be a potent anti-locust agent. This study explores the efficacy of biologically active pyrazole structures as potential insecticidal agents through a combination of virtual molecular docking analysis and biological experimental investigations. The results demonstrate a strong correlation between the computational predictions and experimental outcomes, suggesting that the pyrazole derivatives exhibit significant insecticidal properties. The findings highlight the potential of these compounds in the development of innovative insecticides, paving the way for future research in pest control strategies.

Cancer, a leading global cause of death, presents considerable treatment challenges due to resistance to conventional therapies like chemotherapy and radiotherapy. Cyclin-dependent kinase 11 (CDK11), which plays a pivotal role in cell cycle regulation and transcription, is overexpressed in various cancers and is linked to poor prognosis. This study focused on identifying potential inhibitors of CDK11 using computational drug discovery methods. Techniques such as pharmacophore modeling, virtual screening, molecular docking, ADMET predictions, molecular dynamics simulations, and binding free energy analysis were applied to screen a large natural product database. Three pharmacophore models were validated, leading to the identification of several promising compounds with stronger binding affinities than the reference inhibitor. ADMET profiling indicated favorable drug-like properties, while molecular dynamics simulations confirmed the stability and favorable interactions of top candidates with CDK11. Binding free energy calculations further revealed that UNPD29888 exhibited the strongest binding affinity. In conclusion, the identified compound shows potential as a CDK11 inhibitor based on computational predictions, suggesting their future application in cancer treatment by targeting CDK11. These computational findings encourage further experimental validation as anti-cancer agents.

ROCK inhibitors can inhibit IL-1β and NLRP3, and their therapeutic potential for osteoarthritis and rheumatoid arthritis has been confirmed, but their impact on gouty arthritis has not been reported yet. By hybridization the structure of Edaravone, a series of ROCK inhibitors with pyrazolone scaffold were designed and synthesized. RM-04 has acceptable selective ROCK2 inhibitory activity with an IC₅₀ of 4.62 µM, and its IC₅₀ values for scavenging DPPH^• and ABTS^•+ are 16.72 µM and 23.15 µM, respectively, which is equivalent to that of Edaravone. Furthermore, RM-04 exhibits good pharmacokinetic properties and good safety in vivo. Meanwhile, in sodium urate-induced acute gout model, RM-04 at a dose of 5 mg/kg exhibited the alleviating effect approximately equivalent to that of Celecoxib, indicating that ROCKs inhibitors with antioxidation activity could reduce the damage caused by gouty arthritis.

A series of flavonoid derivatives containing piperazine sulfonate were designed and synthesized. The results of antiviral experiments in vivo showed that some target compounds had good inhibitory effect on tobacco mosaic virus (TMV). The EC₅₀ values of S15 and S19 curative activity were 174.5 and 110.4 μg/mL, respectively, which were better than 253.7 μg/mL of Ningnanmycin (NNM). The EC₅₀ values of S4 and S19 protection activity were 140.3 and 116.1 μg/mL, respectively, better than that of NNM (247.1 μg/mL). Microscale thermophoresis (MST) and molecular docking experiments showed that S19 had a good molecular binding force with TMV. Transmission electron microscopy (TEM) results show that S19 can fracture TMV particles and affect self-assembly. S19 treatment had almost no effect on the growth of seeds and seedlings, and can change the content of chlorophyll malondialdehyde (MDA) and superoxide dismutase (SOD) in tobacco to a certain extent, and improve the disease resistance of tobacco.

The drug combination is an attractive approach for cancer treatment. PARP and kinase inhibitors have recently been explored against cancer cells, but their combination has not been investigated comprehensively. In this study, we used various drug combination databases to build ML models for drug combinations against brain cancer cells. Some decision tree-based models were used for this purpose. The results were further evaluated using molecular docking and molecular dynamics (MD) simulation. The possibility of the hit drug combinations for crossing the Blood-brain barrier (BBB) was also examined. Based on the obtained results, the combination of niraparib, as the PARP inhibitor, and lapatinib, as the kinase inhibitor, exhibited more considerable outcomes with a remarkable model performance (accuracy of 0.915) and prediction confidence of 0.92. The protein tweety homolog 3 and BTB/POZ domain-containing protein 2 are the main targets of niraparib and lapatinib with - 10.2 and - 8.5 scores, respectively. Due to the outcomes, this drug combination can use the CAT1 transporter on the BBB surface and effectively cross the BBB. Based on the obtained results, niraparib-lapatinib can be a promising drug combination candidate for brain cancer treatment. This combination is worth to be examined by experimental investigation in vitro and in vivo.

Apigenin, a dietary flavonoid with notable anti-cancer properties, has emerged as a promising candidate for the treatment of neurodegenerative disorders, particularly Alzheimer's disease (AD). While extensively studied for its ability to modulate key molecular pathways in cancers, apigenin also exerts neuroprotective effects by reducing neuroinflammation, protecting neurons from oxidative stress, and enhancing neuronal survival and synaptic plasticity. This dual functionality makes apigenin an intriguing therapeutic option for diseases like AD, where kinase dysregulation plays a central role. In this study, we focus on Microtubule Affinity-Regulating Kinase 4 (MARK4), a key enzyme implicated in tauopathies associated with AD, as well as in cancer progression. Through in silico analysis, we explore the interaction between apigenin and MARK4, revealing significant structural changes within the kinase domain upon ligand binding. These computational findings were confirmed via experimental assays using purified recombinant MARK4, where apigenin demonstrated potent inhibition with an IC₅₀ value of 2.39 µM. Fluorescence binding assays further confirmed a strong binding affinity (Ka = 10⁸ M^-1), indicating that apigenin efficiently occupies the MARK4 active site, thereby suppressing its enzymatic activity. These results position apigenin as a potent inhibitor of MARK4, offering a dual therapeutic advantage-both as an anti-cancer agent and as a neuroprotective compound for the potential treatment of AD. This study opens new avenues for the development of apigenin-based therapeutics targeting kinase dysregulation in cancer and neurodegeneration.