Molecular Diversity最新文献

Protein-ligand interactions are the molecular basis of many important cellular activities, such as gene regulation, cell metabolism, and signal transduction. Protein-ligand binding affinity is a crucial metric of the strength of the interaction between the two, and accurate prediction of its binding affinity is essential for discovering drugs' new uses. So far, although many predictive models based on machine learning and deep learning have been reported, most of the models mainly focus on one-dimensional sequence and two-dimensional structural characteristics of proteins and ligands, but fail to deeply explore the detailed interaction information between proteins and ligand atoms in the binding pocket region of three-dimensional space. In this study, we introduced a novel 4D tensor feature to capture key interactions within the binding pocket and developed a three-dimensional convolutional neural network (CNN) model based on this feature. Using ten-fold cross-validation, we identified the optimal parameter combination and pocket size. Additionally, we employed feature engineering to extract features across multiple dimensions, including one-dimensional sequences, two-dimensional structures of the ligand and protein, and three-dimensional interaction features between them. We proposed an efficient protein-ligand binding affinity prediction model MCDTA (multi-dimensional convolutional drug-target affinity), built on a multi-dimensional convolutional neural network framework. Feature ablation experiments revealed that the 4D tensor feature had the most significant impact on model performance. MCDTA performed exceptionally well on the PDBbind v.2020 dataset, achieving an RMSE of 1.231 and a PCC of 0.823. In comparative experiments, it outperformed five other mainstream binding affinity prediction models, with an RMSE of 1.349 and a PCC of 0.795. Moreover, MCDTA demonstrated strong generalization ability and practical screening performance across multiple benchmark datasets, highlighting its reliability and accuracy in predicting protein-ligand binding affinity. The code for MCDTA is available at https://github.com/dfhuang-AI/MCDTA .

The development of phosphorylation-suppressing inhibitors targeting Signal Transducer and Activator of Transcription 3 (STAT3) represents a promising therapeutic strategy for non-small cell lung cancer (NSCLC). In this study, a generative model was developed using transfer learning and virtual screening, leveraging a comprehensive dataset of STAT3 inhibitors to explore the chemical space for novel candidates. This approach yielded a chemically diverse library of compounds, which were prioritized through molecular docking and molecular dynamics (MD) simulations. Among the identified candidates, the HG110 molecule demonstrated potent suppression of STAT3 phosphorylation at Tyr705 and inhibited its nuclear translocation in IL6-stimulated H441 cells. Rigorous MD simulations further confirmed the stability and interaction profiles of top candidates within the STAT3 binding site. Notably, HG106 and HG110 exhibited superior binding affinities and stable conformations, with favorable interactions involving key residues in the STAT3 binding pocket, outperforming known inhibitors. These findings underscore the potential of generative deep learning to expedite the discovery of selective STAT3 inhibitors, providing a compelling pathway for advancing NSCLC therapies.

Prostate cancer (PC) is among the most prevalent cancers in males. It is the leading cause of death in men, in around 48 out of 185 countries. Increased androgen receptor (AR) activity is the key factor contributing to the development or progression of newly diagnosed cases of prostate cancer. Over time, numerous compounds targeting AR have been identified, presenting encouraging avenues for suppressing its hyperactivity. In our investigation, we used the GEPIA tool to study the importance of AR in the context of prostate cancer. This tool integrates the data from TCGA and GTEx in the gene expression pattern analysis and their clinical relevance. This analysis evaluates overall survival, disease-free survival, and transcripts per million (TPM) analysis of AR in PC. We performed docking and simulation for FDA-approved anticancer drugs to assess their potential interactions with the AR. We also conducted a comprehensive analysis of drugs using a quantum calculation (DFT) which provides electronic properties, chemical reactivity, and stability using the HOMO-LUMO energy gap. This study suggests that repurposed synthetic anticancer drugs could be better options for treating prostate cancer by inhibiting AR. In this work, we have shown the potential of pomalidomide, a synthetic anticancer drug, as a potential candidate for androgen-dependent PC treatment.

The twenty-first century presents a serious threat to public health due to the growth in antibiotic resistance among opportunistic bacteria, particularly within the ESKAPE group, which includes Enterobacter species with high morbidity, mortality, virulence, and nosocomial dissemination rates. Enterobacter species, especially Enterobacter cloacae, bacteria have developed resistance to multiple antibiotics through mechanisms, such as continuous production of AmpC beta-lactamase. In this study, a comprehensive bioinformatics approach was employed to analyze the genome of Enterobacter cloacae, utilizing sequence data from GenBank (ID: OW968328.1). The AbritAMR and ResFinder tools were utilized to identify antibiotic-resistant genes, which included the presence of blaOXA-48, blaCMH, FosA, OqxA, and OqxB each conferring resistance to specific antibiotics such as β-lactams and fluoroquinolones. These proteins were analyzed using bioinformatics tools such as ProtParam, SOPMA, Robetta, I-TASSER, AlphaFold, and PROCHECK to investigate different structural models and their properties. The models from AlphaFold had the best quality in terms of structural accuracy, providing valuable insights into the 3D conformations of these resistant proteins. Based on the Molecular docking studies, these constructed targets were docked with 20 natural compounds known for their activity against Gram-negative bacteria. Among them, Deacetylnomilin showed the highest docking score and passed their ADMET properties. Molecular dynamic (MD) simulation was conducted for 100 ns for Deacetylnomilin with different resistant proteins. Deacetylnomilin exhibited more favorable binding free energies compared to the reference compounds across all five proteins, indicating higher stability and affinity. These results suggest that Deacetylnomilin could be an effective inhibitor against the resistant proteins of Enterobacter cloacae, making it a promising candidate for further drug development.

The Junin virus causes Argentine hemorrhagic fever, leading to severe complications such as high fever, malaise, muscle pain, and bleeding disorders, including hemorrhages in the skin and mucous membranes. Neurological issues like confusion, seizures, and coma can also occur. Without prompt and effective treatment, the disease can be fatal, with mortality rates reaching up to 30%. Taking serious measures is essential to mitigate the spread of the disease. Vaccination is the most effective choice to neutralize the Junin virus in the current situation. Consequently, to design the highly immunogenic and non-allergenic multi-epitope subunit vaccine against the Junin virus, we employed the immunoinformatic approach to screen the glycoprotein, nucleoprotein, and RDRP protein for potential immunogenic CTL (Cytotoxic T Lymphocyte), HTL (Helper T Lymphocyte) and B (B Lymphocyte) cell epitopes. Afterward, the predicted epitopes were subjected to 3D modeling and validation. The strong binding affinity of the constructed vaccines with the human TLR3 was confirmed through molecular docking, with scores of - 333 kcal/mol for glycoprotein, - 297 kcal/mol for nucleoprotein, - 308 kcal/mol for RDRP, and - 305 kcal/mol for combined vaccines. Additionally, the binding free energies recorded were - 63.54 kcal/mol, - 64.16 kcal/mol, - 56.81 kcal/mol, and - 51.52 kcal/mol, respectively. Furthermore, the dynamic stability, residual fluctuation, and compactness of vaccine-TLR-3 complexes were confirmed by the molecular dynamic simulation. The codon adaptation index (CAI) values and high GC content confirmed the stable expression of constructed vaccines in the pET-28a ( +) expression vector. The immune simulation analysis demonstrated that administering booster doses of the developed vaccines resulted in a notable increase in IgG, IgM, interleukins, and cytokines levels, indicating effective antigen clearance over time. In conclusion, our study provides preclinical evidence for designing a highly effective Junin virus vaccine, necessitating further in-vitro and in-vivo experiments.

Metastatic cervical cancer, the advanced stage where the cancer spreads beyond the cervix to other parts of the body, poses significant treatment challenges and is associated with poor survival rates. Vascular Endothelial Growth Factor Receptor 2 (VEGFR2), a critical angiogenic mediator, is upregulated in metastatic cervical cancer, driving the formation of new blood vessels that fuel tumor growth and spread, making it an attractive target for anti-angiogenic therapies aimed at halting metastasis. This study aims to determine the anti-angiogenic effects of natural compounds to identify new VEGFR2 inhibitors for managing metastatic cervical cancer. The potential effect of these compounds as VEGFR2 inhibitors at the structural level was assessed using various methods such as virtual screening, docking, MD simulations (1000 ns), binding free energy calculations, and free energy landscape analysis. Four compounds, including IMPHY007574, IMPHY004129, IMPHY008783, and IMPHY004928, were found to be potential VEGFR2 inhibitors. Among the structures analyzed in the present work, IMPHY007574 revealed the highest binding stability with VEGFR2 and the most favorable interaction pattern, thus proving the possibility of its use as an effective anti-angiogenic compound. The other three compounds also demonstrated a reasonably good promise in VEGFR2 inhibition. These findings provide a foundation for developing novel therapeutic strategies for metastatic cervical cancer, potentially overcoming drug resistance and improving patient survival rates.

Acquired immunodeficiency syndrome (AIDS) poses a significant threat to life. Antiretroviral therapy is employed to diminish the replication of the human immunodeficiency virus (HIV), extending life expectancy and improving the quality of patients' lives. These HIV-1 integrase inhibitors form robust covalent interactions with Mg²⁺ ions, contributing to their tight binding, thereby inhibiting the integration of viral DNA into the CD4 cell DNA. The second-generation INSTIs, the most recently approved, exhibit a higher genetic barrier compared to first-generation drugs. Hence, there is a need to develop novel and safe compounds as inhibitors of HIV-1 integrase. This article presents an overview of the current landscape of anti-HIV-1 integrase inhibitors, emphasizing the structure-activity relationship (SAR) of small molecules. The molecules discussed include monocyclic rings consisting of triazoles moiety, and pyrimidine analog along with bicyclic rings with nitrogen-containing moieties. Researchers are exploring anti-HIV-1 integrase inhibitors from natural sources like marine environments, plant extracts, and microbial products, emphasizing the importance of diverse bioactive compounds in combating the virus, which have also been included in the manuscript. The current manuscript will be helpful to the scientific community engaged in the manipulation of small molecules as anti-HIV integrase inhibitors for designing newer leads.

The present study aimed to develop robust machine learning (ML) models to predict the skin permeability of poorly water-soluble drugs in the presence of menthol and limonene as penetration enhancers (PEs). The ML models were also applied in virtual screening (VS) to identify hydrophobic drugs that exhibited better skin permeability in the presence of permeation enhancers i.e. menthol and limonene. The drugs identified through ML-based VS underwent experimental validation using in vitro skin penetration studies. The developed model predicted 80% probability of permeability enhancement for Sumatriptan Succinate (SS), Voriconazole (VCZ), and Pantoprazole Sodium (PS) with menthol and limonene. The in vitro release studies revealed that menthol increased penetration by approximately 2.49-fold, 2.25-fold, and 4.96-fold for SS, VCZ, and PS, respectively, while limonene enhanced permeability by approximately 1.32-fold, 2.27-fold, and 3.7-fold for SS, VCZ, and PS. The results from in silico and in vitro studies were positively correlated, indicating that the developed ML models could effectively reduce the need for extensive in vitro and in vivo experimentation.

MurB or UDP-N-acetylenolpyruvoylglucosamine reductase (EC 1.3.1.98) is involved in the synthesis of bacterial cell walls of Salmonella typhimurium LT2 as it catalyzes one of the reactions in the formation of peptidoglycan. Since the enzyme is required for bacterial survival and is not present in humans, this makes it an ideal drug target, for multidrug resistance (MDR) strains. Thus, we proceeded with the identification of novel inhibitors of MurB that could overcome the existing resistance. The potential leads were identified from the PubChem library by selecting compounds with high structural similarity to the known inhibitors of MurB. These compounds were then taken through molecular docking studies and were further assessed based on physicochemical and ADMET characteristics. Regarding binding efficiency and drug-likeliness, two hit molecules with PubChem CID:10416900 and CID:14163894 were identified against MurB. Both compounds were closely bound to the MurB active site and did not induce any substantial structural changes in the MurB structure during all-atom molecular dynamics (MD) simulations and MM-PBSA studies. These compounds showed higher potential than the existing inhibitors and stood out as promising leads for the development of therapeutic inhibitors of MurB. The findings of the study, therefore, point to the viability of these compounds in the treatment of bacterial infections, thus enhancing the quality of patient care and disease management. More studies and experimental validation are required to explore their clinical use to the optimum.

Piperlongumine, a natural product from traditional Chinese medicine, shows promising antitumor effects but suffers from high toxicity. In this study, X and Q series Piperlongumine derivatives containing 1, 2, 3-triazole were designed and synthesized using the principle of molecular hybridization. The antitumor activity of these target compounds was evaluated, revealing significant activity compared to piperlongumine across four cancer cell lines. The structure-activity relationship of these compounds was analyzed using 3D-QSAR. Among these derivatives, compound 6Q demonstrated the highest antitumor activity against human chronic myeloid leukemia (K562) cells, with an IC₅₀ value of 0.31 μM, low toxicity to normal cells, and a selectivity index (SI) of 11.2. Further in vitro experiments confirmed that 6Q induced apoptosis in K562 cells by disrupting mitochondrial membrane potential, activating the MAPK signaling pathway, and causing cell cycle arrest in the G2/M phase. These findings underscored the potential of the natural product derivative 6Q as a promising candidate for further development in cancer therapy.