Molecular Diversity最新文献

{"title":"Design, synthesis, antifungal, and antibacterial evaluation of ferulic acid derivatives bearing amide moiety.","authors":"Qiang Fei, Yanbi Luo, Haijiang Chen, Wenneng Wu, Su Xu","doi":"10.1007/s11030-024-11076-4","DOIUrl":"https://doi.org/10.1007/s11030-024-11076-4","url":null,"abstract":"<p><p>Natural compounds' derivatives as lead structures could effectively solve plant disease problems. In this article, amide compounds and amide ester compounds were synthetized through ferulic acid as the parent nucleus structure, and their biological activities in vitro and in vivo were evaluated. Compound 1q was screened out as the one with the best activity performance toward Xanthomonas axonopodis pv. citri (Xac), which displayed the inhibition rate of 100% and the EC₅₀ as low as 4.56 μg/mL. The results of in vivo experiments on citrus leaves infected with Xac showed that compound 1q had a protective efficacy of 60.98% and a curative efficacy of 26.56%. The mechanism of action as well as molecular docking was previously studied using extracellular polysaccharide (EPS) content, bacterial membrane permeability, and scanning electron microscopy (SEM) observations. Experimental results show that compound 1q can become an antibacterial agent for preventing and managing plant diseases.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142891320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pyridazine and pyridazinone compounds in crops protection: a review.","authors":"Xining Ma, Ping Sun, Jiaxin Wang, Xinyu Huang, Jian Wu","doi":"10.1007/s11030-024-11083-5","DOIUrl":"https://doi.org/10.1007/s11030-024-11083-5","url":null,"abstract":"<p><p>Pyridazine and pyridazinone belong to the same group of six-membered heterocyclic compounds, and both structurally feature two adjacent nitrogen atoms. Pyridazine and pyridazinone derivatives are frequently used as core structures in the development of new green agrochemicals due to their high activity and environmental friendliness, attracting significant attention from researchers in recent years. Over the past 20 years, significant developments have occurred in the field of pyridazine and pyridazinone derivatives, which exhibit insecticidal, fungicidal, herbicidal, antiviral, and plant growth regulating activities. Hence, summarizing the process of creating novel molecules with pyridazine and pyridazinone structures through design concepts, understanding structure-activity relationships, and mechanisms of action is an important undertaking. This review aims to provide a comprehensive overview of these advancements, shedding light on the discovery and mechanism of action of novel pesticides in the pyridazine and pyridazinone categories.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142891323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HDAC3_VS_assistant: cheminformatics-driven discovery of histone deacetylase 3 inhibitors.","authors":"Oleg V Tinkov, Veniamin Y Grigorev","doi":"10.1007/s11030-024-11066-6","DOIUrl":"https://doi.org/10.1007/s11030-024-11066-6","url":null,"abstract":"<p><p>Histone deacetylase 3 (HDAC3) inhibitors keep significant therapeutic promise for treating oncological, neurodegenerative, and inflammatory diseases. In this work, we developed robust QSAR regression models for HDAC3 inhibitory activity and acute toxicity (LD₅₀, intravenous administration in mice). A total of 1751 compounds were curated for HDAC3 activity, and 15,068 for toxicity. The models employed molecular descriptors such as Morgan fingerprints, MACCS-166 keys, and Klekota-Roth, PubChem fingerprints integrated with machine learning algorithms including random forest, gradient boosting regressor, and support vector machine. The HDAC3 QSAR models achieved Q²_test values of up to 0.76 and RMSE values as low as 0.58, while toxicity models attained Q²_test values of 0.63 and RMSE values down to 0.41, with applicability domain (AD) coverage exceeding 68%. Internal validation by fivefold cross-validation (Q²cv = 0.70 for HDAC3 and 0.60 for toxicity) and y-randomization confirmed model reliability. Shapley additive explanation (SHAP) was also used to explain the influence of modeling features on model prediction results. The most predictive QSAR models are integrated into the developed HDAC3_VS_assistant application, which is freely available at https://hdac3-vs-assistant-v2.streamlit.app/ . Virtual screening conducted using the HDAC3_VS_assistant web application allowed us to reveal a number of potential inhibitors, and the nature of their bonds with the active HDAC3 site was additionally investigated by molecular docking.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A 4D tensor-enhanced multi-dimensional convolutional neural network for accurate prediction of protein-ligand binding affinity.","authors":"Dingfang Huang, Yu Wang, Yiming Sun, Wenhao Ji, Qing Zhang, Yunya Jiang, Haodi Qiu, Haichun Liu, Tao Lu, Xian Wei, Yadong Chen, Yanmin Zhang","doi":"10.1007/s11030-024-11044-y","DOIUrl":"https://doi.org/10.1007/s11030-024-11044-y","url":null,"abstract":"<p><p>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 .</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of STAT3 phosphorylation inhibitors using generative deep learning, virtual screening, molecular dynamics simulations, and biological evaluation for non-small cell lung cancer therapy.","authors":"Weiji Cai, Beier Jiang, Yichen Yin, Lei Ma, Tao Li, Jing Chen","doi":"10.1007/s11030-024-11067-5","DOIUrl":"https://doi.org/10.1007/s11030-024-11067-5","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the role of pomalidomide in androgen-dependent prostate cancer: a computational analysis.","authors":"Shivani Pathak, Vipendra Kumar Singh, Prashant Kumar Gupta, Arun Kumar Mahapatra, Rajanish Giri, Rashmi Sahu, Rohit Sharma, Neha Garg","doi":"10.1007/s11030-024-11081-7","DOIUrl":"https://doi.org/10.1007/s11030-024-11081-7","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142871035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive computer-based assessment of Deacetylnomilin as an inhibitor for antibiotic-resistant genes identified from the whole genome sequence of the multidrug-resistant Enterobacter cloacae isolate 1382.","authors":"Shubhi Singh, Sahithya Selvakumar, Priya Swaminathan","doi":"10.1007/s11030-024-11077-3","DOIUrl":"https://doi.org/10.1007/s11030-024-11077-3","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Immunoinformatic based designing of highly immunogenic multi-epitope subunit vaccines to stimulate an adaptive immune response against Junin virus.","authors":"Mohammed Alissa, Abdullah Alghamdi, Suad A Alghamdi, Muhammad Suleman","doi":"10.1007/s11030-024-11082-6","DOIUrl":"https://doi.org/10.1007/s11030-024-11082-6","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142845482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential VEGFR2 inhibitors for managing metastatic cervical cancer: insights from molecular dynamics and free energy landscape studies.","authors":"Ahmed Alobaida, Amr S Abouzied, A Taslim Ahmed, Bader Huwaimel","doi":"10.1007/s11030-024-11080-8","DOIUrl":"https://doi.org/10.1007/s11030-024-11080-8","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142845483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural aspects of HIV-1 integrase inhibitors: SAR studies and synthetic strategies.","authors":"Pallavi Barik, Shankar Gupta, Gurpreet Singh, Sanjay Kumar Bharti, Vivek Asati","doi":"10.1007/s11030-024-11068-4","DOIUrl":"https://doi.org/10.1007/s11030-024-11068-4","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142845484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}