Background: Drugs incorporating heterocyclic chemical skeletons possess a plethora of therapeutic activities such as anticancer, antimicrobial, antihypertensive, and antipsychiatric effects. It is becoming routine, nowadays, to use cheminformatics and bioinformatics methods to elucidate the mechanism(s) of action of such drugs.
Objective: This study aimed to probe the activity of a recently published series of N1- (anthraquinon-2-yl) amidrazone piperazine derivatives employing computational strategies[1], identify their structural basis of binding to BCR/ABL kinase domain, and explain their anticancer activities in human breast adenocarcinoma (MCF-7) and chronic myelogenous leukemia (K562) cell lines.
Methods: We applied an in silico integrative informatics approach integrating molecular descriptors, docking studies, cheminformatics, and network analysis.
Results: Our results highlighted the possible involvement of the BCR/ABL and DRD2 pathways in the anticancer activity of the studied compounds, and induced fit docking (IFD) indicated that the BCR/ABL kinase domain is a putative drug target. Additionally, high-scoring docking poses identified a unique network of hydrogen bonding with amino acids Y253, K271, E286, V299, L301, T315, M318, I360, R362, V379, and D3810.
Conclusion: Using an integrative informatics approach to characterize our anticancer compounds, we were able to explain the biological differences between synthesized and biologically validated amidrazone piperazine anticancer agents. We were also able to postulate a mechanism of action of this novel group of anticancer agents.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.2174/1573409916666200819113444 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Complex hierarchical structures analysis in single-cell data with Poincaré deep manifold transformation.
Brief Bioinform
November 2024
School of Engineering, Westlake University, No. 600 Dunyu Road, 310030 Zhejiang, P.R. China.
Single-cell RNA sequencing (scRNA-seq) offers remarkable insights into cellular development and differentiation by capturing the gene expression profiles of individual cells. The role of dimensionality reduction and visualization in the interpretation of scRNA-seq data has gained widely acceptance. However, current methods face several challenges, including incomplete structure-preserving strategies and high distortion in embeddings, which fail to effectively model complex cell trajectories with multiple branches.
View Article and Find Full Text PDFscMMAE: masked cross-attention network for single-cell multimodal omics fusion to enhance unimodal omics.
Brief Bioinform
November 2024
Guangdong Provincial Key Laboratory of Mathematical and Neural Dynamical Systems, Great Bay University, No. 16 Daxue Rd, Songshanhu District, Dongguan, Guangdong, 523000, China.
Multimodal omics provide deeper insight into the biological processes and cellular functions, especially transcriptomics and proteomics. Computational methods have been proposed for the integration of single-cell multimodal omics of transcriptomics and proteomics. However, existing methods primarily concentrate on the alignment of different omics, overlooking the unique information inherent in each omics type.
View Article and Find Full Text PDFEvaluation of an Interdisciplinary Educational Program to Foster Learning Health Systems: Education Evaluation.
JMIR Med Educ
January 2025
Centre for Digital Transformation of Health, University of Melbourne, Carlton, Australia.
Background: Learning health systems (LHS) have the potential to use health data in real time through rapid and continuous cycles of data interrogation, implementing insights to practice, feedback, and practice change. However, there is a lack of an appropriately skilled interprofessional informatics workforce that can leverage knowledge to design innovative solutions. Therefore, there is a need to develop tailored professional development training in digital health, to foster skilled interprofessional learning communities in the health care workforce in Australia.
View Article and Find Full Text PDFSomatic mutations and DNA methylation identify a subgroup of poor prognosis within lower-risk myelodysplastic syndromes.
Hemasphere
January 2025
Université Paris Cité, Institut Cochin, INSERM U1016, CNRS UMR8104 Assistance Publique-Hôpitaux de Paris.Centre, Laboratory of Hematology, Hôpital Cochin Paris France.
Lower risk (LR) myelodysplastic syndromes (MDS) are heterogeneous hematopoietic stem and progenitor disorders caused by the accumulation of somatic mutations in various genes including epigenetic regulators that may produce convergent DNA methylation patterns driving specific gene expression profiles. The integration of genomic, epigenomic, and transcriptomic profiling has the potential to spotlight distinct LR-MDS categories on the basis of pathophysiological mechanisms. We performed a comprehensive study of somatic mutations and DNA methylation in a large and clinically well-annotated cohort of treatment-naive patients with LR-MDS at diagnosis from the EUMDS registry (ClinicalTrials.
View Article and Find Full Text PDFConsensus representation of multiple cell-cell graphs from gene signaling pathways for cell type annotation.
BMC Biol
January 2025
Research Office, City University of Hong Kong (Dongguan), Dongguan, 523000, China.
Background: Recent advancements in single-cell RNA sequencing have greatly expanded our knowledge of the heterogeneous nature of tissues. However, robust and accurate cell type annotation continues to be a major challenge, hindered by issues such as marker specificity, batch effects, and a lack of comprehensive spatial and interaction data. Traditional annotation methods often fail to adequately address the complexity of cellular interactions and gene regulatory networks.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!