1,720,980 research outputs found
Validation of a network-based strategy for the optimization of combinatorial target selection in breast cancer therapy: SiRNA knockdown of network targets in MDA-MB-231 cells as an in vitro model for inhibition of tumor development
No full textInstituto Nacional de Ciência e Tecnologia de Inovação em Doenças Negligenciadas. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Centro de Desenvolvimento Tecnológico em Saúde. Laboratório de Modelagem de Sistemas Biológicos. Rio de Janeiro, RJ, Brasil.Instituto Nacional de Ciência e Tecnologia de Inovação em Doenças Negligenciadas. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Centro de Desenvolvimento Tecnológico em Saúde. Laboratório de Modelagem de Sistemas Biológicos. Rio de Janeiro, RJ, Brasil.University of Alberta. Faculty of Medicine & Dentistry. Department of Oncology. Department of Physics. Edmonton, Alberta, Canada.University of Alberta. Faculty of Medicine & Dentistry. Department of Oncology. Edmonton, Alberta, Canada.Network-based strategies provided by systems biology are attractive tools for cancer therapy. Modulation of cancer networks by anticancer drugs may alter the response of malignant cells and/or drive network re-organization into the inhibition of cancer progression. Previously, using systems biology approach and cancer signaling networks, we identified top-5 highly expressed and connected proteins (HSP90AB1, CSNK2B, TK1, YWHAB and VIM) in the invasive MDA-MB-231 breast cancer cell line. Here, we have knocked down the expression of these proteins, individually or together using siRNAs. The transfected cell lines were assessed for in vitro cell growth, colony formation, migration and invasion relative to control transfected MDA-MB-231, the non-invasive MCF-7 breast carcinoma cell line and the non-tumoral mammary epithelial cell line MCF-10A. The knockdown of the top-5 upregulated connectivity hubs successfully inhibited the in vitro proliferation, colony formation, anchorage independence, migration and invasion in MDA-MB-231 cells; with minimal effects in the control transfected MDA-MB-231 cells or MCF-7 and MCF-10A cells. The in vitro validation of bioinformatics predictions regarding optimized multi-target selection for therapy suggests that protein expression levels together with protein-protein interaction network analysis may provide an optimized combinatorial target selection for a highly effective anti-metastatic precision therapy in triple-negative breast cancer. This approach increases the ability to identify not only druggable hubs as essential targets for cancer survival, but also interactions most susceptible to synergistic drug action. The data provided in this report constitute a preliminary step toward the personalized clinical application of our strategy to optimize the therapeutic use of anti-cancer drugs
Delineating SARS-CoV-2 spike protein and antibodies interaction interfaces via siamese neural networks: A geometric and image-based analysis
Full text linkThe analysis of molecular interactions between antigens and antibodies is crucial for understanding the immunological mechanisms underlying the immune response and for developing effective therapies against various diseases. In this context, the ability to distinguish between protein interfaces that form stable and unstable complexes is a key step in the design of therapeutic antibodies and vaccines. In recent years, deep learning models have provided advanced tools for biomedical research. This work introduces a novel approach to analyzing antibody-antigen interactions, and in particular SARS-CoV-2 spike protein-targeting antibodies, using a Siamese Neural Network specifically designed to integrate depth maps with geometric descriptors of molecular surfaces. By combining these representations, the model captures geometrical shape complementarity to differentiate between stable and unstable protein complexes. The network was trained using image-based representations of antigens and antibodies interfaces enriched with geometric descriptors, using data that include binders and non-binders of the SARS-CoV-2 spike protein receptor-binding domain. The deep learning network operates by comparing feature vectors representing these molecular surfaces; pairs with closer vectors in feature space are associated with stable interactions, while those with more distant vectors suggest instability. Extensive testing with different configurations achieved an accuracy of 90%, demonstrating the robustness of this approach to predict interactions. This innovative integration of artificial intelligence, depth maps and geometric descriptors offers promising applications for designing novel antibodies and vaccines
Molecular Dynamics And Binding Mechanisms Of Volatile Anesthetics Targeting Human Tubulin
Full text linkAnesthesia, despite being the cornerstone of modern surgery, is to this date a biological puzzle. While scientific efforts still have not managed to frame its pharmacology in an exhaustive theoretical framework, microtubules inside neurons are thought to be essential for memory formation and consciousness. The potential ability of volatile anesthetics to alter or dampen the vibrational properties of microtubules justifies the spatiotemporal characterization of the interaction between such molecules and the tubulin dimer through the use of computational molecular modelling
Destabilizing the AXH Tetramer by Mutations: Mechanisms and Potential Antiaggregation Strategies
No full textDesign and optimization of polymeric nanoparticles for the delivery of a novel computationally designed colchicine derivative for cancer application
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
Molecular modeling of subcellular biological filaments: in silico approaches tailored to filament biomechanics
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
Bioinformatics and Machine Learning Approaches for Biomarker Discovery and Predictive Modelling
Full text linkL'abstract è presente nell'allegato / the abstract is in the attachmen
Sustainable biomedical practices in designing and testing new therapies through solvent free drug nanoencapsulation and tissue models
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
Computational Characterization of Small Molecules Binding to the Human XPF Active Site and Virtual Screening to Identify Potential New DNA Repair Inhibitors Targeting the ERCC1-XPF Endonuclease
No full textThe DNA excision repair protein ERCC-1-DNA repair endonuclease XPF (ERCC1-XPF) is a heterodimeric endonuclease essential for the nucleotide excision repair (NER) DNA repair pathway. Although its activity is required to maintain genome integrity in healthy cells, ERCC1-XPF can counteract the effect of DNA-damaging therapies such as platinum-based chemotherapy in cancer cells. Therefore, a promising approach to enhance the effect of these therapies is to combine their use with small molecules, which can inhibit the repair mechanisms in cancer cells. Currently, there are no structures available for the catalytic site of the human ERCC1-XPF, which performs the metal-mediated cleavage of a DNA damaged strand at 5′. We adopted a homology modeling strategy to build a structural model of the human XPF nuclease domain which contained the active site and to extract dominant conformations of the domain using molecular dynamics simulations followed by clustering of the trajectory. We investigated the binding modes of known small molecule inhibitors targeting the active site to build a pharmacophore model. We then performed a virtual screening of the ZINC Is Not Commercial 15 (ZINC15) database to identify new ERCC1-XPF endonuclease inhibitors. Our work provides structural insights regarding the binding mode of small molecules targeting the ERCC1-XPF active site that can be used to rationally optimize such compounds. We also propose a set of new potential DNA repair inhibitors to be considered for combination cancer therapy strategies
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