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- Keyword
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- Anti-HIV activity (1)
- Breast cancer (1)
- Cancer driver mutations (1)
- Convolutional neural networks (1)
- Deep learning (1)
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- Drug discovery (1)
- Ensemble-based machine learning features (1)
- Ester conjugates of TFV (1)
- Gluconacetobacter diazotrophicus (1)
- Lipopolymers (1)
- Machine learning classifiers (1)
- NifA (1)
- Nitrogen fixation (1)
- Nitrogenase (1)
- Phosphonamidate (1)
- Random forest (1)
- SiRNA Delivery (1)
- Sigma-54 activator (1)
- Silencing (1)
- Tenofovir (TFV) (1)
- Tenofovir alafenamide (TAF) (1)
Articles 1 - 4 of 4
Full-Text Articles in Nucleic Acids, Nucleotides, and Nucleosides
Purification And Biochemical Characterization Of The Dna Binding Domain Of The Nitrogenase Transcriptional Activator Nifa From Gluconacetobacter Diazotrophicus, Heidi G. Standke, Lois Kim, Cedric P. Owens
Purification And Biochemical Characterization Of The Dna Binding Domain Of The Nitrogenase Transcriptional Activator Nifa From Gluconacetobacter Diazotrophicus, Heidi G. Standke, Lois Kim, Cedric P. Owens
Biology, Chemistry, and Environmental Sciences Faculty Articles and Research
NifA is a σ54 activator that turns on bacterial nitrogen fixation under reducing conditions and when fixed cellular nitrogen levels are low. The redox sensing mechanism in NifA is poorly understood. In α- and β-proteobacteria, redox sensing involves two pairs of Cys residues within and immediately following the protein’s central AAA+ domain. In this work, we examine if an additional Cys pair that is part of a C(X)5 C motif and located immediately upstream of the DNA binding domain of NifA from the α-proteobacterium Gluconacetobacter diazotrophicus (Gd) is involved in redox sensing. We hypothesize that the …
Synthesis And Evaluation Of Anti-Hiv Activity Of Mono- And Di-Substituted Phosphonamidate Conjugates Of Tenofovir, Aaminat Qureshi, Louise A. Ouattara, Naglaa Salem El-Sayed, Amita Verma, Gustavo F. Doncel, Muhammad Iqbal Choudhary, Hina Siddiqui, Keykavous Parang
Synthesis And Evaluation Of Anti-Hiv Activity Of Mono- And Di-Substituted Phosphonamidate Conjugates Of Tenofovir, Aaminat Qureshi, Louise A. Ouattara, Naglaa Salem El-Sayed, Amita Verma, Gustavo F. Doncel, Muhammad Iqbal Choudhary, Hina Siddiqui, Keykavous Parang
Pharmacy Faculty Articles and Research
The activity of nucleoside and nucleotide analogs as antiviral agents requires phosphorylation by endogenous enzymes. Phosphate-substituted analogs have low bioavailability due to the presence of ionizable negatively-charged groups. To circumvent these limitations, several prodrug approaches have been proposed. Herein, we hypothesized that the conjugation or combination of the lipophilic amide bond with nucleotide-based tenofovir (TFV) (1) could improve the anti-HIV activity. During the current study, the hydroxyl group of phosphonates in TFV was conjugated with the amino group of L-alanine, L-leucine, L-valine, and glycine amino acids and other long fatty ester hydrocarbon chains to synthesize 43 derivatives. Several …
A Systematic Comparison Of Lipopolymers For Sirna Delivery To Multiple Breast Cancer Cell Lines: In Vitro Studies, Hamidreza Montazeri Aliabadi, Remant Bahadur Kc, Emira Bousoik, Ashley Barbarino, Bindu Thapa, Melissa Coyle, Parvin Mahdipoor, Hasan Uludağ
A Systematic Comparison Of Lipopolymers For Sirna Delivery To Multiple Breast Cancer Cell Lines: In Vitro Studies, Hamidreza Montazeri Aliabadi, Remant Bahadur Kc, Emira Bousoik, Ashley Barbarino, Bindu Thapa, Melissa Coyle, Parvin Mahdipoor, Hasan Uludağ
Pharmacy Faculty Articles and Research
Small interfering RNA (siRNA) therapy is a promising approach for treatment of a wide range of cancers, including breast cancers that display variable phenotypic features. To explore the general utility of siRNA therapy to control aberrant expression of genes in breast cancer, we conducted a detailed analysis of siRNA delivery and silencing response in vitro in 6 separate breast cancer cell models (MDA-MB-231, MDA-MB-231-KRas-CRM, MCF-7, AU565, MDA-MB-435 and MDA-MB-468 cells). Using lipopolymers for siRNA complexation and delivery, we found a large variation in siRNA delivery efficiency depending on the specific lipopolymer used for siRNA complexation and delivery. Some lipopolymers were …
Integration Of Random Forest Classifiers And Deep Convolutional Neural Networks For Classification And Biomolecular Modeling Of Cancer Driver Mutations, Steve Agajanian, Odeyemi Oluyemi, Gennady M. Verkhivker
Integration Of Random Forest Classifiers And Deep Convolutional Neural Networks For Classification And Biomolecular Modeling Of Cancer Driver Mutations, Steve Agajanian, Odeyemi Oluyemi, Gennady M. Verkhivker
Mathematics, Physics, and Computer Science Faculty Articles and Research
Development of machine learning solutions for prediction of functional and clinical significance of cancer driver genes and mutations are paramount in modern biomedical research and have gained a significant momentum in a recent decade. In this work, we integrate different machine learning approaches, including tree based methods, random forest and gradient boosted tree (GBT) classifiers along with deep convolutional neural networks (CNN) for prediction of cancer driver mutations in the genomic datasets. The feasibility of CNN in using raw nucleotide sequences for classification of cancer driver mutations was initially explored by employing label encoding, one hot encoding, and embedding to …