scholarly journals Metalloproteomics for molecular target identification of protein-binding anticancer metallodrugs

Metallomics ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1627-1636
Author(s):  
Tasha R. Steel ◽  
Christian G. Hartinger
Keyword(s):  
Protein Binding ◽  
Anticancer Agents ◽  
Target Identification ◽  
Molecular Target ◽  
Protein Targets ◽  
Anticancer Metallodrugs

The development of the metallodrug pull-down as a metalloproteomic technique has enabled the identification of the protein targets of metal-based anticancer agents.

scholarly journals Proteins as possible targets for antitumor metal complexes: biophysical studies of their interactions

2009 ◽  
Author(s):  
Chiara Gabbiani
Keyword(s):  
Mechanism Of Action ◽  
Anticancer Agents ◽  
Protein Adducts ◽  
Functional Information ◽  
X Ray Diffraction ◽  
Protein Targets ◽  
X Ray ◽  
Important Group ◽  
Biophysical Studies ◽  
Anticancer Metallodrugs

There is considerable interest today for the reactions of anticancer metallodrugs with proteins as these interactions might feature processes that are crucial for the biodistribution, the toxicity and even the mechanism of action of this important group of anticancer agents. Valuable structural and functional information on these adducts could be derived from several biophysical studies mainly relying on the application of X-ray diffraction and ESI MS techniques. The structural and functional information achieved on the respective metallodrug–protein adducts allowed us to identify some general trends in the reactivity of anticancer metallodrugs with protein targets.

Emerging Protein Targets for Anticancer Metallodrugs: Inhibition of Thioredoxin Reductase and Cathepsin B by Antitumor Ruthenium(II)−Arene Compounds

2008 ◽  
Vol 51 (21) ◽  
pp. 6773-6781 ◽  
Author(s):  
Angela Casini ◽  
Chiara Gabbiani ◽  
Francesca Sorrentino ◽  
Maria Pia Rigobello ◽  
Alberto Bindoli ◽  
...  
Keyword(s):  
Cathepsin B ◽  
Thioredoxin Reductase ◽  
Protein Targets ◽  
Anticancer Metallodrugs

scholarly journals Drug Target Identification with Machine Learning: How to Choose Negative Examples

2021 ◽  
Vol 22 (10) ◽  
pp. 5118
Author(s):  
Matthieu Najm ◽  
Chloé-Agathe Azencott ◽  
Benoit Playe ◽  
Véronique Stoven
Keyword(s):  
Machine Learning ◽  
Drug Target ◽  
Target Identification ◽  
Target Prediction ◽  
False Positives ◽  
Machine Learning Algorithms ◽  
Statistical Bias ◽  
Protein Targets ◽  
Drug Target Identification ◽  
Approved Drugs

Identification of the protein targets of hit molecules is essential in the drug discovery process. Target prediction with machine learning algorithms can help accelerate this search, limiting the number of required experiments. However, Drug-Target Interactions databases used for training present high statistical bias, leading to a high number of false positives, thus increasing time and cost of experimental validation campaigns. To minimize the number of false positives among predicted targets, we propose a new scheme for choosing negative examples, so that each protein and each drug appears an equal number of times in positive and negative examples. We artificially reproduce the process of target identification for three specific drugs, and more globally for 200 approved drugs. For the detailed three drug examples, and for the larger set of 200 drugs, training with the proposed scheme for the choice of negative examples improved target prediction results: the average number of false positives among the top ranked predicted targets decreased, and overall, the rank of the true targets was improved.Our method corrects databases’ statistical bias and reduces the number of false positive predictions, and therefore the number of useless experiments potentially undertaken.

scholarly journals Nicotinamide phosphoribosyltransferase is a molecular target of potent anticancer agents identified from phenotype-based drug screening

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Daisuke Yamaguchi ◽  
Takamichi Imaizumi ◽  
Kaori Yagi ◽  
Yuichi Matsumoto ◽  
Takayuki Nakashima ◽  
...  
Keyword(s):  
Drug Screening ◽  
Anticancer Agents ◽  
Molecular Target ◽  
Nicotinamide Phosphoribosyltransferase

scholarly journals Identification of Pirin as a Molecular Target of the CCG-1423/CCG- 203971 Series of Anti-Fibrotic and Anti-Metastatic Compounds

2018 ◽  
Author(s):  
Erika M. Lisabeth ◽  
Dylan Kahl ◽  
Indiwari Gopallawa ◽  
Sarah E. Haynes ◽  
Sean A. Misek ◽  
...  
Keyword(s):  
Target Identification ◽  
Molecular Target ◽  
Dermal Fibroblasts ◽  
Titration Calorimetry ◽  
Melanoma Metastasis ◽  
X Ray Crystallography ◽  
Dermal Fibrosis ◽  
Affinity Isolation

AbstractA series of compounds (including CCG-1423 and CCG-203971) discovered through an MRTF/SRF dependent luciferase screen has shown remarkable efficacy in a variety of in vitro and in vivo models, including melanoma metastasis and bleomycin-induced fibrosis. Although these compounds are efficacious, the molecular target is unknown. Here, we describe affinity isolation-based target identification efforts which yielded pirin, an iron-dependent co-transcription factor, as a target of this series of compounds. Using biophysical techniques including isothermal titration calorimetry and X-ray crystallography, we verify that pirin binds these compounds in vitro. We also show with genetic approaches that pirin modulates MRTF-dependent SRE.L Luciferase activation. Finally, using both siRNA and a previously validated pirin inhibitor, we show a role for pirin in TGF-p induced gene expression in primary dermal fibroblasts. A recently developed analog, CCG-257081, which co-crystallizes with pirin, is also effective in the prevention of bleomycin-induced dermal fibrosis.

scholarly journals A Geometric Deep Learning Approach to Predict Binding Conformations of Bioactive Molecules

2021 ◽  
Author(s):  
Oscar Méndez-Lucio ◽  
Mazen Ahmad ◽  
Ehecatl Antonio del Rio-Chanona ◽  
Jörg Kurt Wegner
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Molecular Target ◽  
Bioactive Molecules ◽  
Target Pair ◽  
Learning Approach ◽  
Scoring Functions ◽  
Structure Based Drug Design ◽  
Protein Targets ◽  
Better Than

Understanding the interactions formed between a ligand and its molecular target is key to guide the optimization of molecules. Different experimental and computational methods have been key to understand better these intermolecular interactions. Herein, we report a method based on geometric deep learning that is capable of predicting the binding conformations of ligands to protein targets. Concretely, the model learns a statistical potential based on distance likelihood which is tailor-made for each ligand-target pair. This potential can be coupled with global optimization algorithms to reproduce experimental binding conformations of ligands. We show that the potential based on distance likelihood described in this paper performs similar or better than well-established scoring functions for docking and screening tasks. Overall, this method represents an example of how artificial intelligence can be used to improve structure-based drug design.

scholarly journals Comparative assessment of clinical trials, indications, pharmacokinetic parameters and side effects of approved platinum drugs

Pharmacia ◽  
2022 ◽  
Vol 69 (1) ◽  
pp. 1-7
Author(s):  
Stefka Ivanova
Keyword(s):  
Clinical Trials ◽  
Side Effects ◽  
Observational Study ◽  
Protein Binding ◽  
Anticancer Agents ◽  
Platinum Complexes ◽  
Comparative Assessment ◽  
The Other ◽  
Pharmacokinetic Parameters ◽  
Platinum Drugs

Platinum complexes are among the most commonly applied anticancer agents. The aim of current work is collection, analysing and comparative estimation of clinical trials and pharmacological indications of currently approved for application platinum detivatives: Cisplatin, Carboplatin, Oxaliplatin, Nedaplatin (Japan), Lobaplatin (China), Heptaplatin (North Korea), and Satraplatin. The other aim of the study includes the summarizing of the hystoric data for the stages of the developlement of these drugs, and the comparison of pharmacokimetic parameters, side effecs and the dose-liniting factors of the drugs. The observational study on pharmacokinetic parameters shows that protein binding decreases in order: 95% (Cisplatn); 90% (Oxaliplatin); 50% (Nedaplatin); low (Carboplatin). For every of Cisplatin, Carboplatin, Oxaliplatin have been reported more than 1000 clinical trials; for Lobaplatin, Nedaplatin, Satraplatin - about 10 trials. The differenses in dose-limiting effects are: neuro-, nephro-, ototoxicity (Cisplatin); neurotoxicity (Oxaliplatin); nephrotoxicity (Heptaplatin); myelosuppression: thrombocytopenia, neutropenia, leukopenia (Carboplatin, Nedaplatin, Satraplatin).

Abstract B181: EWS‐FLI1 as a molecular target: Identification of inhibitors for a disordered protein

2009 ◽  
Author(s):  
Julie S. Barber‐Rotenberg ◽  
Yali Kong ◽  
Nilan Schnure ◽  
Sivanesan Dakshanamurthy ◽  
Peter Frazier ◽  
...  
Keyword(s):  
Target Identification ◽  
Molecular Target ◽  
Disordered Protein

scholarly journals Thioredoxin Reductase as a Potential Molecular Target for Anticancer Agents That Induce Oxidative Stress

2004 ◽  
Vol 64 (18) ◽  
pp. 6716-6724 ◽  
Author(s):  
DeeDee K. Smart ◽  
Karen L. Ortiz ◽  
David Mattson ◽  
C. Matthew Bradbury ◽  
Kheem S. Bisht ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Anticancer Agents ◽  
Thioredoxin Reductase ◽  
Molecular Target
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