Structural Insight into Maternal Embryonic Leucine Zipper Kinase (MELK) Conformation and Inhibition toward Structure-Based Drug Design

Biochemistry ◽  
2013 ◽  
Vol 52 (37) ◽  
pp. 6380-6387 ◽  
Author(s):  
Giulia Canevari ◽  
Stefania Re Depaolini ◽  
Ulisse Cucchi ◽  
Jay A. Bertrand ◽  
Elena Casale ◽  
...  
Keyword(s):  
Drug Design ◽  
Leucine Zipper ◽  
Structure Based Drug Design ◽  
Structural Insight ◽  
Insight Into

Sequence diversity and ligand-induced structural rearrangements of viper hyaluronidase

2016 ◽  
Vol 12 (4) ◽  
pp. 1128-1138 ◽  
Author(s):  
A. Christian Bharathi ◽  
Pradeep Kumar Yadav ◽  
B. Syed Ibrahim
Keyword(s):  
Drug Design ◽  
Cancer Treatment ◽  
Drug Targets ◽  
Structural Changes ◽  
Sequence Diversity ◽  
Structural Rearrangements ◽  
Structure Based Drug Design ◽  
Future Drug ◽  
Insight Into

The study focuses on ligand-induced structural changes of viper hyaluronidase and also provides insight into structure-based drug design for eukaryotic hyaluronidases, which could be future drug targets in cancer treatment, and venom spreading.

scholarly journals Metabolic N-Dealkylation and N-Oxidation as Elucidators of the Role of Alkylamino Moieties in Drugs Acting at Various Receptors

Molecules ◽  
2021 ◽  
Vol 26 (7) ◽  
pp. 1917
Author(s):  
Babiker M. EH-Haj
Keyword(s):  
Molecular Docking ◽  
Drug Design ◽  
Physicochemical Properties ◽  
Open Chain ◽  
Structure Based Drug Design ◽  
Receptor Selectivity ◽  
Drug Molecules ◽  
Metabolic Reactions ◽  
Insight Into

Metabolic reactions that occur at alkylamino moieties may provide insight into the roles of these moieties when they are parts of drug molecules that act at different receptors. N-dealkylation of N,N-dialkylamino moieties has been associated with retaining, attenuation or loss of pharmacologic activities of metabolites compared to their parent drugs. Further, N-dealkylation has resulted in clinically used drugs, activation of prodrugs, change of receptor selectivity, and providing potential for developing fully-fledged drugs. While both secondary and tertiary alkylamino moieties (open chain aliphatic or heterocyclic) are metabolized by CYP450 isozymes oxidative N-dealkylation, only tertiary alkylamino moieties are subject to metabolic N-oxidation by Flavin-containing monooxygenase (FMO) to give N-oxide products. In this review, two aspects will be examined after surveying the metabolism of representative alkylamino-moieties-containing drugs that act at various receptors (i) the pharmacologic activities and relevant physicochemical properties (basicity and polarity) of the metabolites with respect to their parent drugs and (ii) the role of alkylamino moieties on the molecular docking of drugs in receptors. Such information is illuminative in structure-based drug design considering that fully-fledged metabolite drugs and metabolite prodrugs have been, respectively, developed from N-desalkyl and N-oxide metabolites.

Insight into the selective inhibition of JNK family members through structure-based drug design

MedChemComm ◽  
2016 ◽  
Vol 7 (4) ◽  
pp. 686-692
Author(s):  
A. Messoussi ◽  
G. Chevé ◽  
K. Bougrin ◽  
A. Yasri
Keyword(s):  
Protein Kinase ◽  
Drug Design ◽  
Neurodegenerative Disorders ◽  
Family Members ◽  
Selective Inhibition ◽  
Mitogen Activated Protein Kinase ◽  
Biological Processes ◽  
Structure Based Drug Design ◽  
Mitogen Activated Protein ◽  
Insight Into

The c-Jun N-terminal kinase (JNK) family, which comprises JNK1, JNK2 and JNK3, belongs to the mitogen-activated protein kinase (MAPK) superfamily, whose members regulate myriad biological processes, including those implicated in tumorigenesis and neurodegenerative disorders.

Improving the Accuracy of Protein-Ligand Binding Affinity Prediction by Deep Learning Models: Benchmark and Model

2019 ◽  
Author(s):  
Mohammad Rezaei ◽  
Yanjun Li ◽  
Xiaolin Li ◽  
Chenglong Li
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
Binding Affinity ◽  
Benchmark Dataset ◽  
Rational Drug Design ◽  
Learning Models ◽  
Structure Based Drug Design ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Rational Drug

<b>Introduction:</b> The ability to discriminate among ligands binding to the same protein target in terms of their relative binding affinity lies at the heart of structure-based drug design. Any improvement in the accuracy and reliability of binding affinity prediction methods decreases the discrepancy between experimental and computational results.<br><b>Objectives:</b> The primary objectives were to find the most relevant features affecting binding affinity prediction, least use of manual feature engineering, and improving the reliability of binding affinity prediction using efficient deep learning models by tuning the model hyperparameters.<br><b>Methods:</b> The binding site of target proteins was represented as a grid box around their bound ligand. Both binary and distance-dependent occupancies were examined for how an atom affects its neighbor voxels in this grid. A combination of different features including ANOLEA, ligand elements, and Arpeggio atom types were used to represent the input. An efficient convolutional neural network (CNN) architecture, DeepAtom, was developed, trained and tested on the PDBbind v2016 dataset. Additionally an extended benchmark dataset was compiled to train and evaluate the models.<br><b>Results: </b>The best DeepAtom model showed an improved accuracy in the binding affinity prediction on PDBbind core subset (Pearson’s R=0.83) and is better than the recent state-of-the-art models in this field. In addition when the DeepAtom model was trained on our proposed benchmark dataset, it yields higher correlation compared to the baseline which confirms the value of our model.<br><b>Conclusions:</b> The promising results for the predicted binding affinities is expected to pave the way for embedding deep learning models in virtual screening and rational drug design fields.

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