scholarly journals Small Molecule Efflux Pump Inhibitors in Mycobacterium tuberculosis: A Rational Drug Design Perspective

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Erika Kapp ◽  
Sarel F. Malan ◽  
Jacques Joubert ◽  
Samantha L. Sampson
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Drug Design ◽  
Small Molecule ◽  
Efflux Pump ◽  
Rational Drug Design ◽  
Rational Drug ◽  
Efflux Pump Inhibitors ◽  
Design Perspective

Enzyme regulation by biological disulfides

1989 ◽  
Vol 9 (5) ◽  
pp. 593-604 ◽  
Author(s):  
Raul N. Ondarza
Keyword(s):  
Drug Design ◽  
Small Molecule ◽  
Regulatory Mechanism ◽  
Enzyme Regulation ◽  
Rational Drug Design ◽  
Host Cells ◽  
Disulfide Exchange ◽  
Rational Drug

More than a dozen enzymes have been found to be activated or inhibited in vitro by disulfide-exchange between the protein and small-molecule disulfides. Accordingly, thiol/disulfide ratio changes in vivo may be of great importance in the regulation of cellular metabolism. An awareness of this regulatory mechanism in both host cells and parasites, coupled with information on the presence or absence of key enzymes, may lead to rational drug design against certain diseases involving thiol intermediates, including trypanosomiasis.

Applications of Molecular Simulation in the Discovery of Antituberculosis Drugs: A Review

2019 ◽  
Vol 26 (9) ◽  
pp. 648-663 ◽  
Author(s):  
Jian-Ping Hu ◽  
Zhi-Xiang Wu ◽  
Tao Xie ◽  
Xin-Yu Liu ◽  
Xiao Yan ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Mycobacterium Tuberculosis ◽  
Drug Design ◽  
Molecular Simulation ◽  
Rational Drug Design ◽  
Dynamics Simulation ◽  
Simulation Methods ◽  
Antituberculosis Drugs ◽  
Rational Drug ◽  
Low Toxic

:After decades of efforts, tuberculosis has been well controlled in most places. The existing drugs are no longer sufficient for the treatment of drug-resistant Mycobacterium tuberculosis due to significant toxicity and selective pressure, especially for XDR-TB. In order to accelerate the development of high-efficiency, low-toxic antituberculosis drugs, it is particularly important to use Computer Aided Drug Design (CADD) for rational drug design. Here, we systematically reviewed the specific role of molecular simulation in the discovery of new antituberculosis drugs.:The purpose of this review is to overview current applications of molecular simulation methods in the discovery of antituberculosis drugs. Furthermore, the unique advantages of molecular simulation was discussed in revealing the mechanism of drug resistance.:The comprehensive use of different molecular simulation methods will help reveal the mechanism of drug resistance and improve the efficiency of rational drug design.:With the help of molecular simulation methods such as QM/MM method, the mechanisms of biochemical reactions catalyzed by enzymes at atomic level in Mycobacterium tuberculosis has been deeply analyzed. QSAR and virtual screening both accelerate the development of highefficiency, low-toxic potential antituberculosis drugs. Improving the accuracy of existing algorithms and developing more efficient new methods for CADD will always be a hot topic in the future. It is of great value to utilize molecular dynamics simulation to investigate complex systems that cannot be studied in experiments, especially for drug resistance of Mycobacterium tuberculosis.

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.

Rational Drug Design Approach of Receptor Tyrosine Kinase Type III Inhibitors

2020 ◽  
Vol 26 (42) ◽  
pp. 7623-7640 ◽  
Author(s):  
Cheolhee Kim ◽  
Eunae Kim
Keyword(s):  
Drug Design ◽  
Tyrosine Kinase ◽  
Receptor Tyrosine Kinase ◽  
Synergistic Effects ◽  
Rational Drug Design ◽  
Computational Techniques ◽  
Biological Targets ◽  
Type Iii ◽  
Theoretical Approaches ◽  
Rational Drug

: Rational drug design is accomplished through the complementary use of structural biology and computational biology of biological macromolecules involved in disease pathology. Most of the known theoretical approaches for drug design are based on knowledge of the biological targets to which the drug binds. This approach can be used to design drug molecules that restore the balance of the signaling pathway by inhibiting or stimulating biological targets by molecular modeling procedures as well as by molecular dynamics simulations. Type III receptor tyrosine kinase affects most of the fundamental cellular processes including cell cycle, cell migration, cell metabolism, and survival, as well as cell proliferation and differentiation. Many inhibitors of successful rational drug design show that some computational techniques can be combined to achieve synergistic effects.

Recent Advances in the Rational Drug Design Based on Multi-target Ligands

2020 ◽  
Vol 27 (28) ◽  
pp. 4720-4740 ◽  
Author(s):  
Ting Yang ◽  
Xin Sui ◽  
Bing Yu ◽  
Youqing Shen ◽  
Hailin Cong
Keyword(s):  
Drug Resistance ◽  
Clinical Practice ◽  
Side Effects ◽  
Drug Design ◽  
Rational Drug Design ◽  
Health Conditions ◽  
Pharmacokinetic Parameters ◽  
Single Target ◽  
Rational Drug ◽  
Huge Challenge

Multi-target drugs have gained considerable attention in the last decade owing to their advantages in the treatment of complex diseases and health conditions linked to drug resistance. Single-target drugs, although highly selective, may not necessarily have better efficacy or fewer side effects. Therefore, more attention is being paid to developing drugs that work on multiple targets at the same time, but developing such drugs is a huge challenge for medicinal chemists. Each target must have sufficient activity and have sufficiently characterized pharmacokinetic parameters. Multi-target drugs, which have long been known and effectively used in clinical practice, are briefly discussed in the present article. In addition, in this review, we will discuss the possible applications of multi-target ligands to guide the repositioning of prospective drugs.

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