scholarly journals The cryo-EM structure of the bd oxidase from M. tuberculosis reveals a unique structural framework and enables rational drug design to combat TB

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Schara Safarian ◽  
Helen K. Opel-Reading ◽  
Di Wu ◽  
Ahmad R. Mehdipour ◽  
Kiel Hards ◽  
...  
Keyword(s):  
Md Simulation ◽  
Treatment Options ◽  
Rational Drug Design ◽  
New Drugs ◽  
Simulation Studies ◽  
Structural Framework ◽  
Loop Domain ◽  
Rational Drug ◽  
Enzyme Complexes ◽  
Limited Oxygen

AbstractNew drugs are urgently needed to combat the global TB epidemic. Targeting simultaneously multiple respiratory enzyme complexes of Mycobacterium tuberculosis is regarded as one of the most effective treatment options to shorten drug administration regimes, and reduce the opportunity for the emergence of drug resistance. During infection and proliferation, the cytochrome bd oxidase plays a crucial role for mycobacterial pathophysiology by maintaining aerobic respiration at limited oxygen concentrations. Here, we present the cryo-EM structure of the cytochrome bd oxidase from M. tuberculosis at 2.5 Å. In conjunction with atomistic molecular dynamics (MD) simulation studies we discovered a previously unknown MK-9-binding site, as well as a unique disulfide bond within the Q-loop domain that defines an inactive conformation of the canonical quinol oxidation site in Actinobacteria. Our detailed insights into the long-sought atomic framework of the cytochrome bd oxidase from M. tuberculosis will form the basis for the design of highly specific drugs to act on this enzyme.

scholarly journals Transaminase Catalysis for Enantiopure Saturated Heterocycles as Potential Drug Scaffolds

Catalysts ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1501
Author(s):  
Ágnes Malta-Lakó ◽  
Fangyi Zhang ◽  
Ricardo Mendonça ◽  
László Poppe
Keyword(s):  
Rational Drug Design ◽  
New Drugs ◽  
Potential Drug ◽  
Reaction Conditions ◽  
Substitution Pattern ◽  
Complex Molecules ◽  
Rational Drug ◽  
Chiral Drug ◽  
Drug Scaffolds ◽  
Reaction Routes

As efforts in rational drug design are driving the pharmaceutical industry towards more complex molecules, the synthesis and production of these new drugs can benefit from new reaction routes. In addition to the introduction of new centers of asymmetry, complexity can be also increased by ring saturation, which also provides improved developability measures. Therefore, in this report, our aim was to develop transaminase (TA)-catalyzed asymmetric synthesis of a new group of potential chiral drug scaffolds comprising a saturated amine heterocycle backbone and an asymmetric primary amine sidechain (55a–g). We screened the Codex® Amine Transaminase Kit of 24 transaminases with the morpholine containing ketone 57a, resulting in one (R)-selective TA and three (S)-selective TAs operating at 100 mM substrate concentration and 25 v/v% isopropylamine (IPA) content. The optimized reaction conditions were than applied for asymmetric transamination of further six ketones (57b–g) containing various amine heterocycles, in which a strong effect of the substitution pattern of the γ-position relative to the substituted N-atom could be observed. Mediated by the most enantiotope selective (S)-TAs in scaled-up process, the (S)-amines [(S)-55a–g] were isolated with moderate-to-excellent yields (47–94%) in enantiopure form (>99% ee).

scholarly journals Theoretical and Experimental Approaches Aimed at Drug Design Targeting Neurodegenerative Diseases

Processes ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 940 ◽  
Author(s):  
Samuel Morales-Navarro ◽  
Luis Prent-Peñaloza ◽  
Yeray A. Rodríguez Núñez ◽  
Laura Sánchez-Aros ◽  
Oscar Forero-Doria ◽  
...  
Keyword(s):  
Drug Design ◽  
Green Chemistry ◽  
Neurodegenerative Diseases ◽  
Maximum Yield ◽  
Public Health Problem ◽  
Rational Drug Design ◽  
New Drugs ◽  
Theoretical Approaches ◽  
Rational Drug ◽  
Medical Chemistry

In recent years, green chemistry has been strengthening, showing how basic and applied sciences advance globally, protecting the environment and human health. A clear example of this evolution is the synergy that now exists between theoretical and computational methods to design new drugs in the most efficient possible way, using the minimum of reagents and obtaining the maximum yield. The development of compounds with potential therapeutic activity against multiple targets associated with neurodegenerative diseases/disorders (NDD) such as Alzheimer’s disease is a hot topic in medical chemistry, where different scientists from various disciplines collaborate to find safe, active, and effective drugs. NDD are a public health problem, affecting mainly the population over 60 years old. To generate significant progress in the pharmacological treatment of NDD, it is necessary to employ different experimental strategies of green chemistry, medical chemistry, and molecular biology, coupled with computational and theoretical approaches such as molecular simulations and chemoinformatics, all framed in the rational drug design targeting NDD. Here, we review how green chemistry and computational approaches have been used to develop new compounds with the potential application against NDD, as well as the challenges and new directions of the drug development multidisciplinary process.

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.

Rational Drug Design Paradigms: The Odyssey for Designing Better Drugs

2015 ◽  
Vol 18 (3) ◽  
pp. 238-256 ◽  
Author(s):  
Tahsin Kellici ◽  
Dimitrios Ntountaniotis ◽  
Eleni Vrontaki ◽  
George Liapakis ◽  
Panagiota Moutevelis-Minakakis ◽  
...  
Keyword(s):  
Drug Design ◽  
Rational Drug Design ◽  
Rational Drug

scholarly journals Structure of the full-length human Pannexin1 channel and insights into its role in pyroptosis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Sensen Zhang ◽  
Baolei Yuan ◽  
Jordy Homing Lam ◽  
Jun Zhou ◽  
Xuan Zhou ◽  
...  
Keyword(s):  
Md Simulation ◽  
Potential Role ◽  
Md Simulations ◽  
Full Length ◽  
Inflammasome Activation ◽  
Simulation Studies ◽  
Cryo Electron Microscopy ◽  
Gating Mechanism ◽  
Atp Efflux

AbstractPannexin1 (PANX1) is a large-pore ATP efflux channel with a broad distribution, which allows the exchange of molecules and ions smaller than 1 kDa between the cytoplasm and extracellular space. In this study, we show that in human macrophages PANX1 expression is upregulated by diverse stimuli that promote pyroptosis, which is reminiscent of the previously reported lipopolysaccharide-induced upregulation of PANX1 during inflammasome activation. To further elucidate the function of PANX1, we propose the full-length human Pannexin1 (hPANX1) model through cryo-electron microscopy (cryo-EM) and molecular dynamics (MD) simulation studies, establishing hPANX1 as a homo-heptamer and revealing that both the N-termini and C-termini protrude deeply into the channel pore funnel. MD simulations also elucidate key energetic features governing the channel that lay a foundation to understand the channel gating mechanism. Structural analyses, functional characterizations, and computational studies support the current hPANX1-MD model, suggesting the potential role of hPANX1 in pyroptosis during immune responses.

scholarly journals Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge

2021 ◽  
Author(s):  
Teresa Danielle Bergazin ◽  
Nicolas Tielker ◽  
Yingying Zhang ◽  
Junjun Mao ◽  
M. R. Gunner ◽  
...  
Keyword(s):  
Free Energy ◽  
Partition Coefficients ◽  
Physical Property ◽  
Rational Drug Design ◽  
Log P ◽  
Prediction Methods ◽  
Research Groups ◽  
Reasonable Accuracy ◽  
Statistical Assessment ◽  
Rational Drug

AbstractThe Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pKa for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pKa challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pKa challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pKa values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pKa prediction methods.

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