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.

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

scholarly journals Advances toward structure-based drug discovery for inflammasome targets

2021 ◽  
Vol 219 (1) ◽  
Author(s):  
Li Wang ◽  
Michael A. Crackower ◽  
Hao Wu
Keyword(s):  
Drug Discovery ◽  
High Resolution ◽  
Drug Design ◽  
Structural Biology ◽  
Drug Targets ◽  
Unmet Need ◽  
Structure Based Drug Design ◽  
Important Family ◽  
Recent Advances

Inflammasome proteins play an important role in many diseases of high unmet need, making them attractive drug targets. However, drug discovery for inflammasome proteins has been challenging in part due to the difficulty in solving high-resolution structures using cryo-EM or crystallography. Recent advances in the structural biology of NLRP3 and NLRP1 have provided the first set of data that proves a promise for structure-based drug design for this important family of targets.

Recent Trends in Drug Design and Discovery

2020 ◽  
Vol 20 (19) ◽  
pp. 1761-1770
Author(s):  
Devadasan Velmurugan ◽  
R. Pachaiappan ◽  
Chandrasekaran Ramakrishnan
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Molecular Modeling ◽  
Drug Design ◽  
Computational Methods ◽  
Data Analytics ◽  
Drug Targets ◽  
Structural Data ◽  
Target Protein ◽  
Structure Based Drug Design

Introduction: Structure-based drug design is a wide area of identification of selective inhibitors of a target of interest. From the time of the availability of three dimensional structure of the drug targets, mostly the proteins, many computational methods had emerged to address the challenges associated with drug design process. Particularly, drug-likeness, druggability of the target protein, specificity, off-target binding, etc., are the important factors to determine the efficacy of new chemical inhibitors. Objective: The aim of the present research was to improve the drug design strategies in field of design of novel inhibitors with respect to specific target protein in disease pathology. Recent statistical machine learning methods applied for structural and chemical data analysis had been elaborated in current drug design field. Methods: As the size of the biological data shows a continuous growth, new computational algorithms and analytical methods are being developed with different objectives. It covers a wide area, from protein structure prediction to drug toxicity prediction. Moreover, the computational methods are available to analyze the structural data of varying types and sizes of which, most of the semi-empirical force field and quantum mechanics based molecular modeling methods showed a proven accuracy towards analysing small structural data sets while statistics based methods such as machine learning, QSAR and other specific data analytics methods are robust for large scale data analysis. Results: In this present study, the background has been reviewed for new drug lead development with respect specific drug targets of interest. Overall approach of both the extreme methods were also used to demonstrate with the plausible outcome. Conclusion: In this chapter, we focus on the recent developments in the structure-based drug design using advanced molecular modeling techniques in conjunction with machine learning and other data analytics methods. Natural products based drug discovery is also discussed.

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.

scholarly journals An Updated Review of Computer-Aided Drug Design and Its Application to COVID-19

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Arun Bahadur Gurung ◽  
Mohammad Ajmal Ali ◽  
Joongku Lee ◽  
Mohammad Abul Farah ◽  
Khalid Mashay Al-Anazi
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Drug Targets ◽  
Pharmacophore Modeling ◽  
Review Article ◽  
Drug Candidate ◽  
Computational Techniques ◽  
Computer Aided Drug Design ◽  
Structure Based Drug Design ◽  
Computer Aided

The recent outbreak of the deadly coronavirus disease 19 (COVID-19) pandemic poses serious health concerns around the world. The lack of approved drugs or vaccines continues to be a challenge and further necessitates the discovery of new therapeutic molecules. Computer-aided drug design has helped to expedite the drug discovery and development process by minimizing the cost and time. In this review article, we highlight two important categories of computer-aided drug design (CADD), viz., the ligand-based as well as structured-based drug discovery. Various molecular modeling techniques involved in structure-based drug design are molecular docking and molecular dynamic simulation, whereas ligand-based drug design includes pharmacophore modeling, quantitative structure-activity relationship (QSARs), and artificial intelligence (AI). We have briefly discussed the significance of computer-aided drug design in the context of COVID-19 and how the researchers continue to rely on these computational techniques in the rapid identification of promising drug candidate molecules against various drug targets implicated in the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The structural elucidation of pharmacological drug targets and the discovery of preclinical drug candidate molecules have accelerated both structure-based as well as ligand-based drug design. This review article will help the clinicians and researchers to exploit the immense potential of computer-aided drug design in designing and identification of drug molecules and thereby helping in the management of fatal disease.

scholarly journals Abstract P-25: High-Resolution Cryo-Electron Microscopy Structure of the Staphylococcus Aureus Ribosome Brings to Light New Possible Drug Targets

2021 ◽  
Vol 11 (Suppl_1) ◽  
pp. S22-S23
Author(s):  
Alexander Golubev ◽  
Bulat Fatkhullin ◽  
Iskander Khusainov ◽  
Shamil Validov ◽  
Marat Yusupov ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Protein Synthesis ◽  
Drug Discovery ◽  
Drug Design ◽  
Messenger Rna ◽  
Drug Targets ◽  
Structural Data ◽  
New Drugs ◽  
Fine Tuning ◽  
Structure Based Drug Design

Background: Antibiotic resistance is a growing worldwide problem. One of the major resistant bacterial pathogens is Staphylococcus aureus, which became a burden of healthcare systems around the world. To overcome the issue, more drug discovery studies are needed. One of the main antibiotic targets is a ribosome – the central hub of protein synthesis. Structural data of the ribosome and its features are a crucial milestone for the effective development of new drugs, especially using structure-based drug design approaches. Apart from many small structural features, ribosome possesses rRNA modifications that play a role in the fine-tuning of protein synthesis. Detailed species-specific structural data of the S. aureus ribosome is also a useful model for understanding the resistance mechanisms. This information could help with the design of new antibiotics and the upgrading of old ones. The data on S. aureus ribosomal RNA modifications and corresponding modification enzymes are very limited. Our aim was to improve the current models of the S. aureus ribosome by determining its structure with functional ligands at a much higher resolution - thereby creating a foundation for structure-based drug design experiments and research of new drug targets. Methods: The S. aureus ribosome complex consists of three components: ribosome, fMet-tRNAfMet, mRNA and 70S ribosome. The complex from purified components was formed in vitro and applied to cryo-EM grids. Data was collected at Titan Krios with Gatan K2 detector (IGBMC, France). The data was processed and modeled in Relion 2.1, Chimera, Coot, and Phenix. Results: We determined the cryo-EM reconstruction at 3.2 Å resolution of the S. aureus ribosome with P-site tRNA, messenger RNA. Based on the experimental map and existing bioinformatic data, we at the first time identified and assigned 10 modifications of S. aureus rRNA. We analyzed the positions of rRNA modifications and their possible functions. Conclusion: In this study, we describe our structure of S. aureus ribosome with functional ligands. The present model is the highest resolution and most precise that is available at the moment. We propose a set of methyltransferases as targets for future drug discovery studies. The proposed methyltransferases and corresponding modifications may play an important role in protein synthesis and its regulation.

scholarly journals Molecular drug targets and structure based drug design: A holistic approach

2006 ◽  
Vol 1 (8) ◽  
pp. 314-320 ◽  
Author(s):  
Shailza Singh ◽  
Balwant Kumar Malik ◽  
Durlabh Kumar Sharma
Keyword(s):  
Drug Design ◽  
Drug Targets ◽  
Holistic Approach ◽  
Structure Based Drug Design

scholarly journals Specificity of Molecular Fragments Binding to S100B versus S100A1 as Identified by NMR and Site Identification by Ligand Competitive Saturation (SILCS)

Molecules ◽  
2021 ◽  
Vol 26 (2) ◽  
pp. 381
Author(s):  
Brianna D. Young ◽  
Wenbo Yu ◽  
Darex J. Vera Rodríguez ◽  
Kristen M. Varney ◽  
Alexander D. MacKerell ◽  
...  
Keyword(s):  
Malignant Melanoma ◽  
Drug Design ◽  
Drug Targets ◽  
P53 Protein ◽  
S100 Protein ◽  
Data Sets ◽  
S100 Family ◽  
Promising Therapeutic Target ◽  
Site Identification ◽  
Insight Into

S100B, a biomarker of malignant melanoma, interacts with the p53 protein and diminishes its tumor suppressor function, which makes this S100 family member a promising therapeutic target for treating malignant melanoma. However, it is a challenge to design inhibitors that are specific for S100B in melanoma versus other S100-family members that are important for normal cellular activities. For example, S100A1 is most similar in sequence and structure to S100B, and this S100 protein is important for normal skeletal and cardiac muscle function. Therefore, a combination of NMR and computer aided drug design (CADD) was used to initiate the design of specific S100B inhibitors. Fragment-based screening by NMR, also termed “SAR by NMR,” is a well-established method, and was used to examine spectral perturbations in 2D [1H, 15N]-HSQC spectra of Ca2+-bound S100B and Ca2+-bound S100A1, side-by-side, and under identical conditions for comparison. Of the 1000 compounds screened, two were found to be specific for binding Ca2+-bound S100A1 and four were found to be specific for Ca2+-bound S100B, respectively. The NMR spectral perturbations observed in these six data sets were then used to model how each of these small molecule fragments showed specificity for one S100 versus the other using a CADD approach termed Site Identification by Ligand Competitive Saturation (SILCS). In summary, the combination of NMR and computational approaches provided insight into how S100A1 versus S100B bind small molecules specifically, which will enable improved drug design efforts to inhibit elevated S100B in melanoma. Such a fragment-based approach can be used generally to initiate the design of specific inhibitors for other highly homologous drug targets.

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