Progress of Computer-Aided Drug Design (CADD) of Proteasome Inhibitors

Li, Meng; Liu, Yunde; Zhu, Yongqiang; Liu, Zhenming

doi:10.2174/156802611798281366

Cited by 7 publications

(5 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the case of a lack of crystallographic structure, partial or complete, homology modeling is the perfect tool to obtain a structure model [73,78,79,80]. Homology modeling’s main goal is to make a prediction of the tridimensional structure of a protein based on the fact that proteins with a homologue amino acid sequence share similar structures (templates) [78,81].…”

Section: Computer-aided Drug Designmentioning

confidence: 99%

Computational Approaches for the Discovery of Human Proteasome Inhibitors: An Overview

et al. 2016

View full text Add to dashboard Cite

Abstract:Proteasome emerged as an important target in recent pharmacological research due to its pivotal role in degrading proteins in the cytoplasm and nucleus of eukaryotic cells, regulating a wide variety of cellular pathways, including cell growth and proliferation, apoptosis, DNA repair, transcription, immune response, and signaling processes. The last two decades witnessed intensive efforts to discover 20S proteasome inhibitors with significant chemical diversity and efficacy. To date, the US FDA approved to market three proteasome inhibitors: bortezomib, carfilzomib, and ixazomib. However new, safer and more efficient drugs are still required. Computer-aided drug discovery has long being used in drug discovery campaigns targeting the human proteasome. The aim of this review is to illustrate selected in silico methods like homology modeling, molecular docking, pharmacophore modeling, virtual screening, and combined methods that have been used in proteasome inhibitors discovery. Applications of these methods to proteasome inhibitors discovery will also be presented and discussed to raise improvements in this particular field.

show abstract

Section: Computer-aided Drug Designmentioning

confidence: 99%

Computational Approaches for the Discovery of Human Proteasome Inhibitors: An Overview

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Computer-aided drug design (CADD) approaches are more efficient and less costly than traditional experimental screening methods, and thus play a critical role in drug discovery processes 21 . Common CADD techniques involve virtual screening and molecular docking through target structures, yet machine learning and deep learning have been incorporated into the drug discovery process to enhance accuracy 22 .…”

Section: Introductionmentioning

confidence: 99%

Development of novel ALOX15 inhibitors combining dual machine learning filtering and fragment substitution optimisation approaches, molecular docking and dynamic simulation methods

Liao,

Cao,

Luo

2024

Journal of Enzyme Inhibition and Medicinal Chemistry

View full text Add to dashboard Cite

The oxidation of unsaturated lipids, facilitated by the enzyme Arachidonic acid 15-lipoxygenase (ALOX15), is an essential element in the development of ferroptosis. This study combined a dual-score exclusion strategy with high-throughput virtual screening, naive Bayesian and recursive partitioning machine learning models, the already established ALOX15 inhibitor i472, and a docking-based fragment substitution optimisation approach to identify potential ALOX15 inhibitors, ultimately leading to the discovery of three FDA-approved drugs that demonstrate optimal inhibitory potential against ALOX15. Through fragment substitution-based optimisation, seven new inhibitor structures have been developed. To evaluate their practicality, ADMET predictions and molecular dynamics simulations were performed. In conclusion, the compounds found in this study provide a novel approach to combat conditions related to ferroptosis-related injury by inhibiting ALOX15.

show abstract

“…This is generally done by altering the key substituent group in the nuclear skeleton of target compounds to increase the binding affinity and specificity to the active site of receptor protein, and improve ADME (absorption, distribution, metabolic and excretion), and changing the lipid-aqueous partition [ 20 , 21 ]. The most important step in drug design is to predict the target of a given compound and investigate the binding affinity for and specificity to the active target, which is achievable through the application of Computer-Aided Drug Design (CADD) techniques, which can improve the efficiency of this process [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Study of Imidazolium Salt Derivatives as PIK3CA Inhibitors Using a Comprehensive in Silico Method

Wang

Liang

et al. 2018

IJMS

View full text Add to dashboard Cite

A series of imidazolium salt derivatives have demonstrated potent antitumor activity in prior research. A comprehensive in silicon method was carried out to identify the putative protein target and detailed structure-activity relationship of the compounds. The Topomer CoMFA and CoMSIA techniques were implemented during the investigation to obtain the relationship between the properties of the substituent group and the contour map of around 77 compounds; the Topomer CoMFA and CoMSIA models were reliable with the statistical data. The protein-protein interaction network was constructed by combining the Pharmmapper platform and STRING database. After generating the sub-network, the phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit α (PIK3CA with protein data bank ID: 3ZIM) was selected as the putative target of imidazolium salt derivatives. A docking study was carried out to correlate interactions of amino acids in protein active pockets surrounded by the ligand with contour maps generated by the structure-activity relationship method. Then the molecular dynamics simulations demonstrated that the imidazolium salt derivatives have potent binding capacity and stability to receptor 3ZIM, and the two ligand-receptor complex was stable in the last 2 ns. Finally, the ligand-based structure-activity relationship and receptor-based docking were combined together to identify the structural requirement of the imidazolium salt derivatives, which will be used to design and synthesize the novel PIK3CA inhibitors.

show abstract

Progress of Computer-Aided Drug Design (CADD) of Proteasome Inhibitors

Cited by 7 publications

References 83 publications

Computational Approaches for the Discovery of Human Proteasome Inhibitors: An Overview

Computational Approaches for the Discovery of Human Proteasome Inhibitors: An Overview

Development of novel ALOX15 inhibitors combining dual machine learning filtering and fragment substitution optimisation approaches, molecular docking and dynamic simulation methods

Study of Imidazolium Salt Derivatives as PIK3CA Inhibitors Using a Comprehensive in Silico Method

Contact Info

Product

Resources

About