Comparative molecular docking analysis for analyzing the inhibitory effect of Anakinra and Ustekinumab against IL17F

Nisar, Haseeb; Attique, Syed Awais; Javaid, Anum; Ain, Qurat Ul; Butt, Fatima; Zaid, Muhammad; Shahid, Salman; Nasir, Muhammad Hassan; Sadaf, Saima

doi:10.1080/07391102.2023.2173299

Cited by 7 publications

(3 citation statements)

References 41 publications

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“…Further, the evaluated 3D models of virB4 , virB8 and virB9 proteins were then subjected to molecular docking analysis. The molecular operating environment (MOE) was incorporated which has also been widely employed in already reported studies [ 52 – 54 ]. We particularly selected the induce-fit model to perform this docking analysis.…”

Section: Discussionmentioning

confidence: 99%

Assessing the efficacy of cinnamon compounds against H. pylori through molecular docking, MD Simulations and ADMET analyses

Sarwar,

Zahra,

Awan

et al. 2024

PLoS ONE

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Antibiotics are the drugs that are used for the management of microbial diseases. However, these conventional synthetic drugs can harmfully affect the human health. Since phytochemicals are extracted from natural sources and, are hence relatively safer for human health, they are the enticing alternatives in this regard. Cinnamon is also one of those plants which is being employed as herbal medication for centuries against certain microbial infections due its significant therapeutic effectiveness. A well-known pathogenic bacterium called H. pylori causes a wide range of illnesses in human body. This pathogen’s pathogenicity is determined by certain virulent proteins. In this study, some of such proteins, which included virB4, virB8, and virB9 were selected to evaluate the therapeutic efficiency of cinnamon compounds. These proteins were identified in different isolates of H. pylori. The structural modelling of all these proteins were performed initially in order to proceed them for molecular docking analysis. While, the docking studies illustrated that one of the cinnamon compounds, cinnamyl acetate, showed significant binding interactions with virB4 and virB9. However, benzyl benzoate which is another cinnamon compound, docked well with virB8. Afterwards, the MD simulations were incorporated to explore the interaction motions and structural stability of all the docked complexes. In this regard, the resultant maps of Bfactor, eigenvalues and elastic network model, among other factors ensured the structural stabilities of all the respective complexes. After these crucial estimations, benzyl benzoate and cinnamyl acetate underwent the ADMET investigation to assess their pharmacokinetic characteristics. SwissADME and ADMETLab 2.0 server were employed for this investigation. The compiled findings these servers revealed that both, benzyl benzoate and cinnamyl acetate, exhibited a significant level of pharmacokinetic and drug-likeness conformity.

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Section: Discussionmentioning

confidence: 99%

Assessing the efficacy of cinnamon compounds against H. pylori through molecular docking, MD Simulations and ADMET analyses

Sarwar,

Zahra,

Awan

et al. 2024

PLoS ONE

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“…Differential expressed genes (DEGs) serve as signatures in the Cmap reference database[24, 26], with DEGs or DEGs-based approaches being the most widely used methods for disease-specific signatures[4, 29–34]. Additionally, other methods exist, such as utilizing event-free survival via Cox regression to identify prognosis-related gene signatures[35], exploring disease-related genes through genome-wide association studies (GWAS)[36], conducting transcriptome-wide association studies (TWAS)[37], and using hub genes in protein-protein interaction (PPI) networks as queried signatures [38]. Functional annotations databases, text mining, and other methods are also applied to prioritize disease risk genes and generate signatures[39, 40].…”

Section: Introductionmentioning

confidence: 99%

Tumor relapse-free survival prognosis related consistency between cancer tissue and adjacent normal tissue in drug repurposing for solid tumor via connectivity map

Hao,

Li,

Qiao

et al. 2024

Preprint

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Traditional drug discovery encounters challenges, including high costs, time-intensive processes, and inherent risks. Drug repurposing emerges as a compelling alternative strategy to identify new indications for investigational or approved drugs, circumventing these obstacles. Among the various drug repurposing methods, the Disease-specific Signature-based Connectivity Map (Cmap) approach is widely utilized. However, the commonly employed method for constructing disease-specific signatures, known as Differentially Expressed Genes (DEG), faces issues related to inconsistencies between dysregulated genes and the prognosis of genes in tumor tissue, as well as discrepancies in prognosis genes between tumor and normal tissues.In this study, we propose a novel approach, Prognosis Consistency Scoring (PCS), aimed at addressing these inconsistencies. PCS measures the consistency of gene prognosis between tumor and normal tissues by combining the Recurrence-Free Survival (RFS) prognosis power of genes in both contexts. Disease-specific signatures are then constructed based on PCS, and drug repurposing is performed using the Cmap and Lincs Unified Environment (CLUE). Validation of predicted drugs is conducted using data from DrugBank and ClinicalTrials databases.Our findings reveal that the aforementioned inconsistencies are pervasive. Compared to signatures based on DEGs, PCS-based signatures exhibit superior performance, identifying more drugs with higher prediction accuracy, as confirmed by DrugBank annotations. Notably, a significant proportion of predicted drugs without corresponding indications were subsequently validated in the ClinicalTrials database. Additionally, PCS-based signatures demonstrate elevated disease specificity and association with Drug Related Gene (DRG).

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“…Differential expressed genes (DEGs) serve as signatures in the Cmap reference database [24,26] , with DEGs or DEGs-based approaches being the most widely used methods for diseasespeci c signatures [4,[29][30][31][32][33][34] . Additionally, other methods exist, such as utilizing event-free survival via Cox regression to identify prognosis-related gene signatures [35] , exploring disease-related genes through genome-wide association studies (GWAS) [36] , conducting transcriptome-wide association studies (TWAS) [37] , and using hub genes in protein-protein interaction (PPI) networks as queried signatures [38] . Functional annotations databases, text mining, and other methods are also applied to prioritize disease risk genes and generate signatures [39,40] .…”

mentioning

confidence: 99%

Tumor relapse-free survival prognosis related consistency between cancer tissue and adjacent normal tissue in drug repurposing for solid tumor via connectivity map

Hao,

Li,

Qiao

et al. 2024

Preprint

View full text Add to dashboard Cite

Traditional drug discovery encounters challenges, including high costs, time-intensive processes, and inherent risks. Drug repurposing emerges as a compelling alternative strategy to identify new indications for investigational or approved drugs, circumventing these obstacles. Among the various drug repurposing methods, the Disease-specific Signature-based Connectivity Map (Cmap) approach is widely utilized. However, the commonly employed method for constructing disease-specific signatures, known as Differentially Expressed Genes (DEG), faces issues related to inconsistencies between dysregulated genes and the prognosis of genes in tumor tissue, as well as discrepancies in prognosis genes between tumor and normal tissues. In this study, we propose a novel approach, Prognosis Consistency Scoring (PCS), aimed at addressing these inconsistencies. PCS measures the consistency of gene prognosis between tumor and normal tissues by combining the Recurrence-Free Survival (RFS) prognosis power of genes in both contexts. Disease-specific signatures are then constructed based on PCS, and drug repurposing is performed using the Cmap and Lincs Unified Environment (CLUE). Validation of predicted drugs is conducted using data from DrugBank and ClinicalTrials databases. Our findings reveal that the aforementioned inconsistencies are pervasive. Compared to signatures based on DEGs, PCS-based signatures exhibit superior performance, identifying more drugs with higher prediction accuracy, as confirmed by DrugBank annotations. Notably, a significant proportion of predicted drugs without corresponding indications were subsequently validated in the ClinicalTrials database. Additionally, PCS-based signatures demonstrate elevated disease specificity and association with Drug Related Gene (DRG).

show abstract

Comparative molecular docking analysis for analyzing the inhibitory effect of Anakinra and Ustekinumab against IL17F

Cited by 7 publications

References 41 publications

Assessing the efficacy of cinnamon compounds against H. pylori through molecular docking, MD Simulations and ADMET analyses

Assessing the efficacy of cinnamon compounds against H. pylori through molecular docking, MD Simulations and ADMET analyses

Tumor relapse-free survival prognosis related consistency between cancer tissue and adjacent normal tissue in drug repurposing for solid tumor via connectivity map

Tumor relapse-free survival prognosis related consistency between cancer tissue and adjacent normal tissue in drug repurposing for solid tumor via connectivity map

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