A computational study of somatostatin subtype-4 receptor agonist binding

Slater, Olivia; Kontoyianni, Maria

doi:10.1007/s42452-022-04968-3

Cited by 3 publications

(3 citation statements)

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“…The PDF total energy was calculated using the sum of all homology-derived and stereochemical pseudo-energy terms. Its means of satisfaction of spatial restraints were generated during the model construction [46]. The PDF physical energy, i.e., the sum of stereochemical pseudo-energy terms, is a value related to the physical properties such as bond, angle, and torsion angle among PDF energies.…”

Section: Retrieval Of the Protein Sequence And Homology Modelingmentioning

confidence: 99%

“…DOPE is an atomic distance-based statistical potential; the DOPE score of a protein represents the conformational energy that measures the relative stability of one molecular conformation with respect to other conformations of the same protein. It aids in selecting the best model out of the predicted model structures [46].…”

Section: Retrieval Of the Protein Sequence And Homology Modelingmentioning

confidence: 99%

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Structure-Based In Silico Screening of Marine Phlorotannins for Potential Walrus Calicivirus Inhibitor

Kang,

Kim,

Heo

et al. 2023

IJMS

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A new calicivirus isolated from a walrus was reported in 2004. Since unknown marine mammalian zoonotic viruses could pose great risks to human health, this study aimed to develop therapeutic countermeasures to quell any potential outbreak of a pandemic caused by this virus. We first generated a 3D model of the walrus calicivirus capsid protein and identified compounds from marine natural products, especially phlorotannins, as potential walrus calicivirus inhibitors. A 3D model of the target protein was generated using homology modeling based on two publicly available template sequences. The sequence of the capsid protein exhibited 31.3% identity and 42.7% similarity with the reference templates. The accuracy and reliability of the predicted residues were validated via Ramachandran plotting. Molecular docking simulations were performed between the capsid protein 3D model and 17 phlorotannins. Among them, five phlorotannins demonstrated markedly stable docking profiles; in particular, 2,7-phloroglucinol-6,6-bieckol showed favorable structural integrity and stability during molecular dynamics simulations. The results indicate that the phlorotannins are promising walrus calicivirus inhibitors. Overall, the study findings showcase the rapid turnaround of in silico-based drug discovery approaches, providing useful insights for developing potential therapies against novel pathogenic viruses, especially when the 3D structures of the viruses remain experimentally unknown.

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Section: Retrieval Of the Protein Sequence And Homology Modelingmentioning

confidence: 99%

Section: Retrieval Of the Protein Sequence And Homology Modelingmentioning

confidence: 99%

Structure-Based In Silico Screening of Marine Phlorotannins for Potential Walrus Calicivirus Inhibitor

Kang,

Kim,

Heo

et al. 2023

IJMS

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“…In both human and animal models, these agonists have demonstrated their effectiveness in inhibiting angiogenesis and reducing the proliferation of both healthy and cancerous cells. [177], [183], [184], [185], [186]…”

Section: Introductionmentioning

confidence: 99%

Graph Attention Networks for Drug Combination Discovery: Targeting Pancreatic Cancer Genes with RAIN Protocol

Parichehreh,

Kiaei,

Boush

et al. 2024

Preprint

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BackgroundMalignant neoplasm of the pancreas (MNP), a highly lethal illness with bleak outlook and few therapeutic avenues, entails numerous cellular transformations. These include irregular proliferation of ductal cells, activation of stellate cells, initiation of epithelial-to-mesenchymal transition, and changes in cell shape, movement, and attachment. Discovering potent drug cocktails capable of addressing the genetic and protein factors underlying pancreatic cancer’s development is formidable due to the disease’s intricate and varied nature.MethodIn this study, we introduce a fresh model utilizing Graph Attention Networks (GATs) to pinpoint potential drug pairings with synergistic effects for MNP, following the RAIN protocol. This protocol comprises three primary stages: Initially, employing Graph Neural Network (GNN) to suggest drug combinations for disease management by acquiring embedding vectors of drugs and proteins from a diverse knowledge graph encompassing various biomedical data types, such as drug-protein interactions, gene expression, and drug-target interactions. Subsequently, leveraging natural language processing to gather pertinent articles from clinical trials incorporating the previously recommended drugs. Finally, conducting network meta-analysis to assess the relative effectiveness of these drug combinations.ResultWe implemented our approach on a network dataset featuring drugs and genes as nodes, connected by edges representing their respective p-values. Our GAT model identified Gemcitabine, Pancrelipase Amylase, and Octreotide as the optimal drug combination for targeting the human genes/proteins associated with this cancer. Subsequent scrutiny of clinical trials and literature confirmed the validity of our findings. Additionally, network meta-analysis confirmed the efficacy of these medications concerning the pertinent genes.ConclusionBy employing GAT within the RAIN protocol, our approach represents a novel and efficient method for recommending prominent drug combinations to target proteins/genes associated with pancreatic cancer. This technique has the potential to aid healthcare professionals and researchers in identifying optimal treatments for patients while also unveiling underlying disease mechanisms.HighlightsGraph Attention Networks (GATs) used to recommend drug combinations for pancreatic cancerRAIN protocol applied to extract relevant information from clinical trials and literatureGemcitabine, Pancrelipase Amylase, and Octreotide identified as optimal drug combinationNetwork meta-analysis confirmed the effectiveness of the drug combination on gene targetsNovel and efficient method for drug discovery and disease mechanism elucidationAbstract Figure

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3-Thio-3,4,5-trisubstituted-1,2,4-triazoles: high affinity somatostatin receptor-4 agonist synthesis and structure–activity relationships

Crider,

Hospital,

Sandoval

et al. 2025

RSC Med. Chem.

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A computational study of somatostatin subtype-4 receptor agonist binding

Cited by 3 publications

References 74 publications

Structure-Based In Silico Screening of Marine Phlorotannins for Potential Walrus Calicivirus Inhibitor

Structure-Based In Silico Screening of Marine Phlorotannins for Potential Walrus Calicivirus Inhibitor

Graph Attention Networks for Drug Combination Discovery: Targeting Pancreatic Cancer Genes with RAIN Protocol

3-Thio-3,4,5-trisubstituted-1,2,4-triazoles: high affinity somatostatin receptor-4 agonist synthesis and structure–activity relationships

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