Evidence map›Paper›PMID 39729480›Full record

ArticlePloS one2024

Discovery of novel TACE inhibitors using graph convolutional network, molecular docking, molecular dynamics simulation, and Biological evaluation.

Muhammad Yasir, Jinyoung Park, Eun-Taek Han, Jin-Hee Han, Won Sun Park, Mubashir Hassan, Andrzej Kloczkowski, Wanjoo Chun

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Muhammad YasirDepartment of Pharmacology, Kangwon National University School of Medicine, Chuncheon, Republic of Korea.
Jinyoung ParkDepartment of Pharmacology, Kangwon National University School of Medicine, Chuncheon, Republic of Korea.
Eun-Taek HanDepartment of Medical Environmental Biology and Tropical Medicine, Kangwon National University School of Medicine, Chuncheon, Republic of Korea.
Jin-Hee HanDepartment of Medical Environmental Biology and Tropical Medicine, Kangwon National University School of Medicine, Chuncheon, Republic of Korea.
Won Sun ParkDepartment of Physiology, Kangwon National University School of Medicine, Chuncheon, Republic of Korea.
Mubashir HassanThe Steve and Cindy Rasmussen Institute for Genomic Medicine at Nationwide Children's Hospital, Columbus, Ohio, United States of America.
Andrzej KloczkowskiThe Steve and Cindy Rasmussen Institute for Genomic Medicine at Nationwide Children's Hospital, Columbus, Ohio, United States of America.
Wanjoo ChunDepartment of Pharmacology, Kangwon National University School of Medicine, Chuncheon, Republic of Korea.ORCID 0000-0003-1984-3545

Funding

Novel Use of Genome Information to Understand MutationsR01HG012117 · NHGRI · IOWA STATE UNIVERSITY · PI JERNIGAN, ROBERT L, KLOCZKOWSKI, ANDRZEJ · 2021 to 2025
$2.3M
Protein Sequence MatchingR01GM127701 · NIGMS · IOWA STATE UNIVERSITY · PI JERNIGAN, ROBERT L, KLOCZKOWSKI, ANDRZEJ · 2018 to 2021
$1.4M
NHGRI NIH HHS R01 HG012117NIGMS NIH HHS R01 GM127701
6 · The paper itself

Abstract

The increasing utilization of deep learning models in drug repositioning has proven to be highly efficient and effective. In this study, we employed an integrated deep-learning model followed by traditional drug screening approach to screen a library of FDA-approved drugs, aiming to identify novel inhibitors targeting the TNF-α converting enzyme (TACE). TACE, also known as ADAM17, plays a crucial role in the inflammatory response by converting pro-TNF-α to its active soluble form and cleaving other inflammatory mediators, making it a promising target for therapeutic intervention in diseases such as rheumatoid arthritis. Reference datasets containing active and decoy compounds specific to TACE were obtained from the DUD-E database. Using RDKit, a cheminformatics toolkit, we extracted molecular features from these compounds. We applied the GraphConvMol model within the DeepChem framework, which utilizes graph convolutional networks, to build a predictive model based on the DUD-E datasets. Our trained model was subsequently used to predict the TACE inhibitory potential of FDA-approved drugs. From these predictions, Vorinostat was identified as a potential TACE inhibitor. Moreover, molecular docking and molecular dynamics simulation were conducted to validate these findings, using BMS-561392 as a reference TACE inhibitor. Vorinostat, originally an FDA-approved drug for cancer treatment, exhibited strong binding interactions with key TACE residues, suggesting its repurposing potential. Biological evaluation with RAW 264.7 cell confirmed the computational results, demonstrating that Vorinostat exhibited comparable inhibitory activity against TACE. In conclusion, our study highlights the capability of deep learning models to enhance virtual screening efforts in drug discovery, efficiently identifying potential candidates for specific targets such as TACE. Vorinostat, as a newly identified TACE inhibitor, holds promise for further exploration and investigation in the treatment of inflammatory diseases like rheumatoid arthritis.

Indexed as

ADAM17 ProteinMolecular Docking SimulationMolecular Dynamics SimulationAnimalsDeep LearningDrug DiscoveryDrug Evaluation, PreclinicalDrug RepositioningHumansMiceVorinostatADAM17 ProteinADAM17 protein, humanVorinostat

Identifiers

PMID39729480
PMCPMC11676921

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.