Evidence map›Paper›PMID 33400087›Full record

ArticleThe protein journal2021

Targeting the Autophagy Specific Lipid Kinase VPS34 for Cancer Treatment: An Integrative Repurposing Strategy.

Poornimaa Murali, Kanika Verma, Thanyada Rungrotmongkol, Perarasu Thangavelu, Ramanathan Karuppasamy

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Article in The protein journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

5 authors.

Poornimaa MuraliDepartment of Biotechnology, School of BioSciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India.
Kanika VermaStructural and Computational Biology Research Unit, Department of Biochemistry, Faculty of Science, Chulalongkorn University, 254 Phayathai Road, Bangkok, 10330, Thailand.
Thanyada RungrotmongkolStructural and Computational Biology Research Unit, Department of Biochemistry, Faculty of Science, Chulalongkorn University, 254 Phayathai Road, Bangkok, 10330, Thailand.
Perarasu ThangaveluDepartment of Chemical Engineering, AC Tech Campus, Anna University, Chennai, Tamil Nadu, 600025, India.
Ramanathan KaruppasamyDepartment of Biotechnology, School of BioSciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India. kramanathan@vit.ac.in.ORCID 0000-0002-1613-0729

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The impact of autophagy on cancer treatment and its corresponding responsiveness has galvanized the scientific community to develop novel inhibitors for cancer treatment. Importantly, the discovery of inhibitors that targets the early phase of autophagy was identified as a beneficial choice. Despite the number of research in recent years, screening of the DrugBank repository (9591 molecules) for the Vacuolar protein sorting 34 (VPS34) has not been reported earlier. Therefore, the present study was designed to identify potential VPS34 antagonists using integrated pharmacophore strategies. Primarily, an energy-based pharmacophore and receptor cavity-based analysis yielded five (DHRRR) and seven featured (AADDHRR) pharmacophore hypotheses respectively, which were utilized for the database screening process. The glide score, the binding free energy, pharmacokinetics and pharmacodynamics properties were examined to narrow down the screened compounds. This analysis yielded a hit molecule, DB03916 that exhibited a better docking score, higher binding affinity and better drug-like properties in contrast to the reference compound that suffers from a toxicity property. Importantly, the result was validated using a 50 ns molecular dynamics simulation study. Overall, we conclude that the identified hit molecule DB03916 is believed to serve as a prospective antagonist against VPS34 for cancer treatment.

Indexed as

Anti-Bacterial AgentsAntineoplastic AgentsAutophagyBinding SitesClass III Phosphatidylinositol 3-KinasesDatabases, PharmaceuticalDrug RepositioningGene ExpressionHumansHydrogen BondingMolecular Docking SimulationMolecular Dynamics SimulationNeoplasm ProteinsNeoplasmsPiperazinesProtein BindingAnti-Bacterial AgentsAntineoplastic AgentsClass III Phosphatidylinositol 3-KinasesNeoplasm ProteinsPiperazinesProtein Kinase InhibitorsPyrimidinesFree energy landscapeMM-GBSAMolecular dynamics simulationPrincipal component analysisVacuolar protein sorting 34

Identifiers

What OpenQuestion holds

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Registered trials

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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.