Evidence map›Paper›PMID 32078861›Full record

ArticleEuropean journal of medicinal chemistry2020

Inhibition of striatal-enriched protein tyrosine phosphatase by targeting computationally revealed cryptic pockets.

Xuben Hou, Jin-Peng Sun, Lin Ge, Xiao Liang, Kangshuai Li, Yingkai Zhang, Hao Fang

Open access · greenAbstract readVideo-Audio Media
PubMed Full text at PMC Publisher
In one paragraph

Article in European journal of medicinal chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 44% of its field
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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Integrated Molecular Modeling and Machine Learning for Drug Design.Journal of chemical theory and computation · 2023
    Review
  3. Review
  4. Review
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

7 authors at 2 institutions in 2 countries.

Xuben HouDepartment of Medicinal Chemistry and Key Laboratory of Chemical Biology of Natural Products (MOE), School of Pharmaceutical Science, Shandong University, Jinan, Shandong, 250012, China; Department of Chemistry, New York University, New York, NY, 10003, United States.
Jin-Peng SunKey Laboratory Experimental Teratology of the Ministry of Education and Department of Biochemistry and Molecular Biology, School of Medicine, Shandong University, Jinan, Shandong, 250012, China.
Lin GeKey Laboratory Experimental Teratology of the Ministry of Education and Department of Biochemistry and Molecular Biology, School of Medicine, Shandong University, Jinan, Shandong, 250012, China.
Xiao LiangDepartment of Medicinal Chemistry and Key Laboratory of Chemical Biology of Natural Products (MOE), School of Pharmaceutical Science, Shandong University, Jinan, Shandong, 250012, China.
Kangshuai LiKey Laboratory Experimental Teratology of the Ministry of Education and Department of Biochemistry and Molecular Biology, School of Medicine, Shandong University, Jinan, Shandong, 250012, China.
Yingkai ZhangDepartment of Chemistry, New York University, New York, NY, 10003, United States; NYU-ECNU Center for Computational Chemistry, New York University-Shanghai, Shanghai, 200122, China.
Hao FangDepartment of Medicinal Chemistry and Key Laboratory of Chemical Biology of Natural Products (MOE), School of Pharmaceutical Science, Shandong University, Jinan, Shandong, 250012, China. Electronic address: haofangcn@sdu.edu.cn.
Shandong University · CNNew York University · US

Funding

Computational modulator design and machine learning to target protein-protein interactionsR35GM127040 · NIGMS · NEW YORK UNIVERSITY · PI Yingkai Zhang · 2018 to 2026
$4.6M
NIGMS NIH HHS R35 GM127040
6 · The paper itself

Abstract

Cryptic pockets, which are not apparent in crystallographic structures, provide promising alternatives to traditional binding sites for drug development. However, identifying cryptic pockets is extremely challenging and the therapeutic potential of cryptic pockets remains unclear. Here, we reported the discovery of novel inhibitors for striatal-enriched protein tyrosine phosphatase (STEP), a potential drug target for multiple neuropsychiatric disorders, based on cryptic pocket detection. By combining the use of molecular dynamics simulations and fragment-centric topographical mapping, we identified transiently open cryptic pockets and identified 12 new STEP inhibition scaffolds through structure-based virtual screening. Site-directed mutagenesis verified the binding of ST3 with the predicted cryptic pockets. Moreover, the most potent and selective inhibitors could modulate the phosphorylation of both ERK1/2 and Pyk2 in PC12 cells.

Indexed as

AnimalsBinding SitesDrug Evaluation, PreclinicalEnzyme InhibitorsFuransHumansMolecular Dynamics SimulationMolecular StructureMutagenesis, Site-DirectedMutationPC12 CellsProtein BindingProtein Tyrosine Phosphatases, Non-ReceptorQuinolinesRatsStructure-Activity RelationshipEnzyme InhibitorsFuransProtein Tyrosine Phosphatases, Non-ReceptorPtpn5 protein, ratQuinolinesCryptic pocketInhibitorStriatal-enriched protein tyrosine phosphataseVirtual screening

Identifiers

PMID32078861
PMCPMC7163917
OpenAlexW3005722483

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.