Evidence map›Paper›PMID 38907989›Full record

ArticleJournal of chemical information and modeling2024

DiPPI: A Curated Data Set for Drug-like Molecules in Protein-Protein Interfaces.

Fatma Cankara, Simge Senyuz, Ahenk Zeynep Sayin, Attila Gursoy, Ozlem Keskin

Abstract read
In one paragraph

Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

3 citing papers in PubMed.

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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

5 authors.

Fatma CankaraGraduate School of Sciences and Engineering, Koç University, İstanbul 34450, Turkey.ORCID 0000-0002-9595-3722
Simge SenyuzGraduate School of Sciences and Engineering, Koç University, İstanbul 34450, Turkey.ORCID 0000-0003-1945-2288
Ahenk Zeynep SayinDepartment of Chemical and Biological Engineering, Koç University, İstanbul 34450, Turkey.ORCID 0009-0009-4339-5755
Attila GursoyDepartment of Computer Engineering, Koç University, İstanbul 34450, Turkey.ORCID 0000-0002-2297-2113
Ozlem KeskinDepartment of Chemical and Biological Engineering, Koç University, İstanbul 34450, Turkey.ORCID 0000-0002-4202-4049

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Proteins interact through their interfaces, and dysfunction of protein-protein interactions (PPIs) has been associated with various diseases. Therefore, investigating the properties of the drug-modulated PPIs and interface-targeting drugs is critical. Here, we present a curated large data set for drug-like molecules in protein interfaces. We further introduce DiPPI (Drugs in Protein-Protein Interfaces), a two-module web site to facilitate the search for such molecules and their properties by exploiting our data set in drug repurposing studies. In the interface module of the web site, we present several properties, of interfaces, such as amino acid properties, hotspots, evolutionary conservation of drug-binding amino acids, and post-translational modifications of these residues. On the drug-like molecule side, we list drug-like small molecules and FDA-approved drugs from various databases and highlight those that bind to the interfaces. We further clustered the drugs based on their molecular fingerprints to confine the search for an alternative drug to a smaller space. Drug properties, including Lipinski's rules and various molecular descriptors, are also calculated and made available on the web site to guide the selection of drug molecules. Our data set contains 534,203 interfaces for 98,632 protein structures, of which 55,135 are detected to bind to a drug-like molecule. 2214 drug-like molecules are deposited on our web site, among which 335 are FDA-approved. DiPPI provides users with an easy-to-follow scheme for drug repurposing studies through its well-curated and clustered interface and drug data and is freely available at http://interactome.ku.edu.tr:8501.

Indexed as

ProteinsDatabases, ProteinData CurationDrug RepositioningHumansPharmaceutical PreparationsProtein BindingProtein Interaction MappingPharmaceutical PreparationsProteins

Identifiers

PMID38907989
PMCPMC11577314

What OpenQuestion holds

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LicenceCC BY
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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.