Evidence map›Paper›PMID 31489889›Full record

ReviewMolecules (Basel, Switzerland)2019

Reviewing HIV-1 Gag Mutations in Protease Inhibitors Resistance: Insights for Possible Novel Gag Inhibitor Designs.

Chinh Tran-To Su, Darius Wen-Shuo Koh, Samuel Ken-En Gan

Open access · goldAbstract readReview
In one paragraph

Review in Molecules (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
2.1field-weighted citation impact, top 13% 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

18 citing papers in PubMed, 25 citations in OpenAlex.

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  15. mBio · 2020
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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

3 authors at 1 institution in 1 country.

Chinh Tran-To SuAntibody & Product Development Lab, Bioinformatics Institute, A*STAR, Singapore 138671, Singapore.
Darius Wen-Shuo KohAntibody & Product Development Lab, Bioinformatics Institute, A*STAR, Singapore 138671, Singapore.
Samuel Ken-En GanAntibody & Product Development Lab, Bioinformatics Institute, A*STAR, Singapore 138671, Singapore. samuelg@bii.a-star.edu.sg.ORCID 0000-0001-9936-5090
Agency for Science, Technology and Research · SG

Funding

Bioinformatics Institute, A*STAR Core fund of Antibody and Product Development Lab
6 · The paper itself

Abstract

HIV protease inhibitors against the viral protease are often hampered by drug resistance mutations in protease and in the viral substrate Gag. To overcome this drug resistance and inhibit viral maturation, targeting Gag alongside protease rather than targeting protease alone may be more efficient. In order to successfully inhibit Gag, understanding of its drug resistance mutations and the elicited structural changes on protease binding needs to be investigated. While mutations on Gag have already been mapped to protease inhibitor resistance, there remain many mutations, particularly the non-cleavage mutations, that are not characterized. Through structural studies to unravel how Gag mutations contributes to protease drug resistance synergistically, it is thus possible to glean insights to design novel Gag inhibitors. In this review, we discuss the structural role of both novel and previously reported Gag mutations in PI resistance, and how new Gag inhibitors can be designed.

Indexed as

Drug Resistance, ViralAnti-HIV AgentsDrug Designgag Gene Products, Human Immunodeficiency VirusHIV-1HIV ProteaseHIV Protease InhibitorsHumansModels, MolecularMutationProtein ConformationStructure-Activity RelationshipAnti-HIV Agentsgag Gene Products, Human Immunodeficiency VirusHIV ProteaseHIV Protease Inhibitorsdrug designdrug resistance mutationsGag inhibitorsHIV-1 Gagproteaseprotease inhibitors

Identifiers

PMID31489889
PMCPMC6767625
OpenAlexW2964011324

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.