Evidence map›Paper›PMID 35764916›Full record

ArticleBMC bioinformatics2022

A virus-target host proteins recognition method based on integrated complexes data and seed extension.

Shengrong Xia, Yingchun Xia, Chulei Xiang, Hui Wang, Chao Wang, Jin He, Guolong Shi, Lichuan Gu

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
0.4field-weighted citation impact, top 42% 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.

  1. Pooled it
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 at 1 institution in 2 countries.

Shengrong XiaSchool of Information and Computer, Anhui Agricultural University, Hefei, 230036, Anhui, China.
Yingchun XiaSchool of Information and Computer, Anhui Agricultural University, Hefei, 230036, Anhui, China.
Chulei XiangSchool of Information and Computer, Anhui Agricultural University, Hefei, 230036, Anhui, China.
Hui WangSchool of Information and Computer, Anhui Agricultural University, Hefei, 230036, Anhui, China.
Chao WangSchool of Information and Computer, Anhui Agricultural University, Hefei, 230036, Anhui, China.
Jin HeSchool of Information and Computer, Anhui Agricultural University, Hefei, 230036, Anhui, China.
Guolong ShiSchool of Information and Computer, Anhui Agricultural University, Hefei, 230036, Anhui, China.
Lichuan GuSchool of Information and Computer, Anhui Agricultural University, Hefei, 230036, Anhui, China. glc@ahau.edu.cn.ORCID http://orcid.org/0000-0002-3768-8203
Anhui Agricultural University · CN

Funding

Anhui Provincial Department of Education Research Project KJ2021A0179Anhui university provincial natural science research project KJ2020A0107China Postdoctoral Science Foundation NO.2021M692473National Natural Science Foundation of China 31771679Natural Science Foundation of Anhui Province 2108085QF260the 2022 Anhui University collaborative innovation project GXXT-2022-the Anhui Foundation for Science and Technology Major Project of China 201904e01020006the Anhui Foundation for Science and Technology Major Project of China 202103b06020013The Open Research Fund of National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University NO.AE202103
6 · The paper itself

Abstract

backgroundTarget drugs play an important role in the clinical treatment of virus diseases. Virus-encoded proteins are widely used as targets for target drugs. However, they cannot cope with the drug resistance caused by a mutated virus and ignore the importance of host proteins for virus replication. Some methods use interactions between viruses and their host proteins to predict potential virus-target host proteins, which are less susceptible to mutated viruses. However, these methods only consider the network topology between the virus and the host proteins, ignoring the influences of protein complexes. Therefore, we introduce protein complexes that are less susceptible to drug resistance of mutated viruses, which helps recognize the unknown virus-target host proteins and reduce the cost of disease treatment.

resultsSince protein complexes contain virus-target host proteins, it is reasonable to predict virus-target human proteins from the perspective of the protein complexes. We propose a coverage clustering-core-subsidiary protein complex recognition method named CCA-SE that integrates the known virus-target host proteins, the human protein-protein interaction network, and the known human protein complexes. The proposed method aims to obtain the potential unknown virus-target human host proteins. We list part of the targets after proving our results effectively in enrichment experiments.

conclusionsOur proposed CCA-SE method consists of two parts: one is CCA, which is to recognize protein complexes, and the other is SE, which is to select seed nodes as the core of protein complexes by using seed expansion. The experimental results validate that CCA-SE achieves efficient recognition of the virus-target host proteins.

Indexed as

Protein Interaction MapsVirusesCluster AnalysisDrug Delivery SystemsHost-Pathogen InteractionsHumansHost proteinProtein complexes recognitionProtein–protein interaction networkVirus–target protein

Identifiers

PMID35764916
PMCPMC9238269
OpenAlexW4283647273

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.