Background: Protein-protein interactions (PPIs) are a critical component for many underlying biological processes. A PPI network can provide insight into the mechanisms of these processes, as well as the relationships among different proteins and toxicants that are potentially involved in the processes. There are many PPI databases publicly available, each with a specific focus. The challenge is how to effectively combine their contents to generate a robust and biologically relevant PPI network.
Methods: In this study, seven public PPI databases, BioGRID, DIP, HPRD, IntAct, MINT, REACTOME, and SPIKE, were used to explore a powerful approach to combine multiple PPI databases for an integrated PPI network. We developed a novel method called k-votes to create seven different integrated networks by using values of k ranging from 1-7. Functional modules were mined by using SCAN, a Structural Clustering Algorithm for Networks. Overall module qualities were evaluated for each integrated network using the following statistical and biological measures: (1) modularity, (2) similarity-based modularity, (3) clustering score, and (4) enrichment.
Results: Each integrated human PPI network was constructed based on the number of votes (k) for a particular interaction from the committee of the original seven PPI databases. The performance of functional modules obtained by SCAN from each integrated network was evaluated. The optimal value for k was determined by the functional module analysis. Our results demonstrate that the k-votes method outperforms the traditional union approach in terms of both statistical significance and biological meaning. The best network is achieved at k = 2, which is composed of interactions that are confirmed in at least two PPI databases. In contrast, the traditional union approach yields an integrated network that consists of all interactions of seven PPI databases, which might be subject to high false positives.
Conclusions: We determined that the k-votes method for constructing a robust PPI network by integrating multiple public databases outperforms previously reported approaches and that a value of k=2 provides the best results. The developed strategies for combining databases show promise in the advancement of network construction and modeling.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3236850 | PMC |
http://dx.doi.org/10.1186/1471-2105-12-S10-S7 | DOI Listing |
Lett Appl Microbiol
January 2025
Clinical Laboratory, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University.
MRSA's resistance poses a global health challenge. This study investigates lysine succinylation in MRSA using proteomics and bioinformatics approaches to uncover metabolic and virulence mechanisms, with the goal of identifying novel therapeutic targets. Mass spectrometry and bioinformatics analyses mapped the MRSA succinylome, identifying 8 048 succinylation sites on 1 210 proteins.
View Article and Find Full Text PDFJ Biol Eng
January 2025
Department of Traumatic Clinic, Shanghai East Hospital of Tongji University, Shanghai, 200120, China.
Objective: The direction of this study was to detect and analyze the specific mechanism of anti-apoptosis in mesenchymal stem cells (MSCs) cells caused by high expression of BCL2.
Methods: Bioinformatics was completed in Link omics. GO analysis and KEGG analysis were carried out, and the grope tool of Link omics database was used to evaluate PPI information and other core path analysis information.
PLoS One
January 2025
Department of Laboratory, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, P.R. China.
Background: Systemic lupus erythematosus (SLE) is a complex and incurable autoimmune disease, so several drug remission for SLE symptoms have been developed and used at present. However, treatment varies by patient and disease activity, and existing medications for SLE were far from satisfactory. Novel drug targets to be found for SLE therapy are still needed.
View Article and Find Full Text PDFAm J Transl Res
December 2024
Department of Emergency Medical Services, Faculty of Health Sciences AlQunfudah, Umm Al-Qura University Mekkah, Saudi Arabia.
Background: Liver Hepatocellular Carcinoma (LIHC) is a prevalent and aggressive liver cancer with limited therapeutic options. Identifying key genes involved in LIHC can enhance our understanding of its molecular mechanisms and aid in the development of targeted therapies. This study aims to identify differentially expressed genes (DEGs) and key hub genes in LIHC using bioinformatics approaches and experimental validation.
View Article and Find Full Text PDFNAR Cancer
March 2025
Department of Pathology and Laboratory Medicine, The Warren Alpert Medical School of Brown University, 593 Eddy Street, Providence, RI 02903, USA.
Cancer is a complex disease with heterogeneous mutational and gene expression patterns. Subgroups of patients who share a phenotype might share a specific genetic architecture including protein-protein interactions (PPIs). We developed the Atlas of Protein-Protein Interactions in Cancer (APPIC), an interactive webtool that provides PPI subnetworks of 10 cancer types and their subtypes shared by cohorts of patients.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!