Analyzing of Molecular Networks for Human Diseases and Drug Discovery
- PMID: 30101711
- PMCID: PMC6174636
- DOI: 10.2174/1568026618666180813143408
Analyzing of Molecular Networks for Human Diseases and Drug Discovery
Abstract
Molecular networks represent the interactions and relations of genes/proteins, and also encode molecular mechanisms of biological processes, development and diseases. Among the molecular networks, protein-protein Interaction Networks (PINs) have become effective platforms for uncovering the molecular mechanisms of diseases and drug discovery. PINs have been constructed for various organisms and utilized to solve many biological problems. In human, most proteins present their complex functions by interactions with other proteins, and the sum of these interactions represents the human protein interactome. Especially in the research on human disease and drugs, as an emerging tool, the PIN provides a platform to systematically explore the molecular complexities of specific diseases and the references for drug design. In this review, we summarized the commonly used approaches to aid disease research and drug discovery with PINs, including the network topological analysis, identification of novel pathways, drug targets and sub-network biomarkers for diseases. With the development of bioinformatic techniques and biological networks, PINs will play an increasingly important role in human disease research and drug discovery.
Keywords: Alzheimer`s disease; Drug discovery; Multiple sclerosis; Network analysis; Protein-protein interaction network; Sub-network biomarkers..
Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
Similar articles
-
Interaction networks: from protein functions to drug discovery. A review.Pathol Biol (Paris). 2009 Jun;57(4):324-33. doi: 10.1016/j.patbio.2008.10.004. Epub 2008 Dec 13. Pathol Biol (Paris). 2009. PMID: 19070972 Review.
-
Modelling human protein interaction networks as metric spaces has potential in disease research and drug target discovery.BMC Syst Biol. 2014 Jun 14;8:68. doi: 10.1186/1752-0509-8-68. BMC Syst Biol. 2014. PMID: 24929653 Free PMC article.
-
Network approaches to drug discovery.Expert Opin Drug Discov. 2013 Jan;8(1):7-20. doi: 10.1517/17460441.2013.741119. Epub 2012 Nov 10. Expert Opin Drug Discov. 2013. PMID: 23140510 Review.
-
Protein-protein interactions: principles, techniques, and their potential role in new drug development.J Biomol Struct Dyn. 2011 Jun;28(6):929-38. doi: 10.1080/07391102.2011.10508619. J Biomol Struct Dyn. 2011. PMID: 21469753 Review.
-
Protein interactions: mapping interactome networks to support drug target discovery and selection.Methods Mol Biol. 2012;910:279-96. doi: 10.1007/978-1-61779-965-5_12. Methods Mol Biol. 2012. PMID: 22821600 Review.
Cited by
-
Drug repositioning via host-pathogen protein-protein interactions for the treatment of cervical cancer.Front Oncol. 2023 Jan 25;13:1096081. doi: 10.3389/fonc.2023.1096081. eCollection 2023. Front Oncol. 2023. PMID: 36761959 Free PMC article.
-
Special Issue "Deployment of Proteomics Approaches in Biomedical Research".Int J Mol Sci. 2024 Jan 31;25(3):1717. doi: 10.3390/ijms25031717. Int J Mol Sci. 2024. PMID: 38338994 Free PMC article.
-
The Protein-Protein Interaction Network of Hereditary Parkinsonism Genes Is a Hierarchical Scale-Free Network.Yonsei Med J. 2022 Aug;63(8):724-734. doi: 10.3349/ymj.2022.63.8.724. Yonsei Med J. 2022. PMID: 35914754 Free PMC article.
-
Smell Detection Agent Optimisation Framework and Systems Biology Approach to Detect Dys-Regulated Subnetwork in Cancer Data.Biomolecules. 2021 Dec 27;12(1):37. doi: 10.3390/biom12010037. Biomolecules. 2021. PMID: 35053185 Free PMC article.
-
Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks.Sci Rep. 2021 Jul 8;11(1):14095. doi: 10.1038/s41598-021-93336-z. Sci Rep. 2021. PMID: 34238960 Free PMC article.
References
-
- Whisstock J.C., Lesk A.M. Prediction of protein function from protein sequence and structure. Q. Rev. Biophys. 2004;36(3):307–340. - PubMed
-
- Zaman N., Li L., Jaramillo M.L., Sun Z., Tibiche C., Banville M., Collins C., Trifiro M., Paliouras M., Nantel A., O’Connor-McCourt M., Wang E. Signaling network assessment of mutations and copy number variations predict breast cancer subtype-specific drug targets. Cell Reports. 2013;5(1):216–223. - PubMed
Publication types
MeSH terms
Substances
LinkOut - more resources
Full Text Sources
Other Literature Sources