Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward
- Physically interacting proteins form macromolecule complexes that drive diverse cellular processes. Advances in experimental techniques that capture interactions between proteins provide us with protein-protein interaction (PPI) networks from several model organisms. These datasets have enabled the prediction and other computational analyses of protein complexes. Here we provide a systematic review of the state-of-the-art algorithms for protein complex prediction from PPI networks proposed in the past two decades. The existing approaches that solve this problem are categorized into three groups, including: cluster-quality-based, node affinity-based, and network embedding-based approaches, and we compare and contrast the advantages and disadvantages. We further include a comparative analysis by computing the performance of eighteen methods based on twelve well-established performance measures on four widely used benchmark protein-protein interaction networks. Finally, the limitations and drawbacks of both, current data and approaches,Physically interacting proteins form macromolecule complexes that drive diverse cellular processes. Advances in experimental techniques that capture interactions between proteins provide us with protein-protein interaction (PPI) networks from several model organisms. These datasets have enabled the prediction and other computational analyses of protein complexes. Here we provide a systematic review of the state-of-the-art algorithms for protein complex prediction from PPI networks proposed in the past two decades. The existing approaches that solve this problem are categorized into three groups, including: cluster-quality-based, node affinity-based, and network embedding-based approaches, and we compare and contrast the advantages and disadvantages. We further include a comparative analysis by computing the performance of eighteen methods based on twelve well-established performance measures on four widely used benchmark protein-protein interaction networks. Finally, the limitations and drawbacks of both, current data and approaches, along with the potential solutions in this field are discussed, with emphasis on the points that pave the way for future research efforts in this field. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).…
Author details: | Sara OmranianORCiDGND, Zoran NikoloskiORCiDGND, Dominik G. Grimm |
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DOI: | https://doi.org/10.1016/j.csbj.2022.05.049 |
ISSN: | 2001-0370 |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/35685359 |
Title of parent work (English): | Computational and structural biotechnology journal |
Subtitle (English): | present state, challenges, and the way forward |
Publisher: | Research Network of Computational and Structural Biotechnology (RNCSB) |
Place of publishing: | Gotenburg |
Publication type: | Review |
Language: | English |
Date of first publication: | 2022/06/01 |
Publication year: | 2022 |
Release date: | 2024/03/08 |
Tag: | Clustering Algorithms; Network; Network embedding; Protein Complex Prediction; Protein-Protein interaction network |
Volume: | 20 |
Number of pages: | 14 |
First page: | 2699 |
Last Page: | 2712 |
Funding institution: | Bavarian State Ministry for Economic Affairs Regional Development and; Energy [07 02/683 87/19/21/19/22/20/23]; Max-Planck Gesellschaft (MPG);; Max Planck Institute of Molecular Plant Physiology (MPIMP) |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie und Biologie |
DDC classification: | 5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie |
Peer review: | Referiert |
Publishing method: | Open Access / Gold Open-Access |
DOAJ gelistet | |
License (German): | CC-BY - Namensnennung 4.0 International |