Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward

Sara Omranian, Zoran Nikoloski, Dominik G. Grimm

Research output: Contribution to journalReview articlepeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)2699-2712
Number of pages14
JournalComputational and Structural Biotechnology Journal
Volume20
DOIs
StatePublished - Jan 2022

Keywords

  • Network Clustering Algorithms
  • Network embedding
  • Protein Complex Prediction
  • Protein-Protein interaction network

Fingerprint

Dive into the research topics of 'Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward'. Together they form a unique fingerprint.

Cite this