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Identification of Protein Complexes by Integrating Protein Abundance and Interaction Features Using a Deep Learning Strategy

Many essential cellular functions are carried out by multi-protein complexes that can be characterized by their protein–protein interactions. The interactions between protein subunits are critically dependent on the strengths of their interactions and their cellular abundances, both of which span or...

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Autores principales: Li, Bohui, Altelaar, Maarten, van Breukelen, Bas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10178578/
https://www.ncbi.nlm.nih.gov/pubmed/37175590
http://dx.doi.org/10.3390/ijms24097884
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author Li, Bohui
Altelaar, Maarten
van Breukelen, Bas
author_facet Li, Bohui
Altelaar, Maarten
van Breukelen, Bas
author_sort Li, Bohui
collection PubMed
description Many essential cellular functions are carried out by multi-protein complexes that can be characterized by their protein–protein interactions. The interactions between protein subunits are critically dependent on the strengths of their interactions and their cellular abundances, both of which span orders of magnitude. Despite many efforts devoted to the global discovery of protein complexes by integrating large-scale protein abundance and interaction features, there is still room for improvement. Here, we integrated >7000 quantitative proteomic samples with three published affinity purification/co-fractionation mass spectrometry datasets into a deep learning framework to predict protein–protein interactions (PPIs), followed by the identification of protein complexes using a two-stage clustering strategy. Our deep-learning-technique-based classifier significantly outperformed recently published machine learning prediction models and in the process captured 5010 complexes containing over 9000 unique proteins. The vast majority of proteins in our predicted complexes exhibited low or no tissue specificity, which is an indication that the observed complexes tend to be ubiquitously expressed throughout all cell types and tissues. Interestingly, our combined approach increased the model sensitivity for low abundant proteins, which amongst other things allowed us to detect the interaction of MCM10, which connects to the replicative helicase complex via the MCM6 protein. The integration of protein abundances and their interaction features using a deep learning approach provided a comprehensive map of protein–protein interactions and a unique perspective on possible novel protein complexes.
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spelling pubmed-101785782023-05-13 Identification of Protein Complexes by Integrating Protein Abundance and Interaction Features Using a Deep Learning Strategy Li, Bohui Altelaar, Maarten van Breukelen, Bas Int J Mol Sci Article Many essential cellular functions are carried out by multi-protein complexes that can be characterized by their protein–protein interactions. The interactions between protein subunits are critically dependent on the strengths of their interactions and their cellular abundances, both of which span orders of magnitude. Despite many efforts devoted to the global discovery of protein complexes by integrating large-scale protein abundance and interaction features, there is still room for improvement. Here, we integrated >7000 quantitative proteomic samples with three published affinity purification/co-fractionation mass spectrometry datasets into a deep learning framework to predict protein–protein interactions (PPIs), followed by the identification of protein complexes using a two-stage clustering strategy. Our deep-learning-technique-based classifier significantly outperformed recently published machine learning prediction models and in the process captured 5010 complexes containing over 9000 unique proteins. The vast majority of proteins in our predicted complexes exhibited low or no tissue specificity, which is an indication that the observed complexes tend to be ubiquitously expressed throughout all cell types and tissues. Interestingly, our combined approach increased the model sensitivity for low abundant proteins, which amongst other things allowed us to detect the interaction of MCM10, which connects to the replicative helicase complex via the MCM6 protein. The integration of protein abundances and their interaction features using a deep learning approach provided a comprehensive map of protein–protein interactions and a unique perspective on possible novel protein complexes. MDPI 2023-04-26 /pmc/articles/PMC10178578/ /pubmed/37175590 http://dx.doi.org/10.3390/ijms24097884 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Bohui
Altelaar, Maarten
van Breukelen, Bas
Identification of Protein Complexes by Integrating Protein Abundance and Interaction Features Using a Deep Learning Strategy
title Identification of Protein Complexes by Integrating Protein Abundance and Interaction Features Using a Deep Learning Strategy
title_full Identification of Protein Complexes by Integrating Protein Abundance and Interaction Features Using a Deep Learning Strategy
title_fullStr Identification of Protein Complexes by Integrating Protein Abundance and Interaction Features Using a Deep Learning Strategy
title_full_unstemmed Identification of Protein Complexes by Integrating Protein Abundance and Interaction Features Using a Deep Learning Strategy
title_short Identification of Protein Complexes by Integrating Protein Abundance and Interaction Features Using a Deep Learning Strategy
title_sort identification of protein complexes by integrating protein abundance and interaction features using a deep learning strategy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10178578/
https://www.ncbi.nlm.nih.gov/pubmed/37175590
http://dx.doi.org/10.3390/ijms24097884
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