Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1785040898519728128 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10178578 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT libohui identificationofproteincomplexesbyintegratingproteinabundanceandinteractionfeaturesusingadeeplearningstrategy AT altelaarmaarten identificationofproteincomplexesbyintegratingproteinabundanceandinteractionfeaturesusingadeeplearningstrategy AT vanbreukelenbas identificationofproteincomplexesbyintegratingproteinabundanceandinteractionfeaturesusingadeeplearningstrategy |