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Identification of additional proteins in differential proteomics using protein interaction networks.

ABSTRACT: In this study, we developed a novel computational approach based on protein-protein interaction networks to identify a list of proteins that might have remained undetected in differential proteomic profiling experiments. We tested our computational approach on two sets of human smooth muscle cell protein extracts that were affected differently by DNase I treatment. Differential proteomic analysis by saturation DIGE resulted in the identification of 41 human proteins. The application of our approach to these 41 input proteins consisted of four steps: (i) Compilation of a human protein-protein interaction network from public databases; (ii) calculation of interaction scores based on functional similarity; (iii) determination of a set of candidate proteins that are needed to efficiently and confidently connect the 41 input proteins; and (iv) ranking of the resulting 25 candidate proteins. Two of the three highest-ranked proteins, beta-arrestin 1, and beta-arrestin 2, were experimentally tested, revealing that their abundance levels in human smooth muscle cell samples were indeed affected by DNase I treatment. These proteins had not been detected during the experimental proteomic analysis. Our study suggests that our computational approach may represent a simple, universal, and cost-effective means to identify additional proteins that remain elusive for current 2D gel-based proteomic profiling techniques.

SUBMITTER: Gwinner F

PROVIDER: S-EPMC3964363 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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Identification of additional proteins in differential proteomics using protein interaction networks.

Gwinner Frederik F Acosta-Martin Adelina E AE Boytard Ludovic L Chwastyniak Maggy M Beseme Olivia O Drobecq Hervé H Duban-Deweer Sophie S Juthier Francis F Jude Brigitte B Amouyel Philippe P Pinet Florence F Schwikowski Benno B

Proteomics 20130401 7

In this study, we developed a novel computational approach based on protein-protein interaction networks to identify a list of proteins that might have remained undetected in differential proteomic profiling experiments. We tested our computational approach on two sets of human smooth muscle cell protein extracts that were affected differently by DNase I treatment. Differential proteomic analysis by saturation DIGE resulted in the identification of 41 human proteins. The application of our appro ...[more]

PMID: 23386401

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Project description:Proteins are considered indispensable for facilitating an organism's viability, reproductive capabilities, and other fundamental physiological functions. Conventional biological assays are characterized by prolonged duration, extensive labor requirements, and financial expenses in order to identify essential proteins. Therefore, it is widely accepted that employing computational methods is the most expeditious and effective approach to successfully discerning essential proteins. Despite being a popular choice in machine learning (ML) applications, the deep learning (DL) method is not suggested for this specific research work based on sequence features due to the restricted availability of high-quality training sets of positive and negative samples. However, some DL works on limited availability of data are also executed at recent times which will be our future scope of work. Conventional ML techniques are thus utilized in this work due to their superior performance compared to DL methodologies. In consideration of the aforementioned, a technique called EPI-SF is proposed here, which employs ML to identify essential proteins within the protein-protein interaction network (PPIN). The protein sequence is the primary determinant of protein structure and function. So, initially, relevant protein sequence features are extracted from the proteins within the PPIN. These features are subsequently utilized as input for various machine learning models, including XGB Boost Classifier, AdaBoost Classifier, logistic regression (LR), support vector classification (SVM), Decision Tree model (DT), Random Forest model (RF), and Naïve Bayes model (NB). The objective is to detect the essential proteins within the PPIN. The primary investigation conducted on yeast examined the performance of various ML models for yeast PPIN. Among these models, the RF model technique had the highest level of effectiveness, as indicated by its precision, recall, F1-score, and AUC values of 0.703, 0.720, 0.711, and 0.745, respectively. It is also found to be better in performance when compared to the other state-of-arts based on traditional centrality like betweenness centrality (BC), closeness centrality (CC), etc. and deep learning methods as well like DeepEP, as emphasized in the result section. As a result of its favorable performance, EPI-SF is later employed for the prediction of novel essential proteins inside the human PPIN. Due to the tendency of viruses to selectively target essential proteins involved in the transmission of diseases within human PPIN, investigations are conducted to assess the probable involvement of these proteins in COVID-19 and other related severe diseases.

Dataset Information

Identification of additional proteins in differential proteomics using protein interaction networks.

Publications

Identification of additional proteins in differential proteomics using protein interaction networks.

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