Related Experiment Video
Updated: Jan 20, 2026
Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
Published on: August 21, 2019
Prediction of Protein Sub-Mitochondria Locations Using Protein Interaction Networks
Adele Sadat Haghighat Hoseini1, Mitra Mirzarezaee1,2
1Department of Computer Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran.
Background:
Prediction of the protein localization is among the most important issues in the bioinformatics that is used for the prediction of the proteins in the cells and organelles such as mitochondria. In this study, several machine learning algorithms are applied for the prediction of the intracellular protein locations. These algorithms use the features extracted from protein sequences. In contrast, protein interactions have been less investigated.
Objectives:
As protein interactions usually occur in the same or adjacent places, using this feature to find the location would be efficient and impressive. This study did not aim at increasing the total accuracy of the conducted research. The study has focused on the features of the proteins' interaction and their employment which lead to a higher accuracy.
Materials And Methods:
In this study, we have examined the protein interaction network as one of the features for prediction of the protein localization and its effects on the prediction results. In this regards, we have gathered some of the most common features including Amino Acid Composition, Dipeptide Compositions, Pseudo Amino Acid Compositions (PseAAC), Position Specific Scoring Matrix (PSSM), Functional Domain, Gene Ontology information, and the Pair-wise sequence alignment. The results of the classification are compared to the ones using protein interactions. For achieving this goal different machine learning algorithms were tested.
Results:
The best-obtained results of using single feature set obtained using SVM classifier for PseAAC feature. The accuracy of combining all features with PPI data, using the Decision Tree and Random Forest classifiers, was 82.49% and 83.35%, respectively. In another experiment, using just protein interaction data with the different cutting points resulted in obtaining an accuracy of 93.035% for the protein location prediction.
Conclusion:
In total, it was shown that protein(s) interaction has a significant impact on the prediction of the mitochondrial proteins' location. This feature can separately distinguish the locations well. Using this feature the accuracy of the results is raised up to 5%.
More Related Videos
06:50Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
Published on: January 26, 2024
08:27Visualization and Quantification of Endogenous Intra-Organelle Protein Interactions at ER-Mitochondria Contact Sites by Proximity Ligation Assays
Published on: October 20, 2023
Related Concept Videos
02:26Protein Networks
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
02:26Protein Networks
01:19Translocation of Proteins into the Mitochondria
Sorting of outer membrane proteins:
Mitochondrial outer membrane proteins are of two types: the transmembrane, beta-barrel porins, and the membrane-anchored, alpha-helical proteins. Beta-barrel porin precursors are translocated by the TOM complex and inserted into the outer mitochondrial membrane by the SAM complex. In contrast,...
02:15Protein and Protein Structure
A protein's shape is critical to its function. For example, an enzyme...
02:04Protein-protein Interfaces
02:54Protein Kinases and Phosphatases
Protein kinases
Many proteins in the cell are regulated by phosphorylation, the addition of a phosphate group. A family of enzymes called kinases...