ArticlePloS one2013
The development of a universal in silico predictor of protein-protein interactions.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Significance of Sequence Features in Classification of Protein-Protein Interactions Using Machine Learning.The protein journal · 2024Article
- Protein-protein interaction prediction with deep learning: A comprehensive review.Computational and structural biotechnology journal · 2022Review
- Predicting Protein-Protein Interactions via Gated Graph Attention Signed Network.Biomolecules · 2021Article
- Review
- An integration of deep learning with feature embedding for protein-protein interaction prediction.PeerJ · 2019Article
- A novel matrix of sequence descriptors for predicting protein-protein interactions from amino acid sequences.PloS one · 2019Article
- Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery.Molecules (Basel, Switzerland) · 2018Review
- Prediction of protein-protein interactions with clustered amino acids and weighted sparse representation.International journal of molecular sciences · 2015Article
- Negatome 2.0: a database of non-interacting proteins derived by literature mining, manual annotation and protein structure analysis.Nucleic acids research · 2014Article
- Protein-protein interaction detection: methods and analysis.International journal of proteomics · 2014Review
- Structural bioinformatics of the interactome.Annual review of biophysics · 2014Review
- Unravelling the Neospora caninum secretome through the secreted fraction (ESA) and quantification of the discharged tachyzoite using high-resolution mass spectrometry-based proteomics.Parasites & vectors · 2013Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Protein-protein interactions (PPIs) are essential for understanding the function of biological systems and have been characterized using a vast array of experimental techniques. These techniques detect only a small proportion of all PPIs and are labor intensive and time consuming. Therefore, the development of computational methods capable of predicting PPIs accelerates the pace of discovery of new interactions. This paper reports a machine learning-based prediction model, the Universal In Silico Predictor of Protein-Protein Interactions (UNISPPI), which is a decision tree model that can reliably predict PPIs for all species (including proteins from parasite-host associations) using only 20 combinations of amino acids frequencies from interacting and non-interacting proteins as learning features. UNISPPI was able to correctly classify 79.4% and 72.6% of experimentally supported interactions and non-interacting protein pairs, respectively, from an independent test set. Moreover, UNISPPI suggests that the frequencies of the amino acids asparagine, cysteine and isoleucine are important features for distinguishing between interacting and non-interacting protein pairs. We envisage that UNISPPI can be a useful tool for prioritizing interactions for experimental validation.
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