ArticleInternational journal of molecular sciences2021
In-Pero: Exploiting Deep Learning Embeddings of Protein Sequences to Predict the Localisation of Peroxisomal Proteins.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Emerging approaches for characterizing spatial and temporal dynamics of pathogen-induced organelle remodeling.Cell systems · 2026Review
- Natural Language Processing Methods for the Study of Protein-Ligand Interactions.Journal of chemical information and modeling · 2025Review
- Impact of Alignments on the Accuracy of Protein Subcellular Localization Predictions.Proteins · 2025Article
- Protein subcellular localization prediction tools.Computational and structural biotechnology journal · 2024Review
- Article
- GABA promotes peroxisome proliferation inPlant direct · 2024Article
- SCLpred-ECL: Subcellular Localization Prediction by Deep N-to-1 Convolutional Neural Networks.International journal of molecular sciences · 2024Article
- Protein sequence analysis in the context of drug repurposing.BMC medical informatics and decision making · 2024Article
- Identification of plant vacuole proteins by using graph neural network and contact maps.BMC bioinformatics · 2023Article
- OrganelX web server for sub-peroxisomal and sub-mitochondrial protein localization and peroxisomal target signal detection.Computational and structural biotechnology journal · 2023Article
- Enzyme Commission Number Prediction and Benchmarking with Hierarchical Dual-core Multitask Learning Framework.Research (Washington, D.C.) · 2023Article
- Computational Approaches for Peroxisomal Protein Localization.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Identification of plant vacuole proteins by exploiting deep representation learning features.Computational and structural biotechnology journal · 2022Article
- Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics.Frontiers in bioinformatics · 2022Review
- Insights Into the Peroxisomal Protein Inventory of Zebrafish.Frontiers in physiology · 2022Article
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3 authors.
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Abstract
Peroxisomes are ubiquitous membrane-bound organelles, and aberrant localisation of peroxisomal proteins contributes to the pathogenesis of several disorders. Many computational methods focus on assigning protein sequences to subcellular compartments, but there are no specific tools tailored for the sub-localisation (matrix vs. membrane) of peroxisome proteins. We present here In-Pero, a new method for predicting protein sub-peroxisomal cellular localisation. In-Pero combines standard machine learning approaches with recently proposed multi-dimensional deep-learning representations of the protein amino-acid sequence. It showed a classification accuracy above 0.9 in predicting peroxisomal matrix and membrane proteins. The method is trained and tested using a double cross-validation approach on a curated data set comprising 160 peroxisomal proteins with experimental evidence for sub-peroxisomal localisation. We further show that the proposed approach can be easily adapted (In-Mito) to the prediction of mitochondrial protein localisation obtaining performances for certain classes of proteins (matrix and inner-membrane) superior to existing tools.
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