DOI: 10.5281/zenodo.18428381
title: Is-Pero Dataset: Training and Validation Data for Peroxisomal Protein Prediction
title: Dataset for reproducibility of the analysis from "Is-Pero: a web server application for predicting the localization of peroxisomal proteins"

description: Description

This dataset contains all training, validation, and test data used in the development and evaluation of Is-Pero, a comprehensive web server for end-to-end prediction of peroxisomal protein localization from raw FASTA sequences. The dataset supports reproducibility of results presented in the accompanying manuscript and includes data for three integrated prediction modules:

Is-Pero (Binary Classification): Distinguishes peroxisomal from non-peroxisomal proteins using logistic regression trained on 1,674 experimentally validated peroxisomal proteins and 133,761 non-peroxisomal proteins from SwissProt (release 2024-01). Includes 10-fold cross-validation (CV) splits and independent test sets for comparison with DeepLoc2.0.

In-Pero (Sub-compartment Prediction): Predicts localization within peroxisomes (matrix vs membrane) using a multi-layer perceptron trained on 160 peroxisomal proteins with experimentally validated sub-compartment annotations. Includes 5-fold cross-validation splits and a blind test set of 143 proteins.

Is-mPTS (PEX19 Binding Site Prediction): Identifies membrane-targeting signals recognized by the PEX19 chaperone using logistic regression trained on 82 balanced peptide sequences from human and yeast proteins. Includes 5-fold cross-validation and external validation on experimentally validated PEX19 binding sites from organisms not represented in training data.

Contents

Dataset

is-pero-seqs.tar.gz contains FASTA files with protein sequences or peptide windows for each module and benchmarking. Cross-validation fold assignments or test set designation are available in .tsv files.Is-Pero and Benchmarking with DeepLoc 2.0:



CV DeepLoc 2.0 - deeploc-cv.fasta.gz, deeploc-train.tsv

CV Is-Pero - is-pero-cv.fasta.gz, is-pero-cv.tsv

Test - deeploc-test.fasta.gz, deeploc-test.tsv

Train - deeploc-train.fasta.gz, deeploc-train.tsv


In-Pero:



CV - in-pero-cv.fasta.gz, in-pero-cv.tsv

Test - in-pero-test.fasta.gz, in-pero-test.tsv


Is-mPTS:



CV - is-mpts-cv.fasta.gz, is-mpts-cv.tsv

Test - is-mpts-test.fasta.gz, is-mpts-test.tsv





Prediction
Dataset
Test
Positives
Negatives
Total


Peroxisomal vs not-Peroxisomal
is-pero-cv
cross-validation
1,674
133,761
135,435


 
deeploc-train
training
304
27,666
27,970


 
deeploc-test
validation
380
3,800
4,180


Matrix vs Membrane
in-pero-cv
cross-validation
 28
132
160


 
in-pero-test
validation
50
42
92


mPTS vs not-mPTS
is-mpts-cv
cross-validation
41(23)
41
82


 
is-mpts-test
validation
13(9)
13
26




Table 1: Dataset composition and validation strategy for Is-Pero modules. Is-Pero uses ESM-2 1280-dimensional full-sequence embeddings; In-Pero and Is-Pero use the same embeddings. Is-mPTS extracts and averages local 15-amino acid window representations from per-residue embeddings. LR: logistic regression; MLP: multilayer perceptron; CV: cross-validation. ∗Experimentally validated. For mPTS datasets, the value outside parentheses refers to the number of mPTS-binding sequence segments, while the value in parentheses indicates the number of original protein sequences from which those segments were extracted.

Usage

These data enable:1. Complete reproducibility of Is-Pero model training and evaluation metrics2. Benchmarking of alternative methods on the same datasets3. Extension of models with additional sequences or features4. Development of complementary peroxisomal protein prediction tools

Redundancy Control

All datasets were constructed with explicit redundancy control using UniRef50 cluster representatives to prevent information leakage during cross-validation and ensure robust generalization estimates.

Related Resources

- Is-Pero Web Server: https://structure.biofold.org/is-pero- Source Code: https://github.com/biofold/is-pero- Docker Container: https://hub.docker.com/r/biofold/is-pero

Citation

If you use this dataset, please cite the accompanying manuscript:[]

License

[CC-BY-4.0]