PepSpace

Bioactivity Module

This module contains machine learning-based algorithms for predicting blood-brain barrier-penetrating peptides (BrainPepPass), cell-penetrating peptides (ConvBoost-CPP), and quorum-sensing peptides (QuorumPep-Pred). It supports the analysis of synthetic and natural non-modified peptides using PDB or FASTA files as input.

To use this module, the user must first select the algorithm. Next, upload the peptide file in FASTA or PDB format using one of the options below. Then, the user should click "SUBMIT" to start the prediction or "RESET" to clear the uploaded files.

Upload your peptides to begin

Paste your sequences here (FASTA)

Select the algorithm

B3PP prediction (BrainPepPass)

CPP prediction (ConvBoost-CPP)

QSP prediction (QuorumPep-Pred)

or upload your structural file (PDB)

Format compatibility: All three algorithms accept both FASTA and PDB input. If a given model cannot produce a result for a specific input (e.g. a non-standard residue that breaks structure/sequence parsing), the corresponding cell in the results table shows the error message instead of a score, without affecting the other two models.

References

BrainPepPass: de Oliveira ECL, Hirmz H, Wynendaele E, Seixas Feio JA, Moreira IMS, da Costa KS, Lima AH, De Spiegeleer B, de Sales Júnior CS. BrainPepPass: A Framework Based on Supervised Dimensionality Reduction for Predicting Blood-Brain Barrier-Penetrating Peptides. J Chem Inf Model. 2024 Apr 8;64(7):2368-2382. PMID: 38054399.
DOI: 10.1021/acs.jcim.3c00951

ConvBoostCPPred: Seixas Feio JA, de Oliveira ECL, de Sales CS Junior, da Costa KS, E Lima AHL. Investigating molecular descriptors in cell-penetrating peptides prediction with deep learning: Employing N, O, and hydrophobicity according to the Eisenberg scale. PLoS One. 2024 Jun 13;19(6):e0305253. PMID: 38870192; PMCID: PMC11175476.
DOI: 10.1371/journal.pone.0305253

Bioactivity Module

Upload your peptides to begin

Results —

References