Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Signal Sequences and Sorting Receptors

Signal Sequences and Sorting Receptors

Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

PeptideSGCL: Structure-Enhanced Graph-Transformer Encoding and Dual-Level Contrastive Learning for Peptide Property Prediction.

ACS synthetic biology·2026

Same author

CMA-Nano: A DNA Methylation Detection Method for Nanopore Sequencing Data Based on a Cross-Modal Attention Mechanism.

ACS omega·2026

Same author

An Explainable Deep Learning Framework Integrating DNA Sequence and Transcription Initiation Signals for Gene Expression Prediction.

ACS synthetic biology·2026

Same author

LysePred: A Multiscale Convolutional Neural Network for Predicting Hemolytic Activity of Antimicrobial Peptides.

ACS synthetic biology·2026

Same author

An Interpretable Deep Learning Framework Leveraging RNA Foundation Model and Capsule Networks for Accurate Prediction of RNA 2'-O-Methylation Sites.

Journal of chemical information and modeling·2026

Same author

CONTRA-IL6: an interpretable hybrid convolutional neural network and Transformer framework for accurate prediction of interleukin-6-inducing peptides using protein language models.

Briefings in bioinformatics·2026

Search research articles

Related Experiment Video

Updated: Aug 16, 2025

Construction of Cyclic Cell-Penetrating Peptides for Enhanced Penetration of Biological Barriers

Construction of Cyclic Cell-Penetrating Peptides for Enhanced Penetration of Biological Barriers

Published on: September 19, 2022

SiameseCPP: a sequence-based Siamese network to predict cell-penetrating peptides by contrastive learning.

Xin Zhang¹, Lesong Wei², Xiucai Ye²

¹Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China.

Briefings in Bioinformatics

|December 23, 2022

Summary

This study introduces SiameseCPP, a new deep learning method for predicting cell-penetrating peptides (CPPs). SiameseCPP improves CPP prediction accuracy by using contrastive learning and a Siamese neural network.

More Related Videos

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Biotinylated Cell-penetrating Peptides to Study Intracellular Protein-protein Interactions

Biotinylated Cell-penetrating Peptides to Study Intracellular Protein-protein Interactions

Published on: December 20, 2017

Related Experiment Videos

Last Updated: Aug 16, 2025

Construction of Cyclic Cell-Penetrating Peptides for Enhanced Penetration of Biological Barriers

Construction of Cyclic Cell-Penetrating Peptides for Enhanced Penetration of Biological Barriers

Published on: September 19, 2022

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Biotinylated Cell-penetrating Peptides to Study Intracellular Protein-protein Interactions

Biotinylated Cell-penetrating Peptides to Study Intracellular Protein-protein Interactions

Published on: December 20, 2017

Area of Science:

Biochemistry
Computational Biology
Drug Delivery

Background:

Cell-penetrating peptides (CPPs) are crucial for intracellular delivery of therapeutic molecules.
Existing machine learning methods for CPP prediction often rely on handcrafted features and lack consideration of peptide similarity.
There is a need for advanced computational tools to accurately identify CPPs for therapeutic applications.

Purpose of the Study:

To develop a novel deep learning framework, SiameseCPP, for automated and accurate prediction of cell-penetrating peptides.
To leverage contrastive learning and Siamese neural networks for enhanced CPP representation learning.
To evaluate the performance and generalization ability of SiameseCPP compared to existing methods.

Main Methods:

Developed SiameseCPP, a deep learning framework utilizing a pretrained model and a Siamese neural network (transformer and GRUs).
Implemented contrastive learning for the first time in a CPP predictive model to learn discriminative representations.
Conducted comprehensive experiments to compare SiameseCPP against baseline models.

Main Results:

SiameseCPP significantly outperforms existing baseline models in predicting cell-penetrating peptides.
The model demonstrates strong generalization ability, achieving good performance on other functional peptide datasets.
Contrastive learning proved effective for building a robust CPP predictive model.

Conclusions:

SiameseCPP represents a significant advancement in the automated prediction of cell-penetrating peptides.
The framework's superior performance and generalization capability highlight its potential for therapeutic development.
This study validates the efficacy of contrastive learning in the domain of peptide function prediction.