ArticlePLoS computational biology2016
Effective Design of Multifunctional Peptides by Combining Compatible Functions.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- CPP2Vec: A representation learning approach for cell-penetrating peptides prediction.PLoS computational biology · 2026Article
- Design and classify innovative antimicrobial and dual- and multi-functional peptides using generative artificial intelligence.Scientific reports · 2026Article
- Antimicrobial Peptides as Novel Ecological Approaches to Caries Prevention.Drug design, development and therapy · 2026Review
- Engineered mesenchymal stromal cells with bispecific polyvalent peptides suppress excessive neutrophil infiltration and boost therapy.Science advances · 2025Article
- Exploring the Chemical Features and Biomedical Relevance of Cell-Penetrating Peptides.International journal of molecular sciences · 2024Review
- Review
- Fusing Peptide Epitopes for Advanced Multiplex Serological Testing for SARS-CoV-2 Antibody Detection.ACS bio & med chem Au · 2024Article
- A bird's-eye view of the biological mechanism and machine learning prediction approaches for cell-penetrating peptides.Frontiers in artificial intelligence · 2024Review
- Characterization of four peptides from milk fermented with kombucha cultures and their metal complexes-in search of new biotherapeutics.Frontiers in molecular biosciences · 2024Article
- Evaluation of the Synthetic Multifunctional Peptide Hp-MAP3 Derivative of Temporin-PTa.Toxins · 2023Article
- Antimicrobial peptides with cell-penetrating activity as prophylactic and treatment drugs.Bioscience reports · 2022Review
- Designed Multifunctional Peptides for Intracellular Targets.Antibiotics (Basel, Switzerland) · 2022Review
- Article
- Selective Moonlighting Cell-Penetrating Peptides.Pharmaceutics · 2021Article
- A large-scale comparative study on peptide encodings for biomedical classification.NAR genomics and bioinformatics · 2021Article
- Immunoglobulin Classification Based on FC* and GC* Features.Frontiers in genetics · 2021Article
- TargetCPP: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree.Journal of computer-aided molecular design · 2020Article
- PSBP-SVM: A Machine Learning-Based Computational Identifier for Predicting Polystyrene Binding Peptides.Frontiers in bioengineering and biotechnology · 2020Article
- What Can Pleiotropic Proteins in Innate Immunity Teach Us about Bioconjugation and Molecular Design?Bioconjugate chemistry · 2018Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multifunctionality is a common trait of many natural proteins and peptides, yet the rules to generate such multifunctionality remain unclear. We propose that the rules defining some protein/peptide functions are compatible. To explore this hypothesis, we trained a computational method to predict cell-penetrating peptides at the sequence level and learned that antimicrobial peptides and DNA-binding proteins are compatible with the rules of our predictor. Based on this finding, we expected that designing peptides for CPP activity may render AMP and DNA-binding activities. To test this prediction, we designed peptides that embedded two independent functional domains (nuclear localization and yeast pheromone activity), linked by optimizing their composition to fit the rules characterizing cell-penetrating peptides. These peptides presented effective cell penetration, DNA-binding, pheromone and antimicrobial activities, thus confirming the effectiveness of our computational approach to design multifunctional peptides with potential therapeutic uses. Our computational implementation is available at http://bis.ifc.unam.mx/en/software/dcf.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.