ArticleScientific reports2019
In silico design and optimization of selective membranolytic anticancer peptides.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- The impact of the conjugation manner of targeting agent and tandem cell-penetrating peptide on the efficacy of mitomycin C liposomes in treating triple-negative breast tumors.International journal of pharmaceutics: X · 2025Article
- Investigating How Lysophosphatidylcholine and Lysophosphatidylethanolamine Enhance the Membrane Permeabilization Efficacy of Host Defense Peptide Piscidin 1.The journal of physical chemistry. B · 2025Article
- Developing New Peptides and Peptide-Drug Conjugates for Targeting the FGFR2 Receptor-Expressing Tumor Cells and 3D Spheroids.Biomimetics (Basel, Switzerland) · 2024Article
- Peptidic Compound as DNA Binding Agent:Protein and peptide letters · 2024Article
- Exploratory data analysis of physicochemical parameters of natural antimicrobial and anticancer peptides: Unraveling the patterns and trends for the rational design of novel peptides.BioImpacts : BI · 2024Article
- Short Fragmented Peptides fromCurrent drug discovery technologies · 2024Article
- Anticarcinogenic cationic peptides derived from tryptic hydrolysis of β-lactoglobulin.Frontiers in molecular biosciences · 2024Article
- Targeting Peptides: The New Generation of Targeted Drug Delivery Systems.Pharmaceutics · 2023Review
- Biological Activity of Cyclic Peptide Extracted fromBiology · 2023Article
- Machine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anticancer Peptides.ChemMedChem · 2022Article
- Emerging Computational Approaches for Antimicrobial Peptide Discovery.Antibiotics (Basel, Switzerland) · 2022Review
- Integrated Design of a Membrane-Lytic Peptide-Based Intravenous Nanotherapeutic Suppresses Triple-Negative Breast Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity.Pharmaceuticals (Basel, Switzerland) · 2022Review
- An Integrated Proteomics and Bioinformatics Analysis of the Anticancer Properties of RT2 Antimicrobial Peptide on Human Colon Cancer (Caco-2) Cells.Molecules (Basel, Switzerland) · 2022Article
- Design of Membrane Active Peptides Considering Multi-Objective Optimization for Biomedical Application.Membranes · 2022Review
- Natural Peptides Inducing Cancer Cell Death: Mechanisms and Properties of Specific Candidates for Cancer Therapeutics.Molecules (Basel, Switzerland) · 2021Review
- Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification.Briefings in bioinformatics · 2021Review
- Copper-binding anticancer peptides from the piscidin family: an expanded mechanism that encompasses physical and chemical bilayer disruption.Scientific reports · 2021Article
- Machine learning-guided discovery and design of non-hemolytic peptides.Scientific reports · 2020Article
- Peptides with Dual Antimicrobial-Anticancer Activity: Strategies to Overcome Peptide Limitations and Rational Design of Anticancer Peptides.Molecules (Basel, Switzerland) · 2020Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Membranolytic anticancer peptides represent a potential strategy in the fight against cancer. However, our understanding of the underlying structure-activity relationships and the mechanisms driving their cell selectivity is still limited. We developed a computational approach as a step towards the rational design of potent and selective anticancer peptides. This machine learning model distinguishes between peptides with and without anticancer activity. This classifier was experimentally validated by synthesizing and testing a selection of 12 computationally generated peptides. In total, 83% of these predictions were correct. We then utilized an evolutionary molecular design algorithm to improve the peptide selectivity for cancer cells. This simulated molecular evolution process led to a five-fold selectivity increase with regard to human dermal microvascular endothelial cells and more than ten-fold improvement towards human erythrocytes. The results of the present study advocate for the applicability of machine learning models and evolutionary algorithms to design and optimize novel synthetic anticancer peptides with reduced hemolytic liability and increased cell-type selectivity.
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