Homeoprotein Intercellular Transfer, the Hidden Face of Cell-Penetrating Peptides

2010 ◽  
pp. 249-257 ◽  
Author(s):  
Alain Prochiantz
Keyword(s):  
Cell Penetrating Peptides ◽  
Intercellular Transfer ◽  
Cell Penetrating

Oncolytic Coxsackievirus and the Mechanisms of its Effects on Cancer: A Narrative Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Ali Ahmadi ◽  
Hadi Esmaeili Gouvarchin Ghaleh ◽  
Ruhollah Dorostkar ◽  
Mahdieh Farzanehpour ◽  
Masoumeh Bolandian
Keyword(s):  
Genetic Disease ◽  
Control Cell ◽  
Gene Mutations ◽  
Therapeutic Agents ◽  
Cell Penetrating Peptides ◽  
Triggered Release ◽  
New Techniques ◽  
Viral Therapy ◽  
Cell Penetrating ◽  
Cancer Cell Targeting

Abstract:: Cancer is a genetic disease triggered by gene mutations, which control cell growth and their functionality inherited from previous generations. The targeted therapy of some tumors was not especially successful. A host of new techniques can be used to treat aptamer-mediated targeting, cancer immunotherapy, cancer stem cell (CSC) therapy, cell-penetrating peptides (CPPs), hormone therapy, intracellular cancer cell targeting, nanoparticles, and viral therapy. These include chemical-analog conjugation, gene delivery, ligand-receptor-based targeting, prodrug therapies, and triggered release strategies. Virotherapy is a biotechnological technique for turning viruses into therapeutic agents by the reprogramming of viruses to cure diseases. In several tumors, including melanoma, multiple myeloma, bladder cancer, and breast cancer, the oncolytic capacity of oncolytic Coxsackievirus has been studied. The present study aims to assess oncolytic Coxsackievirus and its mechanisms of effect on cancer cells.

A Novel Amino Acid Sequence-based Computational Approach to Predicting Cell-penetrating Peptides

2019 ◽  
Vol 15 (3) ◽  
pp. 206-211 ◽  
Author(s):  
Jihui Tang ◽  
Jie Ning ◽  
Xiaoyan Liu ◽  
Baoming Wu ◽  
Rongfeng Hu
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Amino Acid Position ◽  
Cell Penetrating Peptides ◽  
Support Vector ◽  
Cell Penetration ◽  
Drug Candidates ◽  
Machine Learning Model ◽  
Cell Penetrating ◽  
Novel Method

<P>Introduction: Machine Learning is a useful tool for the prediction of cell-penetration compounds as drug candidates. </P><P> Materials and Methods: In this study, we developed a novel method for predicting Cell-Penetrating Peptides (CPPs) membrane penetrating capability. For this, we used orthogonal encoding to encode amino acid and each amino acid position as one variable. Then a software of IBM spss modeler and a dataset including 533 CPPs, were used for model screening. </P><P> Results: The results indicated that the machine learning model of Support Vector Machine (SVM) was suitable for predicting membrane penetrating capability. For improvement, the three CPPs with the most longer lengths were used to predict CPPs. The penetration capability can be predicted with an accuracy of close to 95%. </P><P> Conclusion: All the results indicated that by using amino acid position as a variable can be a perspective method for predicting CPPs membrane penetrating capability.</P>

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