Current Computer-Aided Drug DesignCurrent Computer-Aided Drug DesignCurrent Computer-Aided Drug Design1573-40991875-6697Bentham Science64396110.2174/1573409919666230426093230ChemistryResearch ArticleApplication of Cell Penetrating Peptides for Intracellular Delivery of Endostatin: A Computational ApproachZamaniMozhdehinfo@benthamscience.netNezafatNavidinfo@benthamscience.netMokarramPoonehinfo@benthamscience.netKadkhodaeiBehnaminfo@benthamscience.netAutophagy Research Center, Shiraz University of Medical SciencesPharmaceutical Sciences Research Cente, Shiraz University of Medical SciencesDepartment of Biochemistry, Autophagy Research Center, School of Medicine, Shiraz University of Medical SciencesDepartment of Radiation Oncology,, Shiraz University of Medical Sciences0103202420320822307012025Copyright ©; 2024, Bentham Science Publishers2024Bentham Science Publishershttps://rjeid.com/1573-4099/article/view/643961

Background:Endostatin is an antiangiogenic compound with anticancer activity. The poor stability and low half-life of endostatin are the main barriers to the clinical use of this protein. Cell-penetrating peptides (CPPs) are extensively applied as carrier in the delivery of drugs and different therapeutic agents. Therefore, they can be proper candidates to improve endostatin delivery to the target cells.

Objective:In this study, we aim to computationally predict appropriate CPPs for the delivery of endostatin.

Methods:Potential appropriate CPPs for protein delivery were selected based on the literature. The main parameters for detection of best CPP-endostatin fusions, including stability, hydrophobicity, antigenicity, and subcellular localization, were predicted using ProtParam, VaxiJen, and DeepLoc-1.0 servers, respectively. The 3D structures of the best CPP-Endostatin fusions were modeled by the I-TASSER server. The predicted models were validated using PROCHECK, ERRAT, Verify3D and ProSA-Web servers. The best models were visualized by the PyMol molecular graphics system.

Results:Considering the principal parameters in the selection of best CPPs for endostatin delivery, endostatin fusions with four CPPs, including Cyt c-ss-MAP, TP-biot1, MPGα, and DPV1047, high stability and hydrophobicity, no antigenicity and extracellular localization were predicted as the best potential fusions for endostatin delivery. Four CPPs, including Cyt c-ss-MAP, TP-biot1, MPGα, and DPV1047, were predicted as the best potential candidates to improve endostatin delivery.

Conclusion:Application of these CPPs may overcome the limitation of endostatin therapeutic applications, including poor stability and low half-life. Subsequent experimental studies will contribute to verifying these computational results.

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