TumorHPD: Computational approach for designing tumor homing peptides

TumorHPD: A Web Server for Prediction and Design of Tumor Homing Peptides TumorHPD is a computational tool developed to predict, design, and scan tumor homing peptides. Tumor homing peptides are short peptides that specifically recognize tumor cells or tumor-associated microenvironments such as tumor vasculature after systemic delivery. This resource provides a machine learning-based approach for identifying peptides with tumor-targeting potential, which can support targeted drug delivery, tumor imaging, and peptide-based cancer therapeutics. Web Server: http://crdd.osdd.net/raghava/tumorhpd/ Citation Sharma, A., Kapoor, P., Gautam, A., Chaudhary, K., Kumar, R., Chauhan, J. S., Tyagi, A., and Raghava, G. P. S. Computational approach for designing tumor homing peptides. Scientific Reports, 3, 1607, 2013. https://doi.org/10.1038/srep01607 About the Research Tumor homing peptides are short peptides, generally 3 to 15 amino acids long, that can specifically bind to tumor cells or tumor vasculature. These peptides are useful because they can guide therapeutic molecules, imaging agents, nanoparticles, oligonucleotides, or anticancer drugs directly to tumor sites. Common tumor-homing motifs include RGD and NGR. RGD peptides bind to integrins, while NGR peptides bind to aminopeptidase N, which is present on tumor endothelial cells. TumorHPD was developed to computationally predict such tumor homing peptides and to help design improved peptide analogues with better tumor-targeting potential. Data Compilation: The tool was trained on experimentally validated tumor homing peptides collected from the TumorHoPe database. Since experimentally validated non-tumor-homing peptides were not available, random peptides generated from Swiss-Prot proteins were used as negative examples. Methodology: TumorHPD uses Support Vector Machine-based models trained on sequence-derived peptide features such as amino acid composition, dipeptide composition, and binary profile patterns.