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

Author(s): Jihui Tang*, Jie Ning, Xiaoyan Liu, Baoming Wu, Rongfeng Hu*

Journal Name: Current Computer-Aided Drug Design

Volume 15 , Issue 3 , 2019

Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


Introduction: Machine Learning is a useful tool for the prediction of cell-penetration compounds as drug candidates.

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.

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%.

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.

Keywords: Cell-penetrating peptides, machine learning, prediction, support vector machine, IBM spss modeler, amino acid position.

Hansen, M.; Kilk, K.; Langel, Ü. Predicting cell-penetrating peptides. Adv. Drug Deliver. Rev.NI, 2008, 60, 572-579.
Green, M.; Paul, M.L. Autonomous functional domains of chemically synthesized human immunodeficiency virus tat trans-activator protein. Cell, 1988, 55-, 1179, 1188.
Frankel, A.D.; Pabo, C.O. Cellular uptake of the tat protein from human immunodeficiency virus. Cell, 1988, 55(6), 1189-1193.
Joliot, A.; Pernelle, C.; Deagostini-Bazin, H.; Prochiantz, A. Antennapedia homeobox peptide regulates neural morphogenesis. Proc. Natl. Acad. Sci. USA, 1991, 88, 1864-1868.
Derossi, D.; Joliot, A.H.; Chassaing, G.; Prochiantz, A. The third helix of the Antennapedia homeodomain translocates through biological membranes. J. Biol. Chem., 1994, 269, 10444-10450.
Eiríksdóttir, E.; Konate, K.; Langel, U.; Divita, G.; Deshayes, S. Secondary structure of cell-penetrating peptides controls membrane interaction and insertion. Biochim. Biophys. Acta, 2010, 1798, 1119-1128.
Elmquist, A.; Hansen, M.; Langel, U. Structure-activity relationship study of the cell-penetrating peptide pVEC. Biochim. Biophys. Acta, 2006, 1758, 721-729.
Kamide, K.; Nakakubo, H.; Uno, S.; Fukamizu, A. Isolation of novel cell-penetrating peptides from a random peptide library using in vitro virus and their modifications. Int. J. Mol. Med., 2010, 25, 41-51.
Morris, M.C.; Depollier, J.; Mery, J.; Heitz, F.; Divita, G. A peptide carrier for the delivery of biologically active proteins into mammalian cells. Nat. Biotechnol., 2001, 19, 1173-1176.
Vasconcelos, L.; Parn, K.; Langel, U. Therapeutic potential of cell-penetrating peptides. Ther. Deliv., 2013, 4, 573-591.
Guidotti, G.; Brambilla, L.; Rossi, D. Cell-Penetrating peptides: From basic research to clinics. Trends Pharmacol. Sci., 2017, 38, 406-424.
Agrawal, P.; Bhalla, S.; Usmani, S.S.; Singh, S.; Chaudhary, K.; Raghava, G.P.; Gautam, A. CPPsite 2.0: A repository of experimentally validated cell penetrating peptides. Nucleic Acids Res., 2016, 44, D1098-D1103.
Gautam, A.; Singh, H.; Tyagi, A.; Chaudhary, K.; Kumar, R.; Kapoor, P.; Raghava, G.P. CPPsite: A curated database of cell penetrating peptides. Database, 2012, 1-7.
Kristensen, M.; Birch, D.; Mørck, N.H. Applications and challenges for use of cell-penetrating peptides as delivery vectors for peptide and protein cargos. Int. J. Mol. Sci., 2016, 17, 1-17.
Christian, S.; Pilch, J.; Akerman, M.E.; Porkka, K.; Laakkonen, P.; Ruoslahti, E. Nucleolin expressed at the cell surface is a marker of endothelial cells in angiogenic blood vessels. J. Cell Biol., 2003, 163, 871-878.
Borrelli, A.; Tornesello, A.L.; Tornesello, M.L.; Buonaguro, F.M. cell penetrating peptides as molecular carriers for anti-cancer agents. Molecules, 2018, 23, 1-28.
Copolovici, D.M.; Langel, K.; Eriste, E.; Langel, Ü. Cell-penetrating peptides: Design, synthesis, and applications. ACS Nano, 2014, 8, 1972-1994.
Stewart, K.M.; Horton, K.L.; Kelley, S.O. Cell-penetrating peptides as delivery vehicles for biology and medicine. ChemInform, 2008, 39, 2242-2255.
Perillo, E.; Allard-Vannier, E.; Falanga, A.; Stiuso, P.; Vitiello, M.T.; Galdiero, M.; Galdiero, S.; Chourpa, I. Quantitative and qualitative effect of gH625 on the nanoliposome-mediated delivery of mitoxantrone anticancer drug to HeLa cells. Int. J. Pharm., 2015, 488, 59-66.
Peng, L.H.; Niu, J.; Zhang, C.Z.; Yu, W.; Wu, J.H.; Shan, Y.H.; Wang, X.R.; Shen, Y.Q.; Mao, Z.W.; Liang, W.Q.; Gao, J.Q. TAT conjugated cationic noble metal nanoparticles for gene delivery to epidermal stem cells. Biomaterials, 2014, 35, 5605-5618.
Gros, E.; Deshayes, S.; Morris, M.C.; Aldrian-Herrada, G.; Depollier, J.; Heitz, F.; Divita, G. A non-covalent peptide-based strategy for protein and peptide nucleic acid transduction. Biochim. Biophys. Acta, 2006, 1758, 384-393.
Elmquist, A.; Hansen, M.; Langel, U. Structure-activity relationship study of the cell-penetrating peptide pVEC. Biochim. Biophys. Acta, 2006, 1758, 721-729.
Pooga, M.; Langel, Ü. Classes of cell-penetrating peptides. Methods Mol. Biol., 2015, 1324, 3-28.
Sandberg, M.; Eriksson, L.; Jonsson, J.; Sjöström, M.; Wold, S. New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids. J. Med. Chem., 1998, 41, 2481-2491.
Thompson, J.D.; Higgins, D.G.; Gibson, T.J. CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res., 1994, 22, 4673-4680.
Karelson, M.; Dobchev, D. Using artificial neural networks to predict cell-penetrating compounds. Expert Opin. Drug Discov., 2011, 6, 783-796.
Dobchev, D.A.; Mager, I.; Tulp, I.; Karelson, G.; Tamm, T.; Tamm, K.; Janes, J.; Langel, U.; Karelson, M. Prediction of cell-penetrating peptides using artificial neural networks. Curr. Comput. Aided Drug Des., 2010, 6, 79-89.
Kalafatovic, D.; Giralt, E. Cell-Penetrating peptides: Design strategies beyond primary structure and amphipathicity. Molecules, 2017, 22, 1-38.
Nakariyakul, S.; Liu, Z.P.; Chen, L. A sequence-based computational approach to predicting PDZ domain- peptide interactions. Biochim. Biophys. Acta, 2014, 1844(1 Pt B), 165-170.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2019
Published on: 09 April, 2019
Page: [206 - 211]
Pages: 6
DOI: 10.2174/1573409914666180925100355
Price: $65

Article Metrics

PDF: 74