Feigin VL et al (2019) “Global, regional, and national burden of neurological disorders, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016,“. Lancet Neurol 18(5):459–480

Google Scholar 

Abbott NJ, Patabendige AA, Dolman DE, Yusof SR, Begley DJ (2010) Structure and function of the blood–brain barrier. Neurobiol Dis 37(1):13–25

CAS  PubMed  Google Scholar 

Zhou X, Smith QR, Liu X (2021) Brain penetrating peptides and peptide–drug conjugates to overcome the blood–brain barrier and target CNS diseases,. Wiley Interdisciplinary Reviews: Nanomedicine and Nanobiotechnology 13(4):e1695

PubMed  Google Scholar 

Stalmans S et al (2013) “Chemical-functional diversity in cell-penetrating peptides,“. PLoS ONE 8(8):e71752

CAS  PubMed  PubMed Central  Google Scholar 

Madani F, Lindberg S, Langel Ü, Futaki S, Gräslund A (2011) “Mechanisms of cellular uptake of cell-penetrating peptides,“ Journal of biophysics, vol. 2011

Yamano S et al (2014) “Long-term efficient gene delivery using polyethylenimine with modified Tat peptide,“ Biomaterials, vol. 35, no. 5, pp. 1705–1715, 2014/02/01/

Huwyler J, Wu D, Pardridge WM (1996) “Brain drug delivery of small molecules using immunoliposomes,“ Proceedings of the National Academy of Sciences, vol. 93, no. 24, pp. 14164–14169,

Knight A, Carvajal J, Schneider H, Coutelle C, Chamberlain S, Fairweather N (1999) Non-viral neuronal gene delivery mediated by the HC fragment of tetanus toxin,. Eur J Biochem 259(3):762–769

CAS  PubMed  Google Scholar 

El-Andaloussi S, Holm T, Langel U (2005) “Cell-penetrating peptides: mechanisms and applications,“. Curr Pharm Design 11(28):3597–3611

CAS  Google Scholar 

Milletti F (2012) “Cell-penetrating peptides: classes, origin, and current landscape,“. Drug Discovery Today 17:15–16

Google Scholar 

Lindgren M, Langel Ü (2011) “Classes and prediction of cell-penetrating peptides,“Cell-Penetrating Peptides,pp.3–19,

Stewart KM, Horton KL, Kelley SO (2008) “Cell-penetrating peptides as delivery vehicles for biology and medicine,“. Org Biomol Chem 6(13):2242–2255

CAS  PubMed  Google Scholar 

Mueller J, Kretzschmar I, Volkmer R, Boisguerin P (2008) Comparison of cellular uptake using 22 CPPs in 4 different cell lines. Bioconjug Chem 19(12):2363–2374

CAS  PubMed  Google Scholar 

Meade A, Meloni B, Mastaglia F, Knuckey N (2009) The application of cell penetrating peptides for the delivery of neuroprotective peptides/proteins in experimental cerebral ischaemia studies,. J Experimental Stroke Translational Med 2(1):22–40

CAS  Google Scholar 

Mathur D et al (2016) PEPlife: a repository of the half-life of peptides. Sci Rep 6(1):1–7

Google Scholar 

Stalmans S et al (2015) “Cell-penetrating peptides selectively cross the blood-brain barrier in vivo,“. PLoS ONE 10(10):e0139652

PubMed  PubMed Central  Google Scholar 

Wei L, Zhou C, Su R, Zou Q (2019) “PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning,“ Bioinformatics, vol. 35, no. 21, pp. 4272–4280,

Zhang YP, Zou Q (2020) “PPTPP: a novel therapeutic peptide prediction method using physicochemical property encoding and adaptive feature representation learning,“ Bioinformatics, vol. 36, no. 13, pp. 3982–3987,

Tang W et al (2022) “Identifying multi-functional bioactive peptide functions using multi-label deep learning,“. Brief Bioinform 23(1):bbab414

PubMed  Google Scholar 

Dai R et al (2021) “BBPpred: sequence-based prediction of blood-brain barrier peptides with feature representation learning and logistic regression,“. J Chem Inf Model 61(1):525–534

CAS  PubMed  Google Scholar 

Zou H (2021) “Identifying blood-brain barrier peptides by using amino acids physicochemical properties and features fusion method,“Peptide Science, p.e24247,

Kumar V, Patiyal S, Dhall A, Sharma N, Raghava GPS (2021) “B3pred: A random-forest-based method for predicting and designing blood–brain barrier penetrating peptides,“ Pharmaceutics, vol. 13, no. 8, p. 1237,

He W et al (2022) “Accelerating bioactive peptide discovery via mutual information-based meta-learning,“. Brief Bioinform 23(1):bbab499

PubMed  Google Scholar 

Kumar V et al (2021) “B3Pdb: an archive of blood–brain barrier-penetrating peptides,“. Brain Struct Function 226(8):2489–2495

CAS  Google Scholar 

Van Dorpe S et al (2012) “Brainpeps: the blood–brain barrier peptide database,“. Brain Struct Function 217(3):687–718

CAS  Google Scholar 

Boutet E et al (2016) “UniProtKB/Swiss-Prot, the manually annotated section of the UniProt KnowledgeBase: how to use the entry view,“. Plant Bioinformatics. Springer, pp 23–54

Charoenkwan P, Kanthawong S, Nantasenamat C, Hasan MM, Shoombuatong W (2020) “iDPPIV-SCM: A sequence-based predictor for identifying and analyzing dipeptidyl peptidase IV (DPP-IV) inhibitory peptides using a scoring card method,“. J Proteome Res 19(10):4125–4136

CAS  PubMed  Google Scholar 

Charoenkwan P, Chiangjong W, Lee VS, Nantasenamat C, Hasan MM, Shoombuatong W (2021) Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method. Sci Rep 11(1):1–13

Google Scholar 

Charoenkwan P, Chotpatiwetchkul W, Lee VS, Nantasenamat C, Shoombuatong W (2021) “A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides,“. Sci Rep 11(1):1–15

Google Scholar 

Vasylenko T et al (2016) “SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides,“. BMC Bioinformatics 17(19):203–217

Google Scholar 

Liou Y-F et al (2014) “SCMHBP: prediction and analysis of heme binding proteins using propensity scores of dipeptides,“. BMC Bioinformatics 15(16):1–14

Google Scholar 

Vasylenko T, Liou Y-F, Chen H-A, Charoenkwan P, Huang H-L, Ho S-Y (2015) “SCMPSP: Prediction and characterization of photosynthetic proteins based on a scoring card method,“ in BMC bioinformatics, vol. 16, no. 1, pp. 1–16: BioMed Central

Charoenkwan P, Kanthawong S, Nantasenamat C, Hasan MM, Shoombuatong W (2020) “iAMY-SCM: Improved prediction and analysis of amyloid proteins using a scoring card method with propensity scores of dipeptides,“ Genomics,

Charoenkwan P, Yana J, Nantasenamat C, Hasan MM, Shoombuatong W (2020) “iUmami-SCM: a novel sequence-based predictor for prediction and analysis of umami peptides using a scoring card method with propensity scores of dipeptides,“. J Chem Inf Model 60(12):6666–6678

CAS  PubMed  Google Scholar 

Charoenkwan P, Yana J, Schaduangrat N, Nantasenamat C, Hasan MM, Shoombuatong W (2020) “iBitter-SCM: Identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides,“ Genomics, vol. 112, no. 4, pp. 2813–2822,

Huang H-L et al (2012) “Prediction and analysis of protein solubility using a novel scoring card method with dipeptide composition,“ in BMC bioinformatics, vol. 13, no. 17, pp. 1–14: Springer

Charoenkwan P, Shoombuatong W, Lee H-C, Chaijaruwanich J, Huang H-L, Ho S-Y (2013) “SCMCRYS: predicting protein crystallization using an ensemble scoring card method with estimating propensity scores of P-collocated amino acid pairs,“. PLoS ONE 8(9):e72368

CAS  PubMed  PubMed Central  Google Scholar 

Charoenkwan P, Chiangjong W, Nantasenamat C, Hasan MM, Manavalan B, Shoombuatong W (2021) “StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides,“. Brief Bioinform 22(6):bbab172

PubMed  Google Scholar 

Charoenkwan P, Nantasenamat C, Hasan MM, Manavalan B, Shoombuatong W (2021) “BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides,“ Bioinformatics, vol. 37, no. 17, pp. 2556–2562,

Pedregosa F et al (2011) “Scikit-learn: Machine learning in Python,“. J Mach Learn Res 12:2825–2830

Google Scholar 

Azadpour M, McKay CM, Smith RL (2014) “Estimating confidence intervals for information transfer analysis of confusion matrices,“The Journal of the Acoustical Society of America, vol. 135, no. 3, pp. EL140-EL146,

Dao F-Y, Lv H, Zhang D, Zhang Z-M, Liu L, Lin H (2021) “DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops,“. Brief Bioinform 22(4):bbaa356

PubMed  Google Scholar 

Yang H et al (2020) “A comparison and assessment of computational method for identifying recombination hotspots in Saccharomyces cerevisiae,“. Brief Bioinform 21(5):1568–1580

PubMed  Google Scholar 

Dao F-Y et al (2019) “Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique,“ Bioinformatics, vol. 35, no. 12, pp. 2075–2083,

Chen W, Lv H, Nie F, Lin H (2019) “i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome,“ Bioinformatics, vol. 35, no. 16, pp. 2796–2800,

Lv H, Dao F-Y, Guan Z-X, Yang H, Li Y-W, Lin H (2021) Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method,. Brief Bioinform 22(4):bbaa255

PubMed  Google Scholar 

Xu Z-C, Feng P-M, Yang H, Qiu W-R, Chen W, Lin H (2019) “iRNAD: a computational tool for identifying D modification sites in RNA sequence,“ Bioinformatics, vol. 35, no. 23, pp. 4922–4929,

Varma S, Simon R (2006) Bias in error estimation when using cross-validation for model selection,. BMC Bioinformatics 7(1):1–8

Google Scholar 

Vabalas A, Gowen E, Poliakoff E, Casson AJ (2019) Machine learning algorithm validation with a limited sample size,. PLoS ONE 14(11):e0224365

CAS  PubMed  PubMed Central  Google Scholar 

Futaki S, Nakase I, Tadokoro A, Takeuchi T, Jones AT (2007) Arginine-rich peptides and their internalization mechanisms,. Biochem Soc Trans 35(4):784–787

CAS  PubMed  Google Scholar 

Ida H et al “Nanoscale Visualization of Morphological Alteration of Live-Cell Membranes by the Interaction with Oligoarginine Cell-Penetrating Peptides,“Analytical Chemistry, vol. 93, no. 13, pp.5383–5393, 2021/04/06 2021.

Kawaguchi Y et al “Dipicolylamine/Metal Complexes that Promote Direct Cell-Membrane Penetration of Octaarginine,“Bioconjugate Chemistry, vol. 30, no. 2, pp.454–460, 2019/02/20 2019.

Vazdar M et al “Arginine “Magic”: Guanidinium Like-Charge Ion Pairing from Aqueous Salts to Cell Penetrating Peptides,“Accounts of Chemical Research, vol. 51, no. 6, pp.1455–1464, 2018/06/19 2018.

Zou X, Rajendran M, Magda D, Miller LW “Cytoplasmic Delivery and Selective, Multicomponent Labeling with Oligoarginine-Linked Protein Tags,“Bioconjugate Chemistry, vol. 26, no. 3, pp.460–465, 2015/03/18 2015.

Münter R et al (2022) “Mechanisms of selective monocyte targeting by liposomes functionalized with a cationic, arginine-rich lipopeptide,“. Acta Biomater 144:96–108 2022/05/01/

PubMed  Google Scholar 

Bode SA, Timmermans SBPE, Eising S, van Gemert SPW, Bonger KM, Löwik DWPM (2019) Click to enter: activation of oligo-arginine cell-penetrating peptides by bioorthogonal tetrazine ligations,. Chem Sci 10(3):701–705. https://doi.org/10.1039/C8SC04394A

CAS  PubMed  Google Scholar 

Wu L-P et al “Crossing the blood-brain-barrier with nanoligand drug carriers self-assembled from a phage display peptide,“Nature Communications, vol. 10, no. 1, p.4635, 2019/10/11 2019.

Elmquist A, Hansen M, Langel Ü (2006) “Structure–activity relationship study of the cell-penetrating peptide pVEC,“ Biochimica et Biophysica Acta (BBA) - Biomembranes, vol. 1758, no. 6, pp. 721–729, 2006/06/01/

Oller-Salvia B, Sánchez-Navarro M, Giralt E, Teixido M (2016) Blood–brain barrier shuttle peptides: an emerging paradigm for brain delivery,. Chem Soc Rev 45(17):4690–4707

CAS  PubMed  Google Scholar 

Sarko D et al “The Pharmacokinetics of Cell-Penetrating Peptides,“Molecular Pharmaceutics, vol. 7, no. 6, pp.2224–2231, 2010/12/06 2010.

Yang NJ, Hinner MJ (2015) “Getting across the cell membrane: an overview for small molecules, peptides, and proteins,“ (in eng), Methods in molecular biology (Clifton, N.J.), vol. 1266, pp. 29–53,

Delaney JS “ESOL: Estimating Aqueous Solubility Directly from Molecular Structure,“Journal of Chemical Information and Computer Sciences, vol. 44, no. 3, pp.1000–1005, 2004/05/01 2004.

Ottaviani G, Gosling DJ, Patissier C, Rodde S, Zhou L, Faller B “What is modulating solubility in simulated intestinal fluids?,“ (in eng),European journal of pharmaceutical sciences: official journal of the European Federation for Pharmaceutical Sciences, vol. 41, no.3–4, pp. 452–457, 2010/11// 2010.

Ali J, Camilleri P, Brown MB, Hutt AJ, Kirton SB “Revisiting the General Solubility Equation: In Silico Prediction of Aqueous Solubility Incorporating the Effect of Topographical Polar Surface Area,“Journal of Chemical Information and Modeling, vol. 52, no. 2, pp.420–428, 2012/02/27 2012.

Falanga A et al “Enhanced uptake of gH625 by blood brain barrier compared to liver in vivo: characterization of the mechanism by an in vitro model and implications for delivery,“Scientific Reports, vol. 8, no. 1, p.13836, 2018/09/14 2018.

Stalmans S et al (2014) “Blood-Brain Barrier Transport of Short Proline-Rich Antimicrobial Peptides,“. Protein & Peptide Letters 21(4):399–406

CAS  Google Scholar 

Benrabh H, Lefauconnier JM “Blood-endothelial cell and blood-brain transport ofl-proline, α-aminoisobutyric acid, andl-alanine,“Neurochemical Research, vol. 21, no. 10, pp.1227–1235, 1996/10/01 1996.

Sánchez-Navarro M, Teixidó M, Giralt E “Jumping Hurdles: Peptides Able To Overcome Biological Barriers,“Accounts of Chemical Research, vol. 50, no. 8, pp.1847–1854, 2017/08/15 2017.

Gao J et al “Proline-Loaded Chitosan Nanoparticles Penetrate the Blood-Brain Barrier to Confer Neuroprotection in Mice Cerebral Ischemia Injury,“ Available at SSRN 4104458

Hau VS, Huber JD, Campos CR, Lipkowski AW, Misicka A, Davis TP (2002/10/01 2002) Effect of guanidino modification and proline substitution on the in vitro stability and blood–brain barrier permeability of endomorphin II,. J Pharm Sci 91(10):2140–2149. https://doi.org/10.1002/jps.10202

Rackovsky S, Scheraga H “Hydrophobicity, hydrophilicity, and the radial and orientational distributions of residues in native proteins,“ Proceedings of the National Academy of Sciences, vol. 74, no. 12, pp. 5248–5251, 1977

Fukuchi S, Nishikawa K (2001) Protein surface amino acid compositions distinctively differ between thermophilic and mesophilic bacteria,. J Mol Biol 309(4):835–843

CAS  PubMed  Google Scholar 

Qian N, Sejnowski TJ (1988) Predicting the secondary structure of globular proteins using neural network models,. J Mol Biol 202(4):865–884

CAS  PubMed  Google Scholar 

Ghasemy S, García-Pindado J, Aboutalebi F, Dormiani K, Teixidó M, Malakoutikhah M (2018) “Fine-tuning the physicochemical properties of peptide-based blood–brain barrier shuttles,“ Bioorganic & Medicinal Chemistry, vol. 26, no. 8, pp. 2099–2106, /05/01/ 2018

Costantino L, Gandolfi F, Tosi G, Rivasi F, Vandelli MA, Forni F (2005) Peptide-derivatized biodegradable nanoparticles able to cross the blood–brain barrier,. J Controlled Release 108(1):84–96

CAS  Google Scholar 

Clark DE (1999) “Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 2. Prediction of blood–brain barrier penetration,“. J Pharm Sci 88(8):815–821