Exploration of Immunology

Table 6. Cell-penetrating peptide (CPP) prediction from IGHV3-64 and K-Ras protein sequences.

Category	SVM score	Prediction	Hydrophobicity	Steric hindrance	Side bulk	Hydropathicity	Amphipathicity	Hydrophilicity	Net hydrogen	Charge	pI	Mol wt
IGHV3-64
MHWVRQAPGK (CPP starts @ 54 AA)	0.11	CPP	–0.24	0.56	0.56	–1.01	0.88	–0.10	1.00	2.50	11.01	1209.58
KGRFTISRDN (CPP @ 85 AA)	0.00	CPP	–0.49	0.67	0.67	–1.45	0.86	0.78	1.50	2.00	10.84	1193.46
K-Ras
HYREQIKRVK (CPP @ 95–104 AA)	0.09	CPP	–0.61	0.62	0.62	–1.96	1.62	0.91	1.70	3.50	10.29	1356.74
VREIRQYRLK (CPP @ 160 AA)	0.15	CPP	–0.59	0.67	0.67	–1.32	1.35	0.78	1.80	3.00	10.91	1360.77
REIRQYRLKK (CPP @ 161 AA)	0.52	CPP	–0.75	0.67	0.67	–2.13	1.72	1.23	2.00	4.00	11.01	1389.81
EIRQYRLKKI (CPP @ 162 AA)	0.32	CPP	–0.50	0.67	0.67	–1.23	1.48	0.75	1.60	3.00	10.29	1346.79
IRQYRLKKIS (CPP @ 163 AA)	0.04	CPP	–0.47	0.66	0.66	–0.96	1.35	0.48	1.60	4.00	11.10	1304.75
RQYRLKKISK (CPP @ 164 AA)	0.68	CPP	–0.65	0.65	0.65	–1.80	1.72	0.96	1.80	5.00	11.17	1319.76
RLKKISKEEK (CPP @ 167 AA)	0.12	CPP	–0.64	0.65	0.65	–1.96	1.97	1.77	1.50	3.00	10.01	1258.67
KKISKEEKTP (CPP @ 168 AA)	0.11	CPP	–0.54	0.62	0.62	–2.12	1.72	1.61	1.20	2.00	9.55	1187.54
KTPGCVKIKK (CPP @ 176 AA)	0.21	CPP	–0.32	0.63	0.63	–0.71	1.47	0.73	0.90	4.00	10.05	1101.56

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This table summarizes peptide fragments scanned from IGHV3-64 and K-Ras using the CellPPD prediction server. For each peptide, the AA sequence, positional range, support vector machine (SVM) score, and physicochemical properties are reported. Peptides with SVM scores > 0.0 were classified as CPP-positive according to the tool’s recommended threshold. These predictions were used to assess the theoretical feasibility of intracellular delivery and do not represent experimental confirmation of cell penetration. AA: amino acid; pI: isoelectric point; IGHV3-64: immunoglobulin heavy variable 3-64; K-Ras: Kirsten rat sarcoma viral oncogene homolog.

Supplementary materials

Other supplementary material for this article is available at: https://www.explorationpub.com/uploads/Article/file/1003252_sup_1.pdf.

Declarations

Acknowledgments

We would like to thank Mrs. J.S.Sakkthi Prabha for formatting and proofreading the manuscript.

Author contributions

RA: Conceptualization, Investigation, Methodology, Writing—original draft. VB, SK, JR, and RS: Writing—review & editing, Funding acquisition, Resources. All authors read and approved the submitted version.

Conflicts of interest

The authors declare that there are no conflicts of interest.

Ethical approval

The study was approved by the AVMC Institutional Human Ethics Committee (IHEC) under protocol No. AVMC-2021-04-14. The study was conducted in accordance with the principles of the Declaration of Helsinki (2024 revision).

Consent to participate

Written informed consent was obtained from all participants prior to sample collection.

Consent to publication

Not applicable.

Availability of data and materials

The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.

Funding

Seed funding support to conduct this research (VMRF/Research/Seed Money/30th April, 2021) was provided by VMRF. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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References

Xi Y, Xu P. Global colorectal cancer burden in 2020 and projections to 2040. Transl Oncol. 2021;14:101174. [DOI] [PubMed] [PMC]

Kobritz M, Nofi CP, Egunsola A, Zimmern AS. Financial toxicity in early-onset colorectal cancer: A National Health Interview Survey study. Surgery. 2024;175:1278–84. [DOI] [PubMed]

Braun T, Rade M, Merz M, Klepzig H, Große F, Fandrei D, et al. Multiomic profiling of T cell lymphoma after therapy with anti-BCMA CAR T cells and GPRC5D-directed bispecific antibody. Nat Med. 2025;31:1145–53. [DOI] [PubMed]

Keeratichamroen S, Subhasitanont P, Chokchaichamnankit D, Weeraphan C, Saharat K, Sritana N, et al. Identification of potential cervical cancer serum biomarkers in Thai patients. Oncol Lett. 2020;19:3815–26. [DOI] [PubMed] [PMC]

ProtParam [Internet]. Switzerland: SIB Swiss Institute of Bioinformatics; c2025 [cited 2025 Apr 12]. Available from: https://web.expasy.org/protparam

Liu Y, Yang X, Gan J, Chen S, Xiao ZX, Cao Y. CB-Dock2: improved protein-ligand blind docking by integrating cavity detection, docking and homologous template fitting. Nucleic Acids Res. 2022;50:W159–64. [DOI] [PubMed] [PMC]

Vita R, Blazeska N, Marrama D; IEDB Curation Team Members; Duesing S, Bennett J, Greenbaum J, De Almeida Mendes M, Mahita J, Wheeler DK, et al. The Immune Epitope Database (IEDB): 2024 update. Nucleic Acids Res. 2025;53:D436–43. [DOI] [PubMed] [PMC]

Kurcinski M, Badaczewska-Dawid A, Kolinski M, Kolinski A, Kmiecik S. Flexible docking of peptides to proteins using CABS-dock. Protein Sci. 2020;29:211–22. [DOI] [PubMed] [PMC]

Gautam A, Chaudhary K, Kumar R, Raghava GP. Computer-Aided Virtual Screening and Designing of Cell-Penetrating Peptides. Methods Mol Biol. 2015;1324:59–69. [DOI] [PubMed]

10.

Zhuang Y, Wang H, Jiang D, Li Y, Feng L, Tian C, et al. Multi gene mutation signatures in colorectal cancer patients: predict for the diagnosis, pathological classification, staging and prognosis. BMC Cancer. 2021;21:380. [DOI] [PubMed] [PMC]

11.

Meng M, Zhong K, Jiang T, Liu Z, Kwan HY, Su T. The current understanding on the impact of KRAS on colorectal cancer. Biomed Pharmacother. 2021;140:111717. [DOI] [PubMed]

12.

Chang DZ, Kumar V, Ma Y, Li K, Kopetz S. Individualized therapies in colorectal cancer: KRAS as a marker for response to EGFR-targeted therapy. J Hematol Oncol. 2009;2:18. [DOI] [PubMed] [PMC]

13.

Stefani C, Miricescu D, Stanescu-Spinu II, Nica RI, Greabu M, Totan AR, et al. Growth Factors, PI3K/AKT/mTOR and MAPK Signaling Pathways in Colorectal Cancer Pathogenesis: Where Are We Now? Int J Mol Sci. 2021;22:10260. [DOI] [PubMed] [PMC]

14.

Tang D, Kroemer G, Kang R. Oncogenic KRAS blockade therapy: renewed enthusiasm and persistent challenges. Mol Cancer. 2021;20:128. [DOI] [PubMed] [PMC]

15.

Zhu C, Guan X, Zhang X, Luan X, Song Z, Cheng X, et al. Targeting KRAS mutant cancers: from druggable therapy to drug resistance. Mol Cancer. 2022;21:159. [DOI] [PubMed] [PMC]

16.

Lu Y, Yang Y, Zhu G, Zeng H, Fan Y, Guo F, et al. Emerging Pharmacotherapeutic Strategies to Overcome Undruggable Proteins in Cancer. Int J Biol Sci. 2023;19:3360–82. [DOI] [PubMed] [PMC]

17.

Chiang ZC, Chiu YK, Lee CC, Hsu NS, Tsou YL, Chen HS, et al. Preparation and characterization of antibody-drug conjugates acting on HER2-positive cancer cells. PLoS One. 2020;15:e0239813. [DOI] [PubMed] [PMC]

18.

Polli JR, Balthasar JP. Cell Penetrating Peptides Conjugated to Anti-Carcinoembryonic Antigen “Catch-and-Release” Monoclonal Antibodies Alter Plasma and Tissue Pharmacokinetics in Colorectal Cancer Xenograft Mice. Bioconjug Chem. 2022;33:1456–66. [DOI] [PubMed] [PMC]

19.

Yu Z, Zhang X, Pei X, Cao W, Ye J, Wang J, et al. Antibody-siRNA conjugates (ARCs) using multifunctional peptide as a tumor enzyme cleavable linker mediated effective intracellular delivery of siRNA. Int J Pharm. 2021;606:120940. [DOI] [PubMed]

20.

Jin Z, Piao L, Sun G, Lv C, Jing Y, Jin R. Dual functional nanoparticles efficiently across the blood-brain barrier to combat glioblastoma via simultaneously inhibit the PI3K pathway and NKG2A axis. J Drug Target. 2021;29:323–35. [DOI] [PubMed]

21.

Su Z, Xiao D, Xie F, Liu L, Wang Y, Fan S, et al. Antibody-drug conjugates: Recent advances in linker chemistry. Acta Pharm Sin B. 2021;11:3889–907. [DOI] [PubMed] [PMC]

22.

Guidotti G, Brambilla L, Rossi D. Cell-Penetrating Peptides: From Basic Research to Clinics. Trends Pharmacol Sci. 2017;38:406–24. [DOI] [PubMed]

23.

Bousis D, Verras GI, Bouchagier K, Antzoulas A, Panagiotopoulos I, Katinioti A, et al. The role of deep learning in diagnosing colorectal cancer. Prz Gastroenterol. 2023;18:266–73. [DOI] [PubMed] [PMC]