Research Article
BibTex RIS Cite

In Silico Analysis of H-Lys-Asp-OH Dipeptide: DFT Optimization, Frontier Orbitals, Electrostatic Potential, EGFR/HER2 Docking and ADMET Profiling

Year 2025, Volume: 46 Issue: 4, 974 - 989, 30.12.2025
https://doi.org/10.17776/csj.1636562

Abstract

Cancer remains a significant health problem as a pathological process driven by complex molecular mechanisms leading to cellular homeostasis disruption. In this complex process, abnormal activation of receptor tyrosine kinases stands out, with hyperactivation of Epidermal Growth Factor Receptor (EGFR) and Human Epidermal Growth Factor Receptor 2 (HER2) strongly associated with aggressive tumor phenotypes and poor clinical outcomes in many cancer types. Considering the resistance development and side effect problems frequently encountered in current targeted therapies, understanding the molecular interactions of this unique dual interaction mechanism dipeptide with EGFR and HER2 is critically important for developing new and effective treatment strategies. In this study, the structural characterization, receptor interactions, and pharmacokinetic properties of the H-Lys-Asp-OH dipeptide were investigated using computational methods. Using DFT/B3LYP/6-311++G(d,p), AutoDock Vina, QikProp, and OSIRIS methods, binding energies of -6.2 kcal/mol for EGFR and -6.3 kcal/mol for HER2, a HOMO-LUMO gap of 5.701 eV, LogP = -4.151, and 0% oral absorption were obtained. Despite poor oral bioavailability, this dipeptide acts as a promising scaffold for further optimization. These findings demonstrate the potential of H-Lys-Asp-OH as a dual EGFR/HER2 inhibitor scaffold and provide a framework for designing targeted therapeutics with improved selectivity profiles.

References

  • [1] Carpenter G., Cohen S., Epidermal growth factor, Annual Review of Biochemistry, 48(1) (1979) 193-216.
  • [2] Hynes N.E., Lane H.A., ERBB receptors and cancer: the complexity of targeted inhibitors, Nature Reviews Cancer, 5(5) (2005) 341-354.
  • [3] Yarden Y., Sliwkowski M.X., Untangling the ErbB signalling network, Nature Reviews Molecular Cell Biology, 2(2) (2001) 127-137.
  • [4] Lemmon M.A., Schlessinger J., Cell signaling by receptor tyrosine kinases, Cell, 141(7) (2010) 1117-1134.
  • [5] Baselga J., Swain S.M., Novel anticancer targets: revisiting ERBB2 and discovering ERBB3, Nature Reviews Cancer, 9(7) (2009) 463-475.
  • [6] Arteaga C.L., Engelman J.A., ERBB receptors: from oncogene discovery to basic science to mechanism-based cancer therapeutics, Cancer Cell, 25(3) (2014) 282-303.
  • [7] Slamon D.J., Leyland-Jones B., Shak S., Fuchs H., Paton V., Bajamonde A., Norton L., Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2, New England Journal of Medicine, 344(11) (2001) 783-792.
  • [8] Lynch T.J., Bell D.W., Sordella R., Gurubhagavatula S., Okimoto R.A., Brannigan B.W., Haber D.A., Activating mutations in the epidermal growth factor receptor underlying responsiveness of non–small-cell lung cancer to gefitinib, New England Journal of Medicine, 350(21) (2004) 2129-2139.
  • [9] EGF–ERBB signalling: towards the systems level, Nature Reviews Molecular Cell Biology, 7(7) (2006) 505-516.
  • [10] Manning G., Whyte D.B., Martinez R., Hunter T., Sudarsanam S., The protein kinase complement of the human genome, Science, 298(5600) (2002) 1912-1934.
  • [11] Kurrikoff K., Aphkhazava D., Langel Ü., The future of peptides in cancer treatment, Current Opinion in Pharmacology, 47 (2019) 27-32.
  • [12] Xie M., Liu D., Yang Y., Anti-cancer peptides: classification, mechanism of action, reconstruction and modification, Open Biology, 10(7) (2020) 200004.
  • [13] Zhang Q.Y., Yan Z.B., Meng Y.M., Hong X.Y., Shao G., Ma J.J., Fu C.Y., Antimicrobial peptides: mechanism of action, activity and clinical potential, Military Medical Research, 8(1) (2021) 48.
  • [14] Xiao W., Jiang W., Chen Z., Huang Y., Mao J., Zheng W., Shi J., Advance in peptide-based drug development: delivery platforms, therapeutics and vaccines, Signal Transduction and Targeted Therapy, 10(1) (2025) 74.
  • [15] Tan X., Liu Q., Fang Y., Zhu Y., Chen F., Zeng W., Dong J., Predicting peptide permeability across diverse barriers: a systematic investigation, Molecular Pharmaceutics, 21(8) (2024) 4116-4127.
  • [16] Adasme-Carreño F., Ochoa-Calle A., Galván M., Ireta J., Conformational preference of dipeptide zwitterions in aqueous solvents, Physical Chemistry Chemical Physics, 26(10) (2024) 8210-8218.
  • [17] Bursch M., Mewes J.M., Hansen A., Grimme S., Best-practice DFT protocols for basic molecular computational chemistry, Angewandte Chemie, 134(42) (2022) e202205735.
  • [18] Frisch M.J., Gaussian 09 Revision D. 01, Gaussian Inc., (2009).
  • [19] Koch W., Holthausen M.C., A chemist's guide to density functional theory, John Wiley & Sons, (2015).
  • [20] Berman H.M., Westbrook J., Feng Z., Gilliland G., Bhat T.N., Weissig H., Bourne P.E., The protein data bank, Nucleic Acids Research, 28(1) (2000) 235-242.
  • [21] Morris G.M., Huey R., Lindstrom W., Sanner M.F., Belew R.K., Goodsell D.S., Olson A.J., AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility, Journal of Computational Chemistry, 30(16) (2009) 2785-2791.
  • [22] Trott O., Olson A.J., AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, Journal of Computational Chemistry, 31 (2009) 455-461.
  • [23] Accelrys Software Inc., Discovery Studio Visualizer 2.0, (2005).
  • [24] Schrödinger LLC, Glide, (2021).
  • [25] Sander T., Freyss J., von Korff M., Reich J. R., Rufener C., OSIRIS, an entirely in-house developed drug discovery informatics system, Journal of Chemical Information and Modeling, 49(2) (2009) 232-246.
  • [26] Lipinski C.A., Lombardo F., Dominy B.W., Feeney P.J., Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, Advanced Drug Delivery Reviews, 23(1-3) (1997) 3-25.
  • [27] Frisch M.J., Gaussian 09 Revision D. 01, Gaussian Inc., (2009).
  • [28] Allen F.H., Kennard O., Watson D.G., Brammer L., Orpen A.G., Taylor R., Tables of bond lengths determined by X-ray and neutron diffraction. Part 1. Bond lengths in organic compounds, Journal of the Chemical Society, Perkin Transactions 2, (12) (1987) S1-S19.
  • [29] Engh R.A., Huber R., Accurate bond and angle parameters for X-ray protein structure refinement, Foundations of Crystallography, 47(4) (1991) 392-400.
  • [30] MacArthur M.W., Thornton J.M., Protein side-chain conformation: a systematic variation of χ1 mean values with resolution–a consequence of multiple rotameric states?, Biological Crystallography, 55(5) (1999) 994-1004.
  • [31] Laskowski R.A., MacArthur M.W., Moss D.S., Thornton J.M., PROCHECK: a program to check the stereochemical quality of protein structures, Applied Crystallography, 26(2) (1993) 283-291.
  • [32] Morris A.L., MacArthur M.W., Hutchinson E.G., Thornton J.M., Stereochemical quality of protein structure coordinates, Proteins: Structure, Function, and Bioinformatics, 12(4) (1992) 345-364.
  • [33] Kabsch W., Sander C., Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features, Biopolymers, 22(12) (1983) 2577-2637.
  • [34] Wang J., Wolf R.M., Caldwell J.W., Kollman P.A., Case D.A., Development and testing of a general amber force field, Journal of Computational Chemistry, 25(9) (2004) 1157-1174.
  • [35] Case D.A., Cheatham T.E., Darden T., Gohlke H., Luo R., Merz K.M., Woods R.J., The Amber biomolecular simulation programs, Journal of Computational Chemistry, 26(16) (2005) 1668-1688.
  • [36] Tomasi J., Mennucci B., Cammi R., Quantum mechanical continuum solvation models, Chemical Reviews, 105(8) (2005) 2999-3094.
  • [37] Pearson R.G., Chemical hardness and density functional theory, Journal of Chemical Sciences, 117(5) (2005) 369-377.
  • [38] Parr R.G., Szentpály L.V., Liu S., Electrophilicity index, Journal of the American Chemical Society, 121(9) (1999) 1922-1924.
  • [39] Murray J.S., Politzer P., The electrostatic potential: an overview, Wiley Interdisciplinary Reviews: Computational Molecular Science, 1(2) (2011) 153-163.
  • [40] Mennucci B., Polarizable continuum model, Wiley Interdisciplinary Reviews: Computational Molecular Science, 2(3) (2012) 386-404.
  • [41] Kitchen D.B., Decornez H., Furr J.R., Bajorath J., Docking and scoring in virtual screening for drug discovery: methods and applications, Nature Reviews Drug Discovery, 3(11) (2004) 935-949.
  • [42] Stamos J., Sliwkowski M.X., Eigenbrot C., Structure of the epidermal growth factor receptor kinase domain alone and in complex with a 4-anilinoquinazoline inhibitor, Journal of Biological Chemistry, 277(48) (2002) 46265-46272.
  • [43] Wood E.R., Truesdale A.T., McDonald O.B., Yuan D., Hassell A., Dickerson S.H., Shewchuk L., A unique structure for epidermal growth factor receptor bound to GW572016 (Lapatinib) relationships among protein conformation, inhibitor off-rate, and receptor activity in tumor cells, Cancer Research, 64(18) (2004) 6652-6659.
  • [44] Hawkins P.C., Skillman A.G., Nicholls A., Comparison of shape-matching and docking as virtual screening tools, Journal of Medicinal Chemistry, 50(1) (2007) 74-82.
  • [45] Böhm H.J., The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure, Journal of Computer-Aided Molecular Design, 8(3) (1994) 243-256.
  • [46] Jorgensen W.L., The many roles of computation in drug discovery, Science, 303(5665) (2004) 1813-1818.
  • [47] Gilson M.K., Zhou H.X., Calculation of protein-ligand binding affinities, Annu. Rev. Biophys. Biomol. Struct., 36(1) (2007) 21-42.
  • [48] Kollman P.A., Massova I., Reyes C., Kuhn B., Huo S., Chong L., Cheatham T.E., Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models, Accounts of Chemical Research, 33(12) (2000) 889-897.
  • [49] London N., Raveh B., Schueler-Furman O., Peptide docking and structure-based characterization of peptide binding: from knowledge to know-how, Current Opinion in Structural Biology, 23(6) (2013) 894-902.
  • [50] Pagadala N.S., Syed K., Tuszynski J., Software for molecular docking: a review, Biophysical Reviews, 9(2) (2017) 91-102.
  • [51] Mannhold R., Poda G.I., Ostermann C., Tetko I.V., Calculation of molecular lipophilicity: State-of-the-art and comparison of log P methods on more than 96,000 compounds, Journal of Pharmaceutical Sciences, 98(3) (2009) 861-893.
  • [52] Artursson P., Karlsson J., Correlation between oral drug absorption in humans and apparent drug permeability coefficients in human intestinal epithelial (Caco-2) cells, Biochemical and Biophysical Research Communications, 175(3) (1991) 880-885.
  • [53] Veber D.F., Johnson S.R., Cheng H.Y., Smith B.R., Ward K.W., Kopple K.D., Molecular properties that influence the oral bioavailability of drug candidates, Journal of Medicinal Chemistry, 45(12) (2002) 2615-2623.
  • [54] Ames B.N., McCann J., Yamasaki E., Methods for detecting carcinogens and mutagens with the Salmonella/mammalian-microsome mutagenicity test, Mutation Research, 31 (1975).
  • [55] Daina A., Michielin O., Zoete V., SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules, Scientific Reports, 7(1) (2017) 42717.
  • [56] Rezai T., Bock J.E., Zhou M.V., Kalyanaraman C., Lokey R.S., Jacobson M.P., Conformational flexibility, internal hydrogen bonding, and passive membrane permeability: successful in silico prediction of the relative permeabilities of cyclic peptides, Journal of the American Chemical Society, 128(43) (14073-14080).
  • [57] Vlieghe P., Lisowski V., Martinez J., Khrestchatisky M., Synthetic therapeutic peptides: science and market, Drug Discovery Today, 15(1-2) (2010) 40-56.
  • [58] Fosgerau K., Hoffmann T., Peptide therapeutics: current status and future directions, Drug Discovery Today, 20(1) (2015) 122-128.
  • [59] Mallinson J., Collins I., Macrocycles in new drug discovery, Future Medicinal Chemistry, 4(11) (1409-1438).
  • [60] Knudsen L.B., Lau J., The discovery and development of liraglutide and semaglutide, Frontiers in Endocrinology, 10 (2019) 155.
  • [61] Chatterjee J., Gilon C., Hoffman A., Kessler H., N-methylation of peptides: a new perspective in medicinal chemistry, Accounts of Chemical Research, 41(10) (1331-1342).
  • [62] Rautio J., Kumpulainen H., Heimbach T., Oliyai R., Oh D., Järvinen T., Savolainen J., Prodrugs: design and clinical applications, Nature Reviews Drug Discovery, 7(3) (2008) 255-270.
  • [63] Torchilin V.P., Recent advances with liposomes as pharmaceutical carriers, Nature Reviews Drug Discovery, 4(2) (2005) 145-160.
  • [64] Veronese F.M., Pasut G., PEGylation, successful approach to drug delivery, Drug Discovery Today, 10(21) (1451-1458).
  • [65] Craik D.J., Fairlie D.P., Liras S., Price D., The future of peptide-based drugs, Chemical Biology & Drug Design, 81(1) (136-147).
  • [66] Çelik S., Demirag A.D., Coşgun A.O., Özel A., Akyüz S., Computational investigation of the interaction mechanism of some anti-alzheimer drugs with the acetylcholinesterase enzyme, Open Journal of Nano, 8(1) (2023) 11-21.
There are 66 citations in total.

Details

Primary Language English
Subjects Atomic and Molecular Physics
Journal Section Research Article
Authors

Aliye Demet Demirağ 0000-0002-9609-9150

Submission Date February 10, 2025
Acceptance Date December 5, 2025
Publication Date December 30, 2025
Published in Issue Year 2025 Volume: 46 Issue: 4

Cite

APA Demirağ, A. D. (2025). In Silico Analysis of H-Lys-Asp-OH Dipeptide: DFT Optimization, Frontier Orbitals, Electrostatic Potential, EGFR/HER2 Docking and ADMET Profiling. Cumhuriyet Science Journal, 46(4), 974-989. https://doi.org/10.17776/csj.1636562

Editor