Computational Design of Nanobody Binding to Cortisol to Improve Their Binding Affinity Using Molecular Docking and Molecular Dynamics Simulations

Umi Baroroh(1*), Nur Asni Setiani(2), Irma Mardiah(3), Dewi Astriany(4), Muhammad Yusuf(5)

(1) Department of Biotechnology Pharmacy, Indonesian School of Pharmacy, Bandung, 40266, West Java, Indonesia
(2) Department of Biotechnology Pharmacy, Indonesian School of Pharmacy, Bandung, 40266, West Java, Indonesia
(3) Department of Biotechnology Pharmacy, Indonesian School of Pharmacy, Bandung, 40266, West Java, Indonesia
(4) Department of Pharmacy, Indonesian School of Pharmacy, Bandung, 40266, West Java, Indonesia
(5) Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang, 45363, West Java, Indonesia Research Center for Molecular Biotechnology and Bioinformatics, Jl. Singaperbangsa No. 2, Bandung 40133, West Java, Indonesia
(*) Corresponding Author


Currently, nanobody binding cortisol has been deposited in the database. Unfortunately, the affinity is still in micromolar order. Substituting hydrophobic residues in the binding pocket and utilizing CDR2 and CDR3 is the strategy to improve the affinity. A single and double substitution at positions 53 and 101 have been introduced to the nanobody structure through molecular modeling. The affinity toward cortisol was evaluated using molecular docking to get the binding pose. The highest binding energy pose was used as the initial coordinate to analyze further using 100 ns molecular dynamics simulations. The binding affinities calculated by MMGBSA showed that MT3, MT5, and MT6 have better binding affinity than WT. In contrast, the ligand movement through MD simulations reveals that MT1, MT3, and MT5 are relatively stable. Hence, docking and MD simulations showed that MT3 is the best mutant than others. This mutant is substituting the threonine to isoleucine at position 53. New hydrophobic interactions occurred and caused the increase of binding. Eventually, this study provides valuable structural information to improve the binding affinity of nanobody binding cortisol for further development of this molecule to antibody-based biosensor design.



nanobody; cortisol; molecular dynamics simulations; molecular docking


[1] Hassanzadeh-Ghassabeh, G., Devoogdt, N., De Pauw, P., Vincke, C., and Muyldermans, S., 2013, Nanobodies and their potential applications, Nanomedicine, 8 (6), 1013–1026.

[2] Tabares-da Rosa, S., Wogulis, L.A., Wogulis, M.D., González-Sapienza, G., and Wilson, D.K., 2019, Structure and specificity of several triclocarban-binding single domain camelid antibody fragments, J. Mol. Recognit., 32 (1), e2755.

[3] Fanning, S.W., and Horn, J.R., 2011, An anti-hapten camelid antibody reveals a cryptic binding site with significant energetic contributions from a nonhypervariable loop, Protein Sci., 20 (7), 1196–1207.

[4] Huston, J.S., Levinson, D., Mudgett-Hunter, M., Tai, M.S., Novotný, J., Margolies, M.N., Ridge, R.J., Bruccoleri, R.E., Haber, E., Crea, R., and Oppermann, H., 1988, Protein engineering of antibody binding sites: Recovery of specific activity in an anti-digoxin single-chain Fv analogue produced in Escherichia coli, Proc. Natl. Acad. Sci. U. S. A., 85 (16), 5879–5883.

[5] Rosano, G.L., and Ceccarelli, E.A., 2014, Recombinant protein expression in Escherichia coli: Advances and challenges, Front. Microbiol., 5, 172.

[6] Löfblom, J., Frejd, F.Y., and Ståhl, S., 2011, Non-immunoglobulin based protein scaffolds, Curr. Opin. Biotechnol., 22 (6), 843–848.

[7] Muyldermans, S., 2013, Nanobodies: Natural single-domain antibodies, Annu. Rev. Biochem., 82 (1), 775–797.

[8] Bever, C.S., Dong, J.X., Vasylieva, N., Barnych, B., Cui, Y., Xu, Z.L., Hammock, B.D., and Gee, S.J., 2016, VHH antibodies: Emerging reagents for the analysis of environmental chemicals, Anal. Bioanal. Chem., 408 (22), 5985–6002.

[9] Spinelli, S., Frenken, L.G.J., Hermans, P., Verrips, T., Brown, K., Tegoni, M., and Cambillau, C., 2000, Camelid heavy-chain variable domains provide efficient combining sites to haptens, Biochemistry, 39 (6), 1217–1222.

[10] Ding, L., Wang, Z., Zhong, P., Jiang, H., Zhao, Z., Zhang, Y., Ren, Z., and Ding, Y., 2019, Structural insights into the mechanism of single domain VHH antibody binding to cortisol, FEBS Lett., 593 (11), 1248–1256.

[11] Corbalán-Tutau, D., Madrid, J.A., Nicolás, F., and Garaulet, M., 2014, Daily profile in two circadian markers “melatonin and cortisol” and associations with metabolic syndrome components, Physiol. Behav., 123, 231–235.

[12] Pasha, S.K., Kaushik, A., Vasudev, A., Snipes, S.A., and Bhansali, S., 2014, Electrochemical immunosensing of saliva cortisol, J. Electrochem. Soc., 161 (2), B3077.

[13] McEwen, B.S., 2002, Editorial: Cortisol, Cushing's syndrome, and a shrinking brain - New evidence for reversibility, J. Clin. Endocrinol. Metab., 87 (5), 1947–1948.

[14] Kaushik, A., Vasudev, A., Arya, S.K., Pasha, S.K., and Bhansali, S., 2014, Recent advances in cortisol sensing technologies for point-of-care application, Biosens. Bioelectron., 53, 499–512.

[15] Dalirirad, S., and Steckl, A.J., 2019, Aptamer-based lateral flow assay for point of care cortisol detection in sweat, Sens. Actuators, B, 283, 79–86.

[16] Dikme, O., and Dikme, O., 2019, Serum cortisol level as a useful predictor of surgical disease in patients with acute abdominal pain, Signa Vitae, 15 (1), 27–31.

[17] le Roux, C.W., Chapman, G.A., Kong, W.M., Dhillo, W.S., Jones, J., and Alaghband-Zadeh, J., 2003, Free cortisol index is better than serum total cortisol in determining hypothalamic-pituitary-adrenal status in patients undergoing surgery, J. Clin. Endocrinol. Metab., 88 (5), 2045–2048.

[18] Rice, P., Upasham, S., Jagannath, B., Manuel, R., Pali, M., and Prasad, S., 2019, CortiWatch: Watch-based cortisol tracker, Future Sci. OA, 5 (9), FSO416.

[19] Kaushik, A., Yndart, A., Jayant, R.D., Sagar, V., Atluri, V., Bhansali, S., and Nair, M., 2015, Electrochemical sensing method for point-of-care cortisol detection in human immunodeficiency virus-infected patients, Int. J. Nanomedicine, 10, 677–685.

[20] Frasconi, M., Mazzarino, M., Botrè, F., and Mazzei, F., 2009, Surface plasmon resonance immunosensor for cortisol and cortisone determination, Anal. Bioanal. Chem., 394 (8), 2151–2159.

[21] Zainol Abidin, A.S., Rahim, R.A., Md Arshad, M.K., Fatin Nabilah, M.F., Voon, C.H., Tang, T.H., and Citartan, M., 2017, Current and potential developments of cortisol aptasensing towards point-of-care diagnostics (POTC), Sensors, 17 (5), 1180.

[22] Fiser, A., and Šali, A., 2003, MODELLER: Generation and refinement of homology-based protein structure models, Methods Enzymol., 374, 461–491.

[23] Morris, G.M., Goodsell, D.S., Halliday, R.S., Huey, R., Hart, W.E., Belew, R.K., and Olson, A.J., 1998, Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function, J. Comput. Chem., 19 (14), 1639–1662.

[24] Morris, G.M., Huey, R., Lindstrom, W., Sanner, M.F., Belew, R.K., Goodsell, D.S., and Olson, A.J., 2009, AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility, J. Comput. Chem., 30 (16), 2785–2791.

[25] Baroroh, U., Yusuf, M., Rachman, S.D., Ishmayana, S., Hasan, K., and Subroto, T., 2019, Molecular dynamics study to improve the substrate adsorption of Saccharomycopsis fibuligera R64 alpha-amylase by designing a new surface binding site, Adv. Appl. Bioinf. Chem., 12, 1–13.

[26] Jakalian, A., Jack, D.B., and Bayly, C.I., 2002, Fast, efficient generation of high-quality atomic charges. AM1-BCC Model: II. Parameterization and validation, J. Comput. Chem., 23 (16), 1623–1641.

[27] Wang, J., Wolf, R.M., Caldwell, J.W., Kollman, P.A., and Case, D.A., 2004, Development and testing of a general amber force field, J. Comput. Chem., 25 (9), 1157–1174.

[28] Miller, B.R., Mcgee, T.D., Swails, J.M., Homeyer, N., Gohlke, H., and Roitberg, A.E., 2012, An efficient program for end-state free energy calculations, J. Chem. Theory Comput., 8 (9), 3314–3321.

[29] van Oss, C.J., Absolom, D.R., and Neumann, A.W., 1980, The “hydrophobic effect”: Essentially a van der Waals interaction, Colloid Polym. Sci., 258 (4), 424–427.

[30] Laskowski, R., MacArthur, M., Moss, D., and Thornton, J., 1993, PROCHECK: A program to check the stereochemical quality of protein structures, J. Appl. Crystallogr., 26 (2), 283–291.

[31] Hernández-Santoyo, A., Tenorio-Barajas, A.Y., Altuzar, V., Vivanco-Cid, H., and Mendoza-Barrera, C., 2013, "Protein-Protein and Protein-Ligand Docking" in Protein Engineering: Technology and Application, Eds. Tomohisa Ogawa, T., IntechOpen, London, 21.

[32] de Freitas, R.F., and Schapira, M., 2017, A systematic analysis of atomic protein-ligand interactions in the PDB, MedChemComm, 8 (10), 1970–1981.

[33] Sneha, P., and Priya Doss, C.G., 2016, Molecular dynamics: New frontier in personalized medicine, Adv. Protein Chem. Struct. Biol., 102, 181–224.


Article Metrics

Abstract views : 1791 | views : 797 | views : 558

Copyright (c) 2022 Indonesian Journal of Chemistry

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.


Indonesian Journal of Chemistry (ISSN 1411-9420 / 2460-1578) - Chemistry Department, Universitas Gadjah Mada, Indonesia.

Analytics View The Statistics of Indones. J. Chem.