Protein-based therapeutics (PPTs) are drugs used to treat a variety of different conditions in the human body by alleviating enzymatic deficiencies, augmenting other proteins and drugs, modulating signal pathways, and more. However, many PPTs struggle from a short half-life due to degradation caused by irreversible protein aggregation in the bloodstream. Currently, the most researched strategies for improving the efficiency and longevity of PPTs are post-translational modifications (PTMs). The goal of our research was to determine which type of PTM increases longevity the most for each of three commonly-used therapeutic proteins by comparing the docking scores (DS) and binding free energies (BFE) from protein aggregation and reception simulations. DS and BFE values were used to create a quantitative index that outputs a relative number from ?1 to 1 to show reduced performance, no change, or increased performance. Results showed that methylation was the most beneficial for insulin (p < 0.1) and human growth hormone (p < 0.0001), and both phosphorylation and methylation were somewhat optimal for erythropoietin (p < 0.1 and p < 0.0001, respectively). Acetylation consistently provided the worst benefits with the most negative indices, while methylation had the most positive indices throughout. However, PTM efficacy varied between PPTs, supporting previous studies regarding how each PTM can confer different benefits based on the unique structures of recipient proteins.
References
[1]
Dimitrov, D.S. (2012) Therapeutic Proteins. In: Voynov, V. and Caravella, J.A., Eds., TherapeuticProteins: MethodsandProtocols, Humana Press, 1-26. https://doi.org/10.1007/978-1-61779-921-1_1
[2]
Roberts, C.J. (2014) Therapeutic Protein Aggregation: Mechanisms, Design, and Control. TrendsinBiotechnology, 32, 372-380. https://doi.org/10.1016/j.tibtech.2014.05.005
[3]
Pisal, D.S., Kosloski, M.P. and Balu-Iyer, S.V. (2010) Delivery of Therapeutic Proteins. JournalofPharmaceuticalSciences, 99, 2557-2575. https://doi.org/10.1002/jps.22054
[4]
Zaman, R., Islam, R.A., Ibnat, N., Othman, I., Zaini, A., Lee, C.Y., etal. (2019) Current Strategies in Extending Half-Lives of Therapeutic Proteins. JournalofControlledRelease, 301, 176-189. https://doi.org/10.1016/j.jconrel.2019.02.016
[5]
Conibear, A.C. (2020) Deciphering Protein Post-Translational Modifications Using Chemical Biology Tools. NatureReviewsChemistry, 4, 674-695. https://doi.org/10.1038/s41570-020-00223-8
[6]
Su, Y., Zhang, B., Sun, R., Liu, W., Zhu, Q., Zhang, X., etal. (2021) PLGA-Based Biodegradable Microspheres in Drug Delivery: Recent Advances in Research and Application. DrugDelivery, 28, 1397-1418. https://doi.org/10.1080/10717544.2021.1938756
[7]
Fu, C., Chen, Q., Zheng, F., Yang, L., Li, H., Zhao, Q., etal. (2018) Genetically Encoding a Lipidated Amino Acid for Extension of Protein Half‐Life inVivo. AngewandteChemieInternationalEdition, 58, 1392-1396. https://doi.org/10.1002/anie.201811837
[8]
Tan, H., Su, W., Zhang, W., Wang, P., Sattler, M. and Zou, P. (2019) Recent Advances in Half-Life Extension Strategies for Therapeutic Peptides and Proteins. CurrentPharmaceuticalDesign, 24, 4932-4946. https://doi.org/10.2174/1381612825666190206105232
[9]
Zhong, Q., Xiao, X., Qiu, Y., Xu, Z., Chen, C., Chong, B., etal. (2023) Protein Posttranslational Modifications in Health and Diseases: Functions, Regulatory Mechanisms, and Therapeutic Implications. MedComm, 4, e261. https://doi.org/10.1002/mco2.261
[10]
Lee, J.M., Hammarén, H.M., Savitski, M.M. and Baek, S.H. (2023) Control of Protein Stability by Post-Translational Modifications. NatureCommunications, 14, Article No. 201. https://doi.org/10.1038/s41467-023-35795-8
[11]
Landgraf, W. and Sandow, J. (2016) Recombinant Human Insulins—Clinical Efficacy and Safety in Diabetes Therapy. EuropeanEndocrinology, 12, 12-17.
[12]
Das, A., Shah, M. and Saraogi, I. (2022) Molecular Aspects of Insulin Aggregation and Various Therapeutic Interventions. ACSBio&MedChemAu, 2, 205-221. https://doi.org/10.1021/acsbiomedchemau.1c00054
[13]
Timofeev, V.I., Chuprov-Netochin, R.N., Samigina, V.R., Bezuglov, V.V., Miroshnikov, K.A. and Kuranova, I.P. (2010) X-Ray Investigation of Gene-Engineered Human Insulin Crystallized from a Solution Containing Polysialic Acid. ActaCrystallographicaSectionFStructuralBiologyandCrystallizationCommunications, 66, 259-263. https://doi.org/10.1107/s1744309110000461
[14]
Lou, M., Garrett, T.P.J., McKern, N.M., Hoyne, P.A., Epa, V.C., Bentley, J.D., etal. (2006) The First Three Domains of the Insulin Receptor Differ Structurally from the Insulin-Like Growth Factor 1 Receptor in the Regions Governing Ligand Specificity. ProceedingsoftheNationalAcademyofSciences, 103, 12429-12434. https://doi.org/10.1073/pnas.0605395103
[15]
Jacob, J., John, M., Jaison, V., Jain, K. and Kakkar, N. (2012) Erythropoietin Use and Abuse. IndianJournalofEndocrinologyandMetabolism, 16, 220-227. https://doi.org/10.4103/2230-8210.93739
[16]
Ghezlou, M., Mokhtari, F., Kalbasi, A., Riazi, G., Kaghazian, H., Emadi, R., etal. (2020) Aggregate Forms of Recombinant Human Erythropoietin with Different Charge Profile Substantially Impact Biological Activities. JournalofPharmaceuticalSciences, 109, 277-283. https://doi.org/10.1016/j.xphs.2019.05.036
[17]
Cheetham, J.C., Smith, D.M., Aoki, K.H., Stevenson, J.L., Hoeffel, T.J., Syed, R.S., etal. (1998) NMR Structure of Human Erythropoietin and a Comparison with Its Receptor Bound Conformation. NatureStructuralBiology, 5, 861-866. https://doi.org/10.1038/2302
[18]
Livnah, O., Stura, E.A., Middleton, S.A., Johnson, D.L., Jolliffe, L.K. and Wilson, I.A. (1999) Crystallographic Evidence for Preformed Dimers of Erythropoietin Receptor before Ligand Activation. Science, 283, 987-990. https://doi.org/10.1126/science.283.5404.987
[19]
Danowitz, M. and Grimberg, A. (2022) Clinical Indications for Growth Hormone Therapy. AdvancesinPediatrics, 69, 203-217. https://doi.org/10.1016/j.yapd.2022.03.005
[20]
Fradkin, A.H., Carpenter, J.F. and Randolph, T.W. (2009) Immunogenicity of Aggregates of Recombinant Human Growth Hormone in Mouse Models. JournalofPharmaceuticalSciences, 98, 3247-3264. https://doi.org/10.1002/jps.21834
[21]
Kuriakose, A., Chirmule, N. and Nair, P. (2016) Immunogenicity of Biotherapeutics: Causes and Association with Posttranslational Modifications. JournalofImmunologyResearch, 2016, Article ID: 1298473. https://doi.org/10.1155/2016/1298473
[22]
Chantalat, L., Jones, N.D., Korber, F., Navaza, J. and Pavlovsky, A.G. (1995) The Crystal Structure of Wild-Type Growth Hormone at 2.5 a Resolution. Protein&PeptideLetters, 2, 333-340. https://doi.org/10.2174/092986650202220524124754
[23]
Clackson, T., Ultsch, M.H., Wells, J.A. and de Vos, A.M. (1998) Structural and Functional Analysis of the 1:1 Growth Hormone: Receptor Complex Reveals the Molecular Basis for Receptor Affinity. JournalofMolecularBiology, 277, 1111-1128. https://doi.org/10.1006/jmbi.1998.1669
[24]
Seok, S. (2021) Structural Insights into Protein Regulation by Phosphorylation and Substrate Recognition of Protein Kinases/Phosphatases. Life, 11, Article No. 957. https://doi.org/10.3390/life11090957
[25]
Shang, S., Liu, J. and Hua, F. (2022) Protein Acylation: Mechanisms, Biological Functions and Therapeutic Targets. SignalTransductionandTargetedTherapy, 7, Article No. 396. https://doi.org/10.1038/s41392-022-01245-y
[26]
Christensen, D.G., Xie, X., Basisty, N., Byrnes, J., McSweeney, S., Schilling, B., etal. (2019) Post-Translational Protein Acetylation: An Elegant Mechanism for Bacteria to Dynamically Regulate Metabolic Functions. FrontiersinMicrobiology, 10, Article No. 1604. https://doi.org/10.3389/fmicb.2019.01604
[27]
Clarke, S.G. (2013) Protein Methylation at the Surface and Buried Deep: Thinking Outside the Histone Box. TrendsinBiochemicalSciences, 38, 243-252. https://doi.org/10.1016/j.tibs.2013.02.004
[28]
Berman, H.M. (2000) The Protein Data Bank. NucleicAcidsResearch, 28, 235-242. https://doi.org/10.1093/nar/28.1.235
[29]
Berman, H., Henrick, K. and Nakamura, H. (2003) Announcing the Worldwide Protein Data Bank. NatureStructural&MolecularBiology, 10, 980-980. https://doi.org/10.1038/nsb1203-980
[30]
Margreitter, C., Petrov, D. and Zagrovic, B. (2013) Vienna-PTM Web Server: A Toolkit for MD Simulations of Protein Post-Translational Modifications. NucleicAcidsResearch, 41, W422-W426. https://doi.org/10.1093/nar/gkt416
[31]
Margreitter, C., Reif, M.M. and Oostenbrink, C. (2017) Update on Phosphate and Charged Post‐Translationally Modified Amino Acid Parameters in the GROMOS Force Field. JournalofComputationalChemistry, 38, 714-720. https://doi.org/10.1002/jcc.24733
[32]
Petrov, D., Margreitter, C., Grandits, M., Oostenbrink, C. and Zagrovic, B. (2013) A Systematic Framework for Molecular Dynamics Simulations of Protein Post-Translational Modifications. PLOSComputationalBiology, 9, e1003154. https://doi.org/10.1371/journal.pcbi.1003154
[33]
Weng, G., Wang, E., Wang, Z., Liu, H., Zhu, F., Li, D., etal. (2019) Hawkdock: A Web Server to Predict and Analyze the Protein-Protein Complex Based on Computational Docking and MM/GBSA. NucleicAcidsResearch, 47, W322-W330. https://doi.org/10.1093/nar/gkz397
[34]
Zacharias, M. (2003) Protein-Protein Docking with a Reduced Protein Model Accounting for Side‐Chain Flexibility. ProteinScience, 12, 1271-1282. https://doi.org/10.1110/ps.0239303
[35]
Feng, T., Chen, F., Kang, Y., Sun, H., Liu, H., Li, D., etal. (2017) Hawkrank: A New Scoring Function for Protein-Protein Docking Based on Weighted Energy Terms. JournalofCheminformatics, 9, Article No. 66. https://doi.org/10.1186/s13321-017-0254-7
[36]
Hou, T., Qiao, X., Zhang, W. and Xu, X. (2002) Empirical Aqueous Solvation Models Based on Accessible Surface Areas with Implicit Electrostatics. TheJournalofPhysicalChemistryB, 106, 11295-11304. https://doi.org/10.1021/jp025595u
[37]
Kinnings, S.L., Liu, N., Tonge, P.J., Jackson, R.M., Xie, L. and Bourne, P.E. (2011) A Machine Learning-Based Method to Improve Docking Scoring Functions and Its Application to Drug Repurposing. JournalofChemicalInformationandModeling, 51, 408-419. https://doi.org/10.1021/ci100369f
[38]
Hou, T., Wang, J., Li, Y. and Wang, W. (2010) Assessing the Performance of the MM/PBSA and MM/GBSA Methods. 1. The Accuracy of Binding Free Energy Calculations Based on Molecular Dynamics Simulations. JournalofChemicalInformationandModeling, 51, 69-82. https://doi.org/10.1021/ci100275a
[39]
Sun, H., Li, Y., Tian, S., Xu, L. and Hou, T. (2014) Assessing the Performance of MM/PBSA and MM/GBSA Methods. 4. Accuracies of MM/PBSA and MM/GBSA Methodologies Evaluated by Various Simulation Protocols Using Pdbbind Data Set. PhysicalChemistryChemicalPhysics, 16, 16719-16729. https://doi.org/10.1039/c4cp01388c
[40]
Chen, F., Liu, H., Sun, H., Pan, P., Li, Y., Li, D., etal. (2016) Assessing the Performance of the MM/PBSA and MM/GBSA Methods. 6. Capability to Predict Protein-protein Binding Free Energies and Re-Rank Binding Poses Generated by Protein-protein Docking. PhysicalChemistryChemicalPhysics, 18, 22129-22139. https://doi.org/10.1039/c6cp03670h
[41]
Hata, H., Phuoc Tran, D., Marzouk Sobeh, M. and Kitao, A. (2021) Binding Free Energy of Protein/Ligand Complexes Calculated Using Dissociation Parallel Cascade Selection Molecular Dynamics and Markov State Model. BiophysicsandPhysicobiology, 18, 305-316. https://doi.org/10.2142/biophysico.bppb-v18.037
[42]
Genheden, S. and Ryde, U. (2015) The MM/PBSA and MM/GBSA Methods to Estimate Ligand-Binding Affinities. ExpertOpiniononDrugDiscovery, 10, 449-461. https://doi.org/10.1517/17460441.2015.1032936
[43]
NIST/SEMATECH e-Handbook of Statistical Methods. http://www.itl.nist.gov/div898/handbook/