Antibodies engineering by computational approach

Mujahed I. Mustafa

doi:10.31893/multirev.2022012

Keywords:

next generation antibodies

novel drugs

synthetic immunology

Abstract

In the pre-era of synthetic antibodies, pharmaceutical companies depended on finding novel drugs from medicinal plants and other traditional resources; while at present, technological advances in biology, computer, and robotics give researchers the ability to rewrite and edit DNA to synthesize extensive sets of drug candidates; these novel and improved candidates serve the basis for creating another library of drug candidates and so on until we find the right biomolecule for the disease of interest. All these technologies combined to synthesize therapeutic antibodies for many types of cancer, autoimmune diseases, and infectious diseases that can address diseases much more readily to very rapidly get therapeutics into patients so that we can potentially impact disease. The antibody mechanism is recognized, binds to disease cells, and pinpoints the immune system to attack those cells effectively. Now a day, they depend on a computational approach to guide and accelerate the process of antibodies engineering by a combination of a selection system and the use of high-throughput data acquisition and analysis to build and construct populations of next-generation antibodies that are thermo-stable, non-immunogenic as possible, and to be administered to many humans as possible. In this review, I will discuss the latest in silico methods for antibody engineering.
References
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2. Agostini F, Vendruscolo F, Tartaglia G (2011) Sequence-Based Prediction of Protein Solubility. J Mol Biol 421:237-41.
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14. Faulds D, Sorkin EM (1994) Abciximab (c7E3 Fab). A review of its pharmacology and therapeutic potential in ischaemic heart disease. Drugs 48:583-98.
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16. Gao S, Huang K, Tu H, Adler A (2013) Monoclonal antibody humanness score and its applications. BMC Biotechnol 13: 55.
17. Geering, B, Fussenegger M (2014) Synthetic immunology: Modulating the human immune system. Trends Biotechnol 33.
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19. Kuhlman B, Baker D (2000) Native protein sequences are close to optimal for their structures. Proc Natl Acad Sci USA 97:10383-8.
20. Kumar et al (2017) Biopharmaceutical Informatics: Supporting biologic drug development via molecular modelling and informatics. Journal of Pharmacy and Pharmacology 70.
21. Kuroda D et al (2012) Computer-aided antibody design. Protein engineering, design & selection: PEDS 25:507-22.
22. Kuroda D et al (2008) Structural classification of CDR-H3 revisited: A lesson in antibody modeling. Proteins 73:608-20.
23. Kuroda D, Tsumoto K (2020) Engineering Stability, Viscosity, and Immunogenicity of Antibodies by Computational Design. Journal of Pharmaceutical Sciences 109.
24. Li L et al (2014) Concentration Dependent Viscosity of Monoclonal Antibody Solutions: Explaining Experimental Behavior in Terms of Molecular Properties. Pharmaceutical research 31.
25. Locker K, HerrA (2020) Antibodies: Structure and Immune Effector Functions.
26. 'Monoclonal antibody, chimeric.' in. (2004) Larry W. Moreland (ed.), Rheumatology and Immunology Therapy (Springer Berlin Heidelberg: Berlin, Heidelberg).
27. Nichols P et al (2015) Rational design of viscosity reducing mutants of a monoclonal antibody: Hydrophobic versus electrostatic inter-molecular interactions. mAbs 7: 212-30.
28. Olimpieri P et al (2014) Tabhu: Tools for antibody humanization. Bioinformatics 31.
29. Perchiacca J et al (2012) Aggregation-resistant domain antibodies engineered with charged mutations near the edges of the complementarity-determining regions. Protein engineering, design & selection: PEDS 25:591-602.
30. Perchiacca J et al (2012) Engineering Aggregation-Resistant Antibodies. Annual Review of Chemical and Biomolecular Engineeringn 3:263-86.
31. Pham N, Meng W (2020) Protein Aggregation and Immunogenicity of Biotherapeutics. International Journal of Pharmaceutics 585:119523.
32. Plotnikov N et al (2017) Quantifying Risks of Asparagine Deamidation and Aspartate Isomerization in Biopharmaceuticals by Computing Reaction Free Energy Surfaces. The journal of physical chemistry. B 121.
33. Si W et al (2018) Synthetic immunology: T-cell engineering and adoptive immunotherapy. Synthetic and Systems Biotechnology 3.
34. Singh S (2020) Systems and Synthetic Immunology. doi: 10.1007/978-981-15-3350-1
35. Sormanni P et al (2014) The CamSol Method of Rational Design of Protein Mutants with Enhanced Solubility. J Mol Biol 427.
36. Stanfield R, Wilson I (2014) Antibody Structure. Microbiology Spectrum 2.
37. Sydow J et al (2014) Structure-Based Prediction of Asparagine and Aspartate Degradation Sites in Antibody Variable Regions. PLoS One 9:e100736.
38. Szechiński J et al (2008) Adalimumab - The first fully human monoclonal antibody used in the treatment of rheumatoid arthritis. Reumatologia 46:151-58.
39. Teplyakov A et al (2014) Antibody modeling assessment II: Structures and Models. Proteins: Structure, Function, and Bioinformatics 82.
40. Viola M et al (2018) Subcutaneous delivery of monoclonal antibodies: How do we get there? Journal of Controlled Release 286.
41. Webster AC et al (2017) Polyclonal and monoclonal antibodies for treating acute rejection episodes in kidney transplant recipients. The Cochrane database of systematic reviews 7:CD004756-CD56.
42. Zhao J et al (2018) In Silico Methods in Antibody Design. Antibodies 7:22.

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

How to cite

Mustafa, M. I. (2022). Antibodies engineering by computational approach. Multidisciplinary Reviews, 5(3), 2022012. https://doi.org/10.31893/multirev.2022012

[1] Abhinandan KR, Martin ACR (2007) Analyzing the “Degree of Humanness” of Antibody Sequences. J Mol Biol 369:852-62.

[2] Agostini F, Vendruscolo F, Tartaglia G (2011) Sequence-Based Prediction of Protein Solubility. J Mol Biol 421:237-41.

[3] Aledo J, Francisco R, Veredas F (2017). A machine learning approach for predicting methionine oxidation sites. BMC Bioinformatics 18.

[4] Sefik A (2019) The Discovery of Monoclonal Antibodies (On Georges Köhler). Allergy 74.

[5] Almagro J, Beavers M, Hernandez-Guzman F, Maier J, Shaulsky J, Butenhof K (2011) Antibody Modeling Assessment. Proteins 79:3050-66.

[6] Almagro, Juan, Alexey Teplyakov, Jinquan Luo, Raymond Sweet (2014) Second antibody modeling assessment (AMA-II). Proteins 82.

[7] Böldicke T (2018) Antibody Engineering.

[8] Bosch F, Laia R (2008) The contributions of Paul Ehrlich to pharmacology: a tribute on the occasion of the centenary of his Nobel Prize. Pharmacology 82:171-79.

[9] Choi Y, Verma D, Griswold K, Bailey-Kellogg C (2017) EpiSweep: Computationally Driven Reengineering of Therapeutic Proteins to Reduce Immunogenicity While Maintaining Function.

[10] Dahiyat B, Mayo S (1997) De Novo Protein Design: Fully Automated Sequence Selection. Science 278:82-87.

[11] Datta J (2020) Molecular modelling & simulation: a review from molecular informatics. doi: 10.6084/m9.figshare.12287606.v4

[12] Ecker D, Jones S, Levine H (2014) The Therapeutic Monoclonal Antibody Market. mAbs 7.

[13] Farhadi T, Fakharian A, Hashemian S (2017) Affinity Improvement of a Humanized Antiviral Antibody by Structure-Based Computational Design. International Journal of Peptide Research and Therapeutics, 25.

[14] Faulds D, Sorkin EM (1994) Abciximab (c7E3 Fab). A review of its pharmacology and therapeutic potential in ischaemic heart disease. Drugs 48:583-98.

[15] Freysd'ottir J (2000) Production of Monoclonal Antibodies. Methods in molecular medicine 40:267-79.

[16] Gao S, Huang K, Tu H, Adler A (2013) Monoclonal antibody humanness score and its applications. BMC Biotechnol 13: 55.

[17] Geering, B, Fussenegger M (2014) Synthetic immunology: Modulating the human immune system. Trends Biotechnol 33.

[18] Kramer R (2012) Toward a Molecular Understanding of Protein Solubility: Increased Negative Surface Charge Correlates with Increased Solubility. Biophysical journal 102:1907-15.

[19] Kuhlman B, Baker D (2000) Native protein sequences are close to optimal for their structures. Proc Natl Acad Sci USA 97:10383-8.

[20] Kumar et al (2017) Biopharmaceutical Informatics: Supporting biologic drug development via molecular modelling and informatics. Journal of Pharmacy and Pharmacology 70.

[21] Kuroda D et al (2012) Computer-aided antibody design. Protein engineering, design & selection: PEDS 25:507-22.

[22] Kuroda D et al (2008) Structural classification of CDR-H3 revisited: A lesson in antibody modeling. Proteins 73:608-20.

[23] Kuroda D, Tsumoto K (2020) Engineering Stability, Viscosity, and Immunogenicity of Antibodies by Computational Design. Journal of Pharmaceutical Sciences 109.

[24] Li L et al (2014) Concentration Dependent Viscosity of Monoclonal Antibody Solutions: Explaining Experimental Behavior in Terms of Molecular Properties. Pharmaceutical research 31.

[25] Locker K, HerrA (2020) Antibodies: Structure and Immune Effector Functions.

[26] 'Monoclonal antibody, chimeric.' in. (2004) Larry W. Moreland (ed.), Rheumatology and Immunology Therapy (Springer Berlin Heidelberg: Berlin, Heidelberg).

[27] Nichols P et al (2015) Rational design of viscosity reducing mutants of a monoclonal antibody: Hydrophobic versus electrostatic inter-molecular interactions. mAbs 7: 212-30.

[28] Olimpieri P et al (2014) Tabhu: Tools for antibody humanization. Bioinformatics 31.

[29] Perchiacca J et al (2012) Aggregation-resistant domain antibodies engineered with charged mutations near the edges of the complementarity-determining regions. Protein engineering, design & selection: PEDS 25:591-602.

[30] Perchiacca J et al (2012) Engineering Aggregation-Resistant Antibodies. Annual Review of Chemical and Biomolecular Engineeringn 3:263-86.

[31] Pham N, Meng W (2020) Protein Aggregation and Immunogenicity of Biotherapeutics. International Journal of Pharmaceutics 585:119523.

[32] Plotnikov N et al (2017) Quantifying Risks of Asparagine Deamidation and Aspartate Isomerization in Biopharmaceuticals by Computing Reaction Free Energy Surfaces. The journal of physical chemistry. B 121.

[33] Si W et al (2018) Synthetic immunology: T-cell engineering and adoptive immunotherapy. Synthetic and Systems Biotechnology 3.

[34] Singh S (2020) Systems and Synthetic Immunology. doi: 10.1007/978-981-15-3350-1

[35] Sormanni P et al (2014) The CamSol Method of Rational Design of Protein Mutants with Enhanced Solubility. J Mol Biol 427.

[36] Stanfield R, Wilson I (2014) Antibody Structure. Microbiology Spectrum 2.

[37] Sydow J et al (2014) Structure-Based Prediction of Asparagine and Aspartate Degradation Sites in Antibody Variable Regions. PLoS One 9:e100736.

[38] Szechiński J et al (2008) Adalimumab - The first fully human monoclonal antibody used in the treatment of rheumatoid arthritis. Reumatologia 46:151-58.

[39] Teplyakov A et al (2014) Antibody modeling assessment II: Structures and Models. Proteins: Structure, Function, and Bioinformatics 82.

[40] Viola M et al (2018) Subcutaneous delivery of monoclonal antibodies: How do we get there? Journal of Controlled Release 286.

[41] Webster AC et al (2017) Polyclonal and monoclonal antibodies for treating acute rejection episodes in kidney transplant recipients. The Cochrane database of systematic reviews 7:CD004756-CD56.

[42] Zhao J et al (2018) In Silico Methods in Antibody Design. Antibodies 7:22.

Abstract

References

How to cite