Patchy Charge Distribution Affects the pH in Protein Solutions during Dialysis
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články
PubMed
39964136
PubMed Central
PMC11887432
DOI
10.1021/acs.langmuir.4c04942
Knihovny.cz E-zdroje
- MeSH
- dialýza MeSH
- koncentrace vodíkových iontů MeSH
- proteiny * chemie MeSH
- roztoky MeSH
- simulace molekulární dynamiky MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- proteiny * MeSH
- roztoky MeSH
When using dialysis ultra- or diafiltration to purify protein solutions, a dialysis buffer in the permeate is employed to set the pH in the protein solution. Failure to achieve the target pH may cause undesired precipitation of the valuable product. However, the pH in the permeate differs from that in the retentate, which contains the proteins. Experimental optimization of the process conditions is time-consuming and expensive, while accurate theoretical predictions still pose a major challenge. Current models of dialysis account for the Donnan equilibrium, acid-base properties, and ion-protein interactions, but they neglect the patchy distribution of ionizable groups on the proteins and its impact on the solution properties. Here, we present a simple computational model of a colloidal particle with weakly acidic sites on the surface, organized in patches. This minimalistic model allows systematic variation of the relevant parameters, while simultaneously demonstrating the essential physics governing the acid-base equilibria in protein solutions. Using molecular simulations in the Grand-Reaction ensemble, we demonstrate that interactions between ionizable sites significantly affect the nanoparticle charge and thereby contribute to pH difference between the permeate and retentate. We show that the significance of this contribution increases if the ionizable sites are located on a smaller patch. Protein solutions are governed by the same physics as our simple model. In this context, our results show that models which aim to quantitatively predict the pH in protein solutions during dialysis need to account for the patchy distribution of ionizable sites on the protein surface.
Department of Physics NTNU Norwegian University of Science and Technology NO 7491 Trondheim Norway
Faculty of Physics University of Vienna Boltzmanngasse 5 1090 Vienna Austria
Vienna Doctoral School in Physics University of Vienna Boltzmanngasse 5 1090 Vienna Austrias
Zobrazit více v PubMed
Liderfelt J.; Royce J.. Biopharmaceutical Processing; Jagschies G.; Lindskog E.; Ła̧cki K.; Galliher P., Eds.; Elsevier, 2018; pp 441–453.
Zydney A. L. New developments in membranes for bioprocessing - A review. J. Membr. Sci. 2021, 620, 11880410.1016/j.memsci.2020.118804. DOI
Stoner M. R.; Fischer N.; Nixon L.; Buckel S.; Benke M.; Austin F.; Randolph T. W.; Kendrick B. S. Protein-solute interactions affect the outcome of ultrafiltration/diafiltration operations. J. Pharm. Sci. 2004, 93, 2332–2342. 10.1002/jps.20145. PubMed DOI
Bolton G. R.; Boesch A. W.; Basha J.; LaCasse D. P.; Kelley B. D.; Acharya H. Effect of protein and solution properties on the donnan effect during the ultrafiltration of proteins. Biotechnol. Prog. 2011, 27, 140–152. 10.1002/btpr.523. PubMed DOI
Briskot T.; Hillebrandt N.; Kluters S.; Wang G.; Studts J.; Hahn T.; Huuk T.; Hubbuch J. Modeling the Gibbs-Donnan effect during ultrafiltration and diafiltration processes using the Poisson-Boltzmann theory in combination with a basic Stern model. J. Membr. Sci. 2022, 648, 12033310.1016/j.memsci.2022.120333. DOI
Miao F.; Velayudhan A.; DiBella E.; Shervin J.; Felo M.; Teeters M.; Alred P. Theoretical analysis of excipient concentrations during the final ultrafiltration/diafiltration step of therapeutic antibody. Biotechnol. Prog. 2009, 25, 964–972. 10.1002/btpr.168. PubMed DOI
Teeters M.; Bezila D.; Benner T.; Alfonso P.; Alred P. Predicting diafiltration solution compositions for final ultrafiltration/diafiltration steps of monoclonal antibodies. Biotechnol. Bioeng. 2011, 108, 1338–1346. 10.1002/bit.23067. PubMed DOI
Baek Y.; Singh N.; Arunkumar A.; Borwankar A.; Zydney A. L. Mass Balance Model with Donnan Equilibrium Accurately Describes Unusual pH and Excipient Profiles during Diafiltration of Monoclonal Antibodies. Biotechnol. J. 2019, 14, 180051710.1002/biot.201800517. PubMed DOI
Jabra M. G.; Tao Y.; Moomaw J. F.; Yu Z.; Hotovec B. J.; Geng S. B.; Zydney A. L. pH and excipient profiles during formulation of highly concentrated biotherapeutics using bufferless media. Biotechnol. Bioeng. 2020, 117, 3390–3399. 10.1002/bit.27502. PubMed DOI
Ambrožič R.; Arzenšek D.; Podgornik A. Designing scalable ultrafiltration/diafiltration process of monoclonal antibodies via mathematical modeling by coupling mass balances and Poisson-Boltzmann equation. Biotechnol. Bioeng. 2021, 118, 633–646. 10.1002/bit.27598. PubMed DOI
Ladwig J. E.; Zhu X.; Rolandi P.; Hart R.; Robinson J.; Rydholm A. Mechanistic model of pH and excipient concentration during ultrafiltration and diafiltration processes of therapeutic antibodies. Biotechnol. Prog. 2020, 36, e299310.1002/btpr.2993. PubMed DOI
Hebbi V.; Roy S.; Rathore A. S.; Shukla A. Modeling and prediction of excipient and pH drifts during ultrafiltration/diafiltration of monoclonal antibody biotherapeutic for high concentration formulations. Sep. Purif. Technol. 2020, 238, 11639210.1016/j.seppur.2019.116392. DOI
Thakur G.; Hebbi V.; Rathore A. S. Near Infrared Spectroscopy as a PAT tool for monitoring and control of protein and excipient concentration in ultrafiltration of highly concentrated antibody formulations. Int. J. Pharm. 2021, 600, 12045610.1016/j.ijpharm.2021.120456. PubMed DOI
Adair G. S. On the Donnan Equilibrium and the Equation of Gibbs. Science 1923, 58, 13.10.1126/science.58.1488.13.a. PubMed DOI
Landsgesell J.; Beyer D.; Hebbeker P.; Košovan P.; Holm C. The pH-Dependent Swelling of Weak Polyelectrolyte Hydrogels Modeled at Different Levels of Resolution. Macromolecules 2022, 55, 3176–3188. 10.1021/acs.macromol.1c02489. DOI
Landsgesell J.; Hebbeker P.; Rud O.; Lunkad R.; Košovan P.; Holm C. Grand-Reaction Method for Simulations of Ionization Equilibria Coupled to Ion Partitioning. Macromolecules 2020, 53, 3007–3020. 10.1021/acs.macromol.0c00260. DOI
Harvey D.Modern Analytical Chemistry; McGraw Hill, 2000.
Nelson D. L.; Lehninger A. L.; Cox M. M.. Lehninger Principles of Biochemistry; Macmillan, 2008.
Po H. N.; Senozan N. M. The Henderson-Hasselbalch Equation: Its History and Limitations. J. Chem. Educ. 2001, 78, 1499.10.1021/ed078p1499. DOI
Petrucci R. H.; Herring F. G.; Madura J. D.; Bissonnette C.. General Chemistry: Principles and Modern Applications, 11th ed.; Pearson, 1997.
Boubeta F. M.; Soler-Illia G. J. A. A.; Tagliazucchi M.; et al. Electrostatically Driven Protein Adsorption: Charge Patches versus Charge Regulation. Langmuir 2018, 34, 15727–15738. 10.1021/acs.langmuir.8b03411. PubMed DOI
Yuan J.; Takae K.; Tanaka H. Impact of Charge Regulation on Self-Assembly of Zwitterionic Nanoparticles. Phys. Rev. Lett. 2022, 128, 15800110.1103/PhysRevLett.128.158001. PubMed DOI
Rathee V. S.; Zervoudakis A. J.; Sidky H.; Sikora B. J.; Whitmer J. K. Weak polyelectrolyte complexation driven by associative charging. J. Chem. Phys. 2018, 148, 11490110.1063/1.5017941. PubMed DOI
Rathee V. S.; Sidky H.; Sikora B. J.; Whitmer J. K. Role of Associative Charging in the Entropy-Energy Balance of Polyelectrolyte Complexes. J. Am. Chem. Soc. 2018, 140, 15319–15328. 10.1021/jacs.8b08649. PubMed DOI
Pineda S. P.; Staňo R.; Murmiliuk A.; Blanco P. M.; Montes P.; Tošner Z.; Groborz O.; Pánek J.; Hrubý M.; Štěpánek M.; Košovan P. Charge Regulation Triggers Condensation of Short Oligopeptides to Polyelectrolytes. JACS Au 2024, 4, 1775–1785. 10.1021/jacsau.3c00668. PubMed DOI PMC
Barroso da Silva F. L.; Lund M.; Jönsson B.; Åkesson T. On the Complexation of Proteins and Polyelectrolytes. J. Phys. Chem. B 2006, 110, 4459–4464. 10.1021/jp054880l. PubMed DOI
Barroso da Silva F. L.; MacKernan D. Benchmarking a Fast Proton Titration Scheme in Implicit Solvent for Biomolecular Simulations. J. Chem. Theory Comput. 2017, 13, 2915–2929. 10.1021/acs.jctc.6b01114. PubMed DOI
Lund M.; Jönsson B. Charge regulation in biomolecular solution. Q. Rev. Biophys. 2013, 46, 265–268. 10.1017/S003358351300005X. PubMed DOI
Ausserwöger H.; Krainer G.; Welsh T. J.; Thorsteinson N.; De Csilléry E.; Sneideris T.; Schneider M. M.; Egebjerg T.; Invernizzi G.; Herling T. W.; Lorenzen N.; Knowles T. P. J. Surface patches induce nonspecific binding and phase separation of antibodies. Proc. Natl. Acad. Sci. U.S.A. 2023, 120, e221033212010.1073/pnas.2210332120. PubMed DOI PMC
Jacobs M. I.; Bansal P.; Shukla D.; Schroeder C. M. Understanding Supramolecular Assembly of Supercharged Proteins. ACS Cent. Sci. 2022, 8, 1350–1361. 10.1021/acscentsci.2c00730. PubMed DOI PMC
Kim J.; Qin S.; Zhou H.-X.; Rosen M. K. Surface Charge Can Modulate Phase Separation of Multidomain Proteins. J. Am. Chem. Soc. 2024, 146, 3383–3395. 10.1021/jacs.3c12789. PubMed DOI PMC
Teixeira A. A. R.; Lund M.; Barroso da Silva F. L. Fast Proton Titration Scheme for Multiscale Modeling of Protein Solutions. J. Chem. Theory Comput. 2010, 6, 3259–3266. 10.1021/ct1003093. PubMed DOI
Lošdorfer Božič A.; Podgornik R. pH Dependence of Charge Multipole Moments in Proteins. Biophys. J. 2017, 113, 1454–1465. 10.1016/j.bpj.2017.08.017. PubMed DOI PMC
Božič A.; Podgornik R. Site Correlations, Capacitance, and Polarizability From Protein Protonation Fluctuations. J. Phys. Chem. B 2021, 125, 12902–12908. 10.1021/acs.jpcb.1c08200. PubMed DOI
Podgornik R. General theory of charge regulation and surface differential capacitance. J. Chem. Phys. 2018, 149, 10470110.1063/1.5045237. PubMed DOI
Avni Y.; Andelman D.; Podgornik R. Charge regulation with fixed and mobile charged macromolecules. Curr. Opin. Electrochem. 2019, 13, 70–77. 10.1016/j.coelec.2018.10.014. DOI
Katchalsky A.; Gillis J. Theory of the potentiometric titration of polymeric acids. Recl. Trav. Chim. Pays-Bas 1949, 68, 879.10.1002/recl.19490680912. DOI
Katchalsky A.; Lifson S.; Mazur J. The electrostatic free energy of polyelectrolyte solutions. I. Randomly kinked macromolecules. J. Polym. Sci. 1953, 11, 409–423. 10.1002/pol.1953.120110503. DOI
Arnold R. The titration of polymeric acids. J. Colloid Sci. 1957, 12, 549–556. 10.1016/0095-8522(57)90060-0. DOI
Mandel M. The potentiometric titration of weak polyacids. Eur. Polym. J. 1970, 6, 807–822. 10.1016/0014-3057(70)90005-4. DOI
Ullner M.; Jönsson B.; Widmark P. Conformational properties and apparent dissociation constants of titrating polyelectrolytes: Monte Carlo simulation and scaling arguments. J. Chem. Phys. 1994, 100, 3365.10.1063/1.466378. DOI
Ullner M.; Jönsson B.; Söderberg B.; Peterson C. A Monte Carlo study of titrating polyelectrolytes. J. Chem. Phys. 1996, 104, 3048–3057. 10.1063/1.471071. DOI
Borkovec M.; Koper G. J. M. A Cluster Expansion Method for the Complete Resolution of Microscopic Ionization Equilibria from NMR Titrations. Anal. Chem. 2000, 72, 3272–3279. 10.1021/ac991494p. PubMed DOI
Avni Y.; Podgornik R.; Andelman D. Critical behavior of charge-regulated macro-ions. J. Chem. Phys. 2020, 153, 02490110.1063/5.0011623. PubMed DOI
Ferrari S.; Bianchi E.; Kahl G. Spontaneous assembly of a hybrid crystal-liquid phase in inverse patchy colloid systems. Nanoscale 2017, 9, 1956–1963. 10.1039/C6NR07987C. PubMed DOI PMC
Bianchi E.; van Oostrum P. D.; Likos C. N.; Kahl G. Inverse patchy colloids: Synthesis, modeling and self-organization. Curr. Opin. Colloid Interface Sci. 2017, 30, 8–15. 10.1016/j.cocis.2017.03.010. DOI
Notarmuzi D.; Bianchi E.. Liquid-liquid phase separation driven by charge heterogeneity. arXiv:2401.10655. arXiv.org e-Print archive. https://arxiv.org/abs/2401.10655, 2024. PubMed PMC
Dempster J. M.; Olvera De La Cruz M. Aggregation of Heterogeneously Charged Colloids. ACS Nano 2016, 10, 5909–5915. 10.1021/acsnano.6b01218. PubMed DOI
Popa I.; Papastavrou G.; Borkovec M. Charge regulation effects on electrostatic patch-charge attraction induced by adsorbed dendrimers. Phys. Chem. Chem. Phys. 2010, 12, 4863.10.1039/b925812d. PubMed DOI
da Silva F. L. B.; Jönsson B. Polyelectrolyte-protein complexation driven by charge regulation. Soft Matter 2009, 5, 2862.10.1039/b902039j. DOI
Blanco P. M.; Achetoni M. M.; Garcés J. L.; Madurga S.; Mas F.; Baieli M. F.; Narambuena C. F. Adsorption of flexible proteins in the ‘wrong side’ of the isoelectric point: casein macropeptide as a model system. Colloids Surf., B 2022, 217, 11261710.1016/j.colsurfb.2022.112617. PubMed DOI
Henzler K.; Haupt B.; Lauterbach K.; Wittemann A.; Borisov O.; Ballauff M. Adsorption of β-actoglobulin on spherical polyelectrolyte brushes: Direct proof of counterion release by isothermal titration calorimetry. J. Am. Chem. Soc. 2010, 132, 3159–3163. 10.1021/ja909938c. PubMed DOI
Yigit C.; Kanduč M.; Ballauff M.; Dzubiella J. Interaction of Charged Patchy Protein Models with Like-Charged Polyelectrolyte Brushes. Langmuir 2017, 33, 417–427. 10.1021/acs.langmuir.6b03797. PubMed DOI
Lunkad R.; Barroso da Silva F. L.; Košovan P. Both Charge-Regulation and Charge-Patch Distribution Can Drive Adsorption on the Wrong Side of the Isoelectric Point. J. Am. Chem. Soc. 2022, 144, 1813–1825. 10.1021/jacs.1c11676. PubMed DOI
Staňo R.; Košovan P.; Tagliabue A.; Holm C. Electrostatically Cross-Linked Reversible Gels–Effects of pH and Ionic Strength. Macromolecules 2021, 54, 4769–4781. 10.1021/acs.macromol.1c00470. DOI
Košovan P.; Richter T.; Holm C. Modelling of polyelectrolyte gels in equilibrium with salt solutions. Macromolecules 2015, 48, 7698–7708. 10.1021/acs.macromol.5b01428. DOI
Hebbeker P.; Blanco P. M.;Uhlík F.; Kosovan P.. Finite-Size Effects in Simulations of Chemical Reactions. 2023; https://chemrxiv.org/engage/chemrxiv/article-details/64da8a8969bfb8925aef5685.
Hockney R. W.; Eastwood J. W.. Computer Simulation Using Particles; Taylor & Francis, New York, 1988.
Deserno M.; Holm C. How to mesh up Ewald sums. I. A theoretical and numerical comparison of various particle mesh routines. J. Chem. Phys. 1998, 109, 7678–7693. 10.1063/1.477414. DOI
Deserno M.; Holm C. How to mesh up Ewald sums. II. An accurate error estimate for the particle–particle–particle-mesh algorithm. J. Chem. Phys. 1998, 109, 7694–7701. 10.1063/1.477415. DOI
Weeber R.; Grad J.-N.; Beyer D.; Blanco P. M.; Kreissl P.; Reinauer A.; Tischler I.; Košovan P.; Holm C.. Reference Module in Chemistry, Molecular Sciences and Chemical Engineering; Elsevier, 2023.
Smith W. R.; Tříska B. The reaction ensemble method for the computer simulation of chemical and phase equilibria. I. Theory and basic examples. J. Chem. Phys. 1994, 100, 3019–3027. 10.1063/1.466443. DOI
Weik F.; Weeber R.; Szuttor K.; Breitsprecher K.; de Graaf J.; Kuron M.; Landsgesell J.; Menke H.; Sean D.; Holm C. ESPResSo 4.0 - an extensible software package for simulating soft matter systems. Eur. Phys. J. Spec. Top. 2019, 227, 1789–1816. 10.1140/epjst/e2019-800186-9. DOI
Janke W.Statistical Analysis of Simulations: Data Correlations and Error Estimation. In Quantum Simulations of Complex Many-Body Systems: Theory To Algorithm 2002; pp 423–445.
Beyer D.; Košovan P.; Holm C. Simulations Explain the Swelling Behavior of Hydrogels with Alternating Neutral and Weakly Acidic Blocks. Macromolecules 2022, 55, 10751–10760. 10.1021/acs.macromol.2c01916. DOI
Wennerström H.; Vallina Estrada E.; Danielsson J.; Oliveberg M. Colloidal stability of the living cell. Proc. Natl. Acad. Sci. U.S.A. 2020, 117, 10113–10121. 10.1073/pnas.1914599117. PubMed DOI PMC
Arkhipov A.; Schulten K.; Freddolino P.; Ying Y.; Shih A.; Chen Z.. Coarse-Graining of Condensed Phase and Biomolecular Systems; Voth G., Ed.; CRC Press, 2008; pp 299–315.