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Methods of Protein Iodination
Published in Erwin Regoeczi, Iodine-Labeled Plasma Proteins, 2019
The enzyme occurs, apart from milk, also in the salivary and lacrimal glands.59 Cation- exchange chromatography is the method of choice to isolate it from milk. In a simplified version of the earlier Amberlite® IRC-50 method,60 the enzyme is batch-extracted from milk by using carboxymethyl cellulose, step-eluted from the exchanger, and, finally passed through a column of Sephadex® G-100.61 The A412/A280 value (see below) of the product thus obtained is 0.91 to 0.95. Further slight improvement (A412/A280 = 0.93 to 0.96) can be achieved62 by reversed hydrophobic chromatography using (NH4)2SO4.
Purification of Proteins/Peptides for Structural Studies
Published in Ajit S. Bhown, Protein/Peptide Sequence Analysis: Current Methodologies, 1988
Chromatographic results have indicated that glycosylated and nonglycosylated proteins, such as ribonucleases B and A, can be separated by RPC (results not shown).23 The only known difference between these two enzymes is the presence of a single, high-mannose-type oligosaccharide chain at an Asparagine residue in position 35. As Figure 7A shows, they can be separated by cation exchange chromatography. Here the main peak is ribonuclease B and the smaller peak (indicated by the asterisk) is the elution position of ribonuclease A.
Ceruloplasmin
Published in René Lontie, Copper Proteins and Copper Enzymes, 1984
The first purifications of Cp were achieved before chromatographic procedures were available and depended on precipitation steps.3,22 Later it was found that the protein was one of the most acidic in serum and binds to the top of an anion exchanger as a blue band if whole serum is applied. Most methods for Cp preparation depend on the use of columns of diethylaminoethyl cellulose or Sephadex®.23-27 Broman28 introduced hydroxyapatite chromatography in the preparation scheme when he discovered that it separated Cp into a major and a minor component (Section II.F.2). Apatite chromatography has, however, not been of general use in purifications, which implies that a large number of investigations have been carried out on a mixture of the two forms. Cation-exchange chromatography provides a final step to remove additional impurities.27,29 An alternative is gel filtration where aggregates and apoprotein elute slightly ahead of the main peak. In Uppsala we have employed a modified version30 of the method published by Broman and Kjellin,31 since it gives large amounts of pure protein in a comparatively short time (Scheme 1).
Design of antibody variable fragments with reduced reactivity to preexisting anti-drug antibodies
Published in mAbs, 2023
Maria U. Johansson, Christopher Weinert, Dietrich Alexander Reichardt, Dana Mahler, Dania Diem, Christian Hess, Diana Feusi, Simon Carnal, Julia Tietz, Noreen Giezendanner, Fabio Mario Spiga, David Urech, Stefan Warmuth
Charge variants were assessed during the stability study using analytical cation exchange chromatography. A 30 μg of scFv were injected onto a MabPac SCX-10 (ThermoFisher Scientific, #075602) using a Hitachi Chromaster HPLC system. Main peak and charge molecules were eluted applying a pH gradient from pH 4 to pH 11 within 15 min and a flowrate of 0.85 ml/min at 25°C. Composition of the mobile phase has previously been reported and consist of 15.6 mM CAPS (Sigma-Aldrich, #C2632), 9.4 mM CHES (AppliChem, #A1065), 4.6 mM TAPS (Sigma-Aldrich, #M8389), 9.9 mM HEPPSO (Molekula, #44974537), 8.7 mM MOPSO (Sigma-Aldrich, #M8389), 11 mM MES (AppliChem, #A0689), 13 mM acetic acid (AppliChem, #361008), and 9.9 mM formic acid (Honeywell, #09676). pH was adjusted to 4 and 11 using NaOH.30 Species eluting prior to or after the main peak were grouped as acidic and basic variants, respectively.
Harnessing the potential of machine learning for advancing “Quality by Design” in biomanufacturing
Published in mAbs, 2022
Ian Walsh, Matthew Myint, Terry Nguyen-Khuong, Ying Swan Ho, Say Kong Ng, Meiyappan Lakshmanan
The biological activity of protein biologics is often affected by a multitude of post-translational modifications that can influence charge distribution. Similar to aggregation, ML models can also play a role in predicting charge variants. Nikita et al. described a reinforcement ML algorithm where they formulated a maximization problem using cation exchange chromatography for separation of charge variants by optimization of the process flowrate.75 Mechanistic models such as general rate models were shown to predict elution peaks in ion-exchange process chromatography.76 The proposed model can be used to predict the separation of charge variants, allowing optimization and control of preparative scale chromatography. However, literature on models for charge variant characterization is limited and further work is required in this space, particularly to incorporate ML into the characterization.
In silico prediction of post-translational modifications in therapeutic antibodies
Published in mAbs, 2022
Developability assessments aim to identify candidates with long-term stability, manufacturability, and low heterogeneity.9 Forced degradation with thermal, pH, and light stress has been used to accelerate chemical degradation and identify liable residues.10 Peptide mapping can identify the specific sites for chemical modifications after forced degradation. In contrast, chromatographic techniques such as cation exchange chromatography and hydrophobic interaction chromatography can monitor the overall change in charge and hydrophilic variants, respectively.11 However, experimental approaches for identifying PTM liabilities are time-consuming and require high quantities of the purified protein.12 The sample preparation and data analysis for peptide mapping is incredibly labor-intensive.13 At earlier stages of drug development, the number of forced degradation conditions is limited by the low availability of the purified protein.10 Computational tools are becoming more common during developability assessments due to the low cost, lack of sample consumption, and high speed. In the past decade, computational tools have been used to predict PTM liable sites and engineer antibodies with better chemical stability.14