Parallel screening of selective and high-affinity displacers for proteins in ion-exchange systems

Rege, Kaushal; Ladiwala, Asif; Tugçu, Nihal; Breneman, Curt M.; Cramer, Steven M.

doi:10.1016/j.chroma.2003.12.071

Cited by 36 publications

(36 citation statements)

References 41 publications

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“…A Jupiter 5 m C4 300A column (4.6 mm × 50 mm) was purchased from Phenomenex (Torrance, CA). Ribonuclease A from bovine pancreas (RNaseA), ribonuclease B from bovine pancreas (RNaseB), ␣-chymotrypsinogen A from bovine pancreas (␣-ChyA), cytochrome C from equine heart (CytC), lysozyme from chicken egg white (Lys), conalbumin from chicken egg white (Conal), hemoglobin from bovine blood (Hemo), myoglobin from equine heart (Myo), avidin from chicken egg white, subtilisin A from Bacillus, elastase from porcine pancreas, papain from papaya latex, bromelain from pineapple stem, alcohol dehydrogenase from equine liver, trypsinogen from bovine pancreas, catalase from bovine liver, aprotinin from bovine lung, aconitase from porcine heart, albumin from bovine serum, neomycin sulfate (displacer 1), paromomycin sulfate (2), bekanamycin sulfate (3), amikacin sulfate (4), spermine (5), bis(hexamethylene)triamine (7), spermidine (8), 1,4-bis(3-aminopropyl)piperazine (9), diethylenetriamine (10), 4,7,10-trioxa-1,13-tridecanediamine (11), N,Ndiethyl-1,3-propanediamine (12), N,N-diethyldiethylenetriamine (13), 2-(2-aminoethylamino)ethanol (14), spectinomycin dihydrochloride pentahydrate (15), l-arginine methyl ester dihydrochloride (16), l-lysine methyl ester dihydrochloride (17), N-hexylethylenediamine (18), piperazine (19), cyclohexylamine (20), acetic acid (21), malonic acid (22), succinic acid (23), adipic acid (24), isocitric acid lactone (25), trans-aconitic acid (26), 1,2,4-butanetricarboxylic acid (27), 1,2,3,4-butanetetracarboxylic acid (28), glycine (29), 3-guanidinopropionic acid (30), 5-aminovaleric acid (31), pantothenic acid (32), aspartic acid (33), l-␤-homoglutamic acid hydrochloride (34), guanidinosuccinic acid (35), l-2,3-diaminopropionic acid hydrochloride (36), lysine (37), arginine (38), meso-2,3,-diaminosuccinic acid (39), ethylenediaminetetrapropionic acid (40), glycerol (41), threitol (42), adonitol (43), dulcitol (44), malic acid (45), tartaric acid (46), mucic acid …”

Section: Methodsmentioning

confidence: 99%

“…Mass action selective displacers typically have an affinity for the resin that lies between that of the two solutes being separated and can be readily predicted using the steric mass action formalism [23,24]. While selective displacement chromatography has been successfully employed in ion exchange systems [5,9,11,[14][15][16][17][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38], to date it has not been applied to CHA or any other multi-modal resin system.…”

Section: Introductionmentioning

confidence: 99%

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Unique selectivity windows using selective displacers/eluents and mobile phase modifiers on hydroxyapatite

Morrison

Gagnon²,

Cramer

2010

Journal of Chromatography A

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unique selectivity windows using selective displacers/eluents and mobile phase modifiers on hydroxyapatite

Morrison

Gagnon²,

Cramer

2010

Journal of Chromatography A

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“…When the solute properties are correlated with their chromatographic retention data, the models are referred to as Quantitative Structure Retention Relationships (QSRRs). We have recently employed predictive QSPR modeling, initially using linear partial least squares (PLS) regression and subsequently employing stateof-the-art non-linear Support Vector Machine (SVM) regression techniques (Song et al, 2002), to investigate protein retention (Mazza et al, 2001), effects of salt type on protein affinity (Ladiwala et al, 2003), and displacer efficacy and selectivity (Rege et al, 2004) in ion-exchange systems. The SVM method of non-linear regression has a number of important properties, including an effective avoidance of over-fitting through capacity control and regularization.…”

Section: Introductionmentioning

confidence: 99%

Investigation of protein retention and selectivity in HIC systems using quantitative structure retention relationship models

Ladiwala

Xia

Luo

et al. 2005

Biotech & Bioengineering

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In the present work, the effect of stationary phase resin chemistry and protein physicochemical properties on protein binding affinity in hydrophobic interaction chromatography (HIC) was investigated using linear gradient chromatography and quantitative structure-retention relationship (QSRR) modeling. Linear gradient experiments were carried out for a set of model proteins on four different HIC resins having different backbone and ligand chemistry. The retention data exhibited significant differences in protein binding affinity, not only across the phenyl and butyl ligand chemistries, but also for the different backbone chemistries found in the Sepharose (cross-linked agarose) and the Toyopearl 650 M (polymethacrylate) series of resins. QSRR models based on a Support Vector Machine (SVM) approach were developed for the linear retention data using molecular descriptors based on protein crystal structure and primary sequence information as well as a set of new hydrophobicity descriptors based on the solvent accessible protein surface area. The results indicate that the QSRR models were successfully able to capture and selectivity predict the changes observed in these systems. Furthermore, the new descriptors resulted in physically interpretable models of protein retention and provided insights into the factors influencing protein affinity in these different HIC systems. The approach put forth in this study provides a framework for developing predictive tools and for gaining insight into protein selectivity in hydrophobic interaction chromatography.

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“…We have recently used predictive QSPR modeling using support vector machine (SVM) regression to investigate protein retention (26), effects of salt type on protein affinity (27), and displacer efficacy and selectivity (28,29) in ion-exchange systems. The SVM method of nonlinear regression has many important properties, including an effective avoidance of overfitting through capacity control and regularization.…”

mentioning

confidence: 99%

A priori prediction of adsorption isotherm parameters and chromatographic behavior in ion-exchange systems

Ladiwala

Rege

Breneman

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

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The a priori prediction of protein adsorption behavior has been a long-standing goal in several fields. In the present work, propertymodeling techniques have been used for the prediction of protein adsorption thermodynamics in ion-exchange systems directly from crystal structure. Quantitative structure-property relationship models of protein isotherm parameters and Gibbs free energy changes in ion-exchange systems were generated by using a support vector machine regression technique. The predictive ability of the models was demonstrated for two test-set proteins not included in the model training set. Molecular descriptors selected during model generation were examined to gain insights into the important physicochemical factors influencing stoichiometry, equilibrium, steric effects, and binding affinity in protein ion-exchange systems. The a priori prediction of protein isotherm parameters can have direct implications for various ion-exchange processes. As proof of concept, a multiscale modeling approach was used for predicting the chromatographic separation of a test set of proteins using the isotherm parameters obtained from the quantitative structure-property relationship models. The simulated column separation showed good agreement with the experimental data. The ability to predict chromatographic behavior of proteins directly from their crystal structures may have significant implications for a range of biotechnology processes.ion-exchange chromatography ͉ protein adsorption ͉ structure-property relationships ͉ steric mass action ͉ support vector machines P rotein adsorption on surfaces is of fundamental importance in a wide array of applications, including biomedical implants (1), tissue engineering (2), food processing (3), biosensors (4), and bioseparations (5). In particular, protein adsorption on ionexchange surfaces has direct implications for bioseparation systems ranging from preparative chromatographic and membrane separations to high-throughput chromatographic and electrophoresisbased proteomic applications (6-8).Although protein adsorption in ion-exchange systems is dominated by Coulombic interactions, other molecular interactions such as hydrophobic and van der Waals interactions can play an important role in creating multimodal interactions and unique selectivities (9-13). Because protein binding in ion-exchange chromatography has been shown to be a reversible phenomenon with no significant associated conformational changes (14), the treatment of the protein as a colloid that reversibly adsorbs and desorbs is reasonable for modeling ion-exchange chromatographic systems. Several mechanistic models have attempted to model protein binding in ion exchange. Roth and Lenhoff (9) have computed the electrostatic and van der Waals energies of interaction between a colloidal protein molecule and a planar charged surface at a fixed distance and orientation. Roth et al. (10) The steric mass action (SMA) formalism (15, 16) has been used successfully to predict complex preparative chromatographic behavior ...

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Parallel screening of selective and high-affinity displacers for proteins in ion-exchange systems

Cited by 36 publications

References 41 publications

Unique selectivity windows using selective displacers/eluents and mobile phase modifiers on hydroxyapatite

Unique selectivity windows using selective displacers/eluents and mobile phase modifiers on hydroxyapatite

Investigation of protein retention and selectivity in HIC systems using quantitative structure retention relationship models

A priori prediction of adsorption isotherm parameters and chromatographic behavior in ion-exchange systems

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