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dc.rights.licenseRestricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.
dc.contributorCramer, Steven M.
dc.contributorGarde, Shekhar
dc.contributorTessier, Peter M.
dc.contributorRoush, David
dc.contributor.authorBanerjee, Suvrajit
dc.date.accessioned2021-11-03T08:43:46Z
dc.date.available2021-11-03T08:43:46Z
dc.date.created2017-01-13T09:58:32Z
dc.date.issued2016-12
dc.identifier.urihttps://hdl.handle.net/20.500.13015/1847
dc.descriptionDecember 2016
dc.descriptionSchool of Engineering
dc.description.abstractFinally, the strongest bound faces from the above method were analyzed and it was demonstrated that protein interactions with MM surfaces were always face-specific for all four proteins on the two MM surfaces. It was seen that complementary charge being present on a protein face was the sufficient criterion for this face to bind to MM surface, and hydrophobicity only synergistically added to the binding if it was present next to such a charged patch. This inspired us to apply our method for predicting the sensitivity of elution behavior of proteins to surface modifications. In order to do this, the strong binding regions on an antibody fragment (Fab), extensively studied in our group previously, to Capto MMC surface were determined. It was observed in a prior study from our group that a single amino acid mutation (hydrophilic to hydrophobic) in one of the predicted binding patches using our method increased retention significantly in gradient chromatography, thus, showing that our computational method may be applied to probe such sensitivity in elution to surface modifications. To conclude, future directions are discussed in terms of both fundamental and industrially relevant approaches. Specifically, the use of model systems to understand MM surface-protein interactions more deeply is recommended. A strategy for designing MM ligands is put forth. Also, utilization of the short MD/coarse-grained free energy method is advised to refine other, even more computationally efficient, predictive tools. A combination of all these techniques has the potential to help us understand and predict binding and elution behavior in MM systems more deeply and aid in the development of protein separation systems in downstream bioprocessing.
dc.description.abstractNext, a computational method was developed by combining short MD simulations and continuum solvent based coarse-grained free energy calculations to predict free energies of binding of the same protein faces studied through rigorous umbrella sampling simulations. The binding free energies from this method over predicted free energies from umbrella sampling. However, through suitable optimization of simulation time and coarse-graining parameters, the relationship between free energies from both methods were seen to be fairly linearly correlated. This new method was computationally much more efficient than umbrella sampling and was employed to calculate coarse-grained binding free energies of 101 faces each of proteins αChymotrypsinogen-A and Horse Cytochrome C on two different cation-exchange MM SAMs, Capto MMC and Nuvia cPrime, over a range of NaCl salt concentrations. Then, an iterative method was developed to calculate overall free energy of protein-MM surface binding and correlate these binding free energies to retention factors from isocratic chromatography. Subsequently, this correlation, combined with analytical expressions from the literature, was employed to correctly predict gradient elution salt concentrations for two other proteins, Ubiquitin and Ribonuclease-A, on Capto MMC and Nuvia cPrime and showed opposite selectivity trends in these two systems. Thus, a framework was established that could be used to predict selectivity in MM chromatography.
dc.description.abstractMultimodal (MM) chromatography provides a powerful means to enhance the selectivity of protein separations by taking advantage of multiple weak interactions that include electrostatic interactions, hydrophobic assembly and van der Waals interactions. The synergistic effect of these interactions is poorly understood and, consequently, it is challenging to predict protein retention behavior in these MM systems. In order to increase our understanding of such phenomena, the binding of proteins to MM chromatographic surfaces was examined using molecular modeling techniques. First, the umbrella sampling method was employed in all-atoms molecular dynamics (MD) simulations to ascertain the binding free energies of two faces of protein Ubiquitin to a Self Assembled Monolayer (SAM) presenting Capto MMC cation-exchange MM ligands. Suitable constraining methods were utilized to maintain a specific face of the protein exposed to the SAM. One face showed remarkably higher binding free energy than the other. The free energies thus obtained were compared to single molecule force spectroscopy experiments, of analogous faces of Ubiquitin performed previously in our group. This comparison showed the same trends in binding free energies from the two techniques, thus, validating our simulation methodology as well establishing a key connection between experiments and simulations at the single molecule scale. The same procedure was followed to evaluate the binding of two faces of Horse Cytochrome C to the Capto MMC SAM. Altogether, the four protein faces studied were chemically and topologically diverse in terms of surface charge and hydrophobicity. The simulations demonstrated the complex interplay of electrostatic and hydrophobic interactions involved during the binding process.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.subjectChemical and biological engineering
dc.titleProtein-surface interactions in multimodal chromatography: a molecular modeling based investigation
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid177872
dc.digitool.pid177873
dc.digitool.pid177874
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreePhD
dc.relation.departmentDept. of Chemical and Biological Engineering


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