A molecular modeling based method to predict elution behavior and binding patches of proteins in multimodal chromatography

Banerjee, Sanmitra; Parimal, Siddharth; Cramer, Steven M.

doi:10.1016/j.chroma.2017.06.059

Cited by 21 publications

(15 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sets of one, two, and three ligands were immobilized on an alkyl thiol SAM surface using the same setup as has been described by our group previously. , We summarize this setup here for completeness. Each ligand was immobilized by covalently bonding its base atom (shown in green in Figure ) to an alkyl thiol chain comprising 10 carbons with one sulfur and carbon atom at the base as shown in Figure .…”

Section: Methodsmentioning

confidence: 99%

Formation of Ligand Clusters on Multimodal Chromatographic Surfaces

et al. 2019

Self Cite

View full text Add to dashboard Cite

Multimodal chromatography is a powerful tool which uses multiple modes of interaction, such as charge and hydrophobicity, to purify protein-based therapeutics. In this work, we performed molecular dynamics simulations of a series of multimodal cation-exchange ligands immobilized on a hydrophilic self-assembled monolayer surface at the commercially relevant surface density (1 ligand/nm2). We found that ligands that were flexible and terminated in a hydrophobic group had a propensity to aggregate on the surface, while less flexible ligands containing a hydrophobic group closer to the surface did not aggregate. For aggregating ligands, this resulted in the formation of a surface pattern that contained relatively large patches of hydrophobicity and charge whose sizes exceeded the length scale of the individual ligands. On the other hand, lowering the surface density to 1 ligand/3 nm2 reduced or eliminated this aggregation behavior. In addition, the introduction of a flexible linker (corresponding to the commercially available ligand) enhanced cluster formation and allowed aggregation to occur at lower surface densities. Further, the use of flexible linkers enabled hydrophobic groups to collapse to the surface, reducing their accessibility. Finally, we developed an approach for quantifying differences in the observed surface patterns by calculating distributions of the patch size and patch length. This clustering phenomenon is likely to play a key role in governing protein–surface interactions in multimodal chromatography. This new understanding of multimodal surfaces has important implications for developing improved predictive models and designing new classes of multimodal separation materials.

show abstract

Section: Methodsmentioning

confidence: 99%

Formation of Ligand Clusters on Multimodal Chromatographic Surfaces

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Yu et al (2015) employed coarse‐grained simulations to investigate the preferred binding orientation of lysozyme on an HCIC surface at different ligand densities under a range of salt concentrations. Our lab has been actively involved in studying the preferred binding regions of small proteins in MM CEX systems by employing protein libraries (Chung et al, 2010), MD simulations (Banerjee et al, 2017; Freed et al, 2011; Parimal et al, 2015, 2017), atomic force microscopy (Srinivasan et al, 2017), and nuclear magnetic resonance (NMR) (Chung et al, 2010; Holstein et al, 2012, 2013; Srinivasan et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Identification of preferred multimodal ligand‐binding regions on IgG1 F_C using nuclear magnetic resonance and molecular dynamics simulations

Gudhka

Bilodeau

McCallum

et al. 2020

Biotech & Bioengineering

Self Cite

View full text Add to dashboard Cite

In this study, the binding of multimodal chromatographic ligands to the IgG1 FC domain were studied using nuclear magnetic resonance and molecular dynamics simulations. Nuclear magnetic resonance experiments carried out with chromatographic ligands and a perdeuterated 15N‐labeled FC domain indicated that while single‐mode ion exchange ligands interacted very weakly throughout the FC surface, multimodal ligands containing negatively charged and aromatic moieties interacted with specific clusters of residues with relatively high affinity, forming distinct binding regions on the FC. The multimodal ligand‐binding sites on the FC were concentrated in the hinge region and near the interface of the CH2 and CH3 domains. Furthermore, the multimodal binding sites were primarily composed of positively charged, polar, and aliphatic residues in these regions, with histidine residues exhibiting some of the strongest binding affinities with the multimodal ligand. Interestingly, comparison of protein surface property data with ligand interaction sites indicated that the patch analysis on FC corroborated molecular‐level binding information obtained from the nuclear magnetic resonance experiments. Finally, molecular dynamics simulation results were shown to be qualitatively consistent with the nuclear magnetic resonance results and to provide further insights into the binding mechanisms. An important contribution to multimodal ligand‐FC binding in these preferred regions was shown to be electrostatic interactions and π–π stacking of surface‐exposed histidines with the ligands. This combined biophysical and simulation approach has provided a deeper molecular‐level understanding of multimodal ligand–FC interactions and sets the stage for future analyses of even more complex biotherapeutics.

show abstract

“…The F C molecule was parameterized using the AMBER99 , force field, at pH 5.0, with a net charge of +15. SAM surfaces with immobilized Capto and Nuvia ligands were set up as described previously by our group. , SAM strands were built as alkyl thiol chains comprising 10 carbon atoms with one sulfur atom and a carbon atom at the base. Ligands were immobilized using a covalent bond between the base atom and the SAM alkyl chain.…”

Section: Materials and Methodsmentioning

confidence: 99%

Probing IgG1 F_C–Multimodal Nanoparticle Interactions: A Combined Nuclear Magnetic Resonance and Molecular Dynamics Simulations Approach

et al. 2021

Self Cite

View full text Add to dashboard Cite

In this study, NMR and molecular dynamics simulations were employed to study IgG1 F C binding to multimodal surfaces. Gold nanoparticles functionalized with two multimodal cation-exchange ligands (Capto and Nuvia) were synthesized and employed to carry out solutionphase NMR experiments with the F C . Experiments with perdeuterated 15 N-labeled F C and the multimodal surfaces revealed micromolar residuelevel binding affinities as compared to millimolar binding affinities with these ligands in free solution, likely due to cooperativity and avidity effects. The binding of F C with the Capto ligand nanoparticles was concentrated near an aliphatic cluster in the C H 2/C H 3 interface, which corresponded to a focused hydrophobic region. In contrast, binding with the Nuvia ligand nanoparticles was more diffuse and corresponded to a large contiguous positive electrostatic potential region on the side face of the F C . Results with lower-ligand-density nanoparticles indicated a decrease in binding affinity for both systems. For the Capto ligand system, several aliphatic residues on the F C that were important for binding to the higher-density surface did not interact with the lower-density nanoparticles. In contrast, no significant difference was observed in the interacting residues on the F C to the high-and low-ligand density Nuvia surfaces. The binding affinities of F C to both multimodal-functionalized nanoparticles decreased in the presence of salt due to the screening of multiple weak interactions of polar and positively charged residues. For the Capto ligand nanoparticle system, this resulted in an even more focused hydrophobic binding region in the interface of the C H 2 and C H 3 domains. Interestingly, for the Nuvia ligand nanoparticles, the presence of salt resulted in a large transition from a diffuse binding region to the same focused binding region determined for Capto nanoparticles at 150 mM salt. Molecular dynamics simulations corroborated the NMR results and provided important insights into the molecular basis of F C binding to these different multimodal systems containing clustered (observed at high-ligand densities) and nonclustered ligand surfaces. This combined biophysical and simulation approach provided significant insights into the interactions of F C with multimodal surfaces and sets the stage for future analyses with even more complex biotherapeutics.

show abstract

A molecular modeling based method to predict elution behavior and binding patches of proteins in multimodal chromatography

Cited by 21 publications

References 84 publications

Formation of Ligand Clusters on Multimodal Chromatographic Surfaces

Formation of Ligand Clusters on Multimodal Chromatographic Surfaces

Identification of preferred multimodal ligand‐binding regions on IgG1 F_C using nuclear magnetic resonance and molecular dynamics simulations

Probing IgG1 F_C–Multimodal Nanoparticle Interactions: A Combined Nuclear Magnetic Resonance and Molecular Dynamics Simulations Approach

Contact Info

Product

Resources

About

A molecular modeling based method to predict elution behavior and binding patches of proteins in multimodal chromatography

Cited by 21 publications

References 84 publications

Formation of Ligand Clusters on Multimodal Chromatographic Surfaces

Formation of Ligand Clusters on Multimodal Chromatographic Surfaces

Identification of preferred multimodal ligand‐binding regions on IgG1 FC using nuclear magnetic resonance and molecular dynamics simulations

Probing IgG1 FC–Multimodal Nanoparticle Interactions: A Combined Nuclear Magnetic Resonance and Molecular Dynamics Simulations Approach

Contact Info

Product

Resources

About

Identification of preferred multimodal ligand‐binding regions on IgG1 F_C using nuclear magnetic resonance and molecular dynamics simulations

Probing IgG1 F_C–Multimodal Nanoparticle Interactions: A Combined Nuclear Magnetic Resonance and Molecular Dynamics Simulations Approach