Kirkwood–Buff-Derived Alcohol Parameters for Aqueous Carbohydrates and Their Application to Preferential Interaction Coefficient Calculations of Proteins

Cloutier, Theresa K.; Sudrik, Chaitanya; Sathish, Hasige A.; Trout, Bernhardt L.

doi:10.1021/acs.jpcb.8b07623

Cited by 20 publications

(35 citation statements)

References 49 publications

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“… 39 Modified CHARMM36 force fields calibrated with thermodynamic solution data have recently been suggested to resolve this overaggregative nature. 41 , 42 …”

Section: Introductionmentioning

confidence: 99%

Properties of Aqueous Trehalose Mixtures: Glass Transition and Hydrogen Bonding

Olgenblum

Sapir

Harries

2020

J. Chem. Theory Comput.

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Trehalose is a naturally occurring disaccharide known to remarkably stabilize biomacromolecules in the biologically active state. The stabilizing effect is typically observed over a large concentration range and affects many macromolecules including proteins, lipids, and DNA. Of special interest is the transition from aqueous solution to the dense and highly concentrated glassy state of trehalose that has been implicated in bioadaptation of different organisms toward desiccation stress. Although several mechanisms have been suggested to link the structure of the low water content glass with its action as an exceptional stabilizer, studies are ongoing to resolve which are most pertinent. Specifically, the role that hydrogen bonding plays in the formation of the glass is not well resolved. Here we model aqueous trehalose mixtures over a wide concentration range, using molecular dynamics simulations with two available force fields. Both force fields indicate glass transition temperatures and osmotic pressures that are close to experimental values, particularly at high trehalose contents. We develop and employ a methodology that allows us to analyze the thermodynamics of hydrogen bonds in simulations at different water contents and temperatures. Remarkably, this analysis is able to link the liquid to glass transition with changes in hydrogen bond characteristics. Most notably, the onset of the glassy state can be quantitatively related to the transition from weakly to strongly correlated hydrogen bonds. Our findings should help resolve the properties of the glass and the mechanisms of its formation in the presence of added macromolecules.

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“… 39 Modified CHARMM36 force fields calibrated with thermodynamic solution data have recently been suggested to resolve this overaggregative nature. 41 , 42 …”

Section: Introductionmentioning

confidence: 99%

Properties of Aqueous Trehalose Mixtures: Glass Transition and Hydrogen Bonding

Olgenblum

Sapir

Harries

2020

J. Chem. Theory Comput.

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“…Such a tendency of D-sorbitol for the creation of hydrogen bonds could be related to the higher number of available hydroxyl groups in its structure [ 77 ]. The appearance of new peaks in the presence of D-sorbitol could be induced by partial self-association of cyclitol molecules via hydrogen bonding [ 78 ]. D-sorbitol clustering leads to a weakening of cyclitol–water interactions; however, it increases the intermolecular interactions between solvent molecules.…”

Section: Resultsmentioning

confidence: 99%

The Study of Protein–Cyclitol Interactions

Dyrda-Terniuk

Sugajski

Pryshchepa

et al. 2022

IJMS

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Investigation of interactions between the target protein molecule and ligand allows for an understanding of the nature of the molecular recognition, functions, and biological activity of protein–ligand complexation. In the present work, non-specific interactions between a model protein (Bovine Serum Albumin) and four cyclitols were investigated. D-sorbitol and adonitol represent the group of linear-structure cyclitols, while shikimic acid and D-(–)-quinic acid have cyclic-structure molecules. Various analytical methods, including chromatographic analysis (HPLC-MS/MS), electrophoretic analysis (SDS-PAGE), spectroscopic analysis (spectrofluorimetry, Fourier transform infrared spectroscopy, and Raman spectroscopy), and isothermal titration calorimetry (ITC), were applied for the description of protein–cyclitol interactions. Additionally, computational calculations were performed to predict the possible binding places. Kinetic studies allowed us to clarify interaction mechanisms that may take place during BSA and cyclitol interaction. The results allow us, among other things, to evaluate the impact of the cyclitol’s structure on the character of its interactions with the protein.

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“…Molecular simulations were carried out according to our previously described methods. , Briefly, MD simulations were performed using GROMACS 5.0.5 and the CHARMM36m , force field, with the exception of force field parameters for the following excipients: sorbitol, sucrose, trehalose, proline, and Arg·HCl. Force field parameters for these excipients were taken from our previous work. ,, Simulations were performed at pH 5.5, with amino acid protonation states set using PropKa on the PDB2PQR server . Simulations were made charge neutral with the addition of sodium or chloride ions.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…Force field parameters for these excipients were taken from our previous work. 9,23,34 Simulations were performed at pH 5.5, with amino acid protonation states set using PropKa on the PDB2PQR server. 35 Simulations were made charge neutral with the addition of sodium or chloride ions.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

Machine Learning Models of Antibody–Excipient Preferential Interactions for Use in Computational Formulation Design

et al. 2020

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Preferential interactions of formulation excipients govern their impact on the stability properties of proteins in solution. The ability to predict these interactions without the need to perform experiments would enable formulation design to begin early in the development of a new antibody therapeutic. With that in mind, we developed a feature set to numerically describe local regions of an antibody’s surface for use in machine learning applications. Then, we used these features to train machine learning models for local antibody–excipient preferential interactions for the excipients sorbitol, sucrose, trehalose, proline, arginine·HCl, and NaCl. Our models had accuracies of up to about 85%. We also used linear (elastic net) models to quantify the contribution of antibody surface features to the preferential interaction coefficients, finding that the carbohydrates and proline tend to have similar important features, while the interactions of arginine·HCl and NaCl are governed by charge features. We present several case studies demonstrating how these machine learning models could be used to predict experimental aggregation and viscosity behavior in solution. Finally, we propose an approach to computational formulation design wherein a panel of excipients may be considered while designing an antibody sequence.

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Kirkwood–Buff-Derived Alcohol Parameters for Aqueous Carbohydrates and Their Application to Preferential Interaction Coefficient Calculations of Proteins

Cited by 20 publications

References 49 publications

Properties of Aqueous Trehalose Mixtures: Glass Transition and Hydrogen Bonding

Properties of Aqueous Trehalose Mixtures: Glass Transition and Hydrogen Bonding

The Study of Protein–Cyclitol Interactions

Machine Learning Models of Antibody–Excipient Preferential Interactions for Use in Computational Formulation Design

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