Protein–Protein Interactions in Dilute to Concentrated Solutions: α-Chymotrypsinogen in Acidic Conditions

Abstract

Protein–protein interactions were investigated for α-chymotrypsinogen by static and dynamic light scattering (SLS and DLS, respectively), as well as small-angle neutron scattering (SANS), as a function of protein and salt concentration at acidic conditions. Net protein–protein interactions were probed via the Kirkwood–Buff integral G22 and the static structure factor S(q) from SLS and SANS data. G22 was obtained by regressing the Rayleigh ratio versus protein concentration with a local Taylor series approach, which does not require one to assume the underlying form or nature of intermolecular interactions. In addition, G22 and S(q) were further analyzed by traditional methods involving fits to effective interaction potentials. Although the fitted model parameters were not always physically realistic, the numerical values for G22 and S(q → 0) were in good agreement from SLS and SANS as a function of protein concentration. In the dilute regime, fitted G22 values agreed with those obtained via the osmotic second virial coefficient B22 and showed that electrostatic interactions are the dominant contribution for colloidal interactions in α-chymotrypsinogen solutions. However, as protein concentration increases, the strength of protein–protein interactions decreases, with a more pronounced decrease at low salt concentrations. The results are consistent with an effective “crowding” or excluded volume contribution to G22 due to the long-ranged electrostatic repulsions that are prominent even at the moderate range of protein concentrations used here (<40 g/L). These apparent crowding effects were confirmed and quantified by assessing the hydrodynamic factor H(q → 0), which is obtained by combining measurements of the collective diffusion coefficient from DLS data with measurements of S(q → 0). H(q → 0) was significantly less than that for a corresponding hard-sphere system and showed that hydrodynamic nonidealities can lead to qualitatively incorrect conclusions regarding B22, G22, and static protein–protein interactions if one uses only DLS to assess protein interactions.

Aggregate structure, morphology and the effect of aggregation mechanisms on viscosity at elevated protein concentrations

“…The aggregate size and concentration is also expected to depend on aggregation mechanism, and therefore this may also be expected to alter the net solution viscosity [24]. More recent results highlight the presence of strong electrostatic intermolecular repulsions for aCgn at pH 3.5 when one considers higher protein concentrations (~ 10-40 mg / mL) [12]. However, it remains unclear how aggregate structure and morphology will evolve at elevated protein concentrations under these acidic pH conditions, where large protein-protein interactions are expected.…”

“…The behavior of protein solutions at intermediate to high concentrations is difficult to predict based on protein properties or measurements at lower concentrations [9]. Previous work has calculated dilute-solution interactions, such as second osmotic coefficients, in silico from protein crystal structures [10,11], but it remains a challenge to extend the calculations to higher protein concentrations where three-body and high order interactions may be expected [12]. Many biophysical techniques, such as circular dichroism, analytical ultra centrifugation, and HPLC based techniques, are limited to low protein concentration for a variety of reasons [2,13].…”

Weak protein interactions and pH- and temperature-dependent aggregation of human Fc1

“…The instrument is equipped with a 120 mW solid state laser that operates at 658 nm. The weight averaged molecular weight (M w ) and the protein-protein Kirkwood Buff (KB) integral (G 22 ) were obtained using Equation 4,32,34 with additional details provided elsewhere. A dn/dc value of 0.187 cm 3 /g was used for all solution conditions.…”

“…Light scattering was measured at 16 angles between 32 and 147 and averaged over each one-second "slice" of column eluate with the in-line MALS detectors. Signals from the detectors were imported into ASTRA 5.3.4 software for data processing using standard methods.…”

“…Signals from the detectors were imported into ASTRA 5.3.4 software for data processing using standard methods. 32,34,38 …”