The thermodynamic properties of unfolding of the Trp-cage mini protein in the presence of various concentrations of urea have been characterized using temperature-induced unfolding monitored by far-UV circular dichroism spectroscopy. Analysis of the data using a two-state model allowed the calculation of the Gibbs energy of unfolding at 25°C as a function of urea concentration. This in turn was analyzed by the linear extrapolation model that yielded the dependence of Gibbs energy on urea concentration, i.e. the m-value for Trp-cage unfolding. The m-value obtained from the experimental data, as well as the experimental heat capacity change upon unfolding, were correlated with the structural parameters derived from the three dimensional structure of Trp-cage. It is shown that the m-value can be predicted well using a transfer model, while the heat capacity changes are in very good agreement with the empirical models based on model compounds studies. These results provide direct evidence that Trp-cage, despite its small size, is an excellent model for studies of protein unfolding and provide thermodynamic data that can be used to compare with atomistic computer simulations.

S100B is a member of the S100 subfamily of EF-hand proteins that has been implicated in malignant melanoma and neurodegenerative conditions such as Alzheimer's and Parkinson's disease. Calcium-induced conformational changes expose a hydrophobic binding cleft, facilitating interactions with a wide variety of nuclear, cytoplasmic, and extracellular target proteins. Previously, peptides derived from CapZ, p53, NDR, HDM2 and HDM4 have been shown to interact with S100B in a calcium-dependent manner. However, the thermodynamic and kinetic basis of these interactions remains largely unknown. To gain further insight, these peptides were screened against the S100B protein using isothermal titration calorimetry and nuclear magnetic resonance. All peptides were found to have binding affinities in the low micromolar to nanomolar range. Binding-induced changes in the line shapes of S100B backbone 1H and 15N were monitored to obtain the dissociation constants and the kinetic binding parameters. The large microscopic Kon rate constants observed in this study, Kon ≥1×107 M-1s-1, suggest that S100B utilizes a “fly casting mechanism” in the recognition of these peptide targets.

The S100 protein family consists of small, dimeric proteins that exert their biological functions in response to changing calcium concentrations. S100B is the best studied member and has been shown to interact with over 20 binding partners in a calcium-dependent manner. The TRTK12 peptide, derived from the consensus binding sequence for S100B, has previously been found to interact with S100A1 and has been proposed to be a general binding partner of the S100 family. To test this hypothesis and gain a better understanding of the specificity of binding for the S100 proteins sixteen members of the human S100 family were screened against this peptide and its alanine variants. Novel interactions were only found with two family members: S100P and S100A2, indicating that TRTK12 selectively interacts with a small subset of the S100 proteins. Substantial promiscuity was observed in the binding site of S100B to accommodate variations in the peptide sequence, while S100A1, S100A2, and S100P exhibited larger differences in the binding constants for the TRTK12 alanine variants. This suggests that single-point substitutions can be used to selectively modulate the affinity of TRTK12 peptides for individual S100 proteins. This study has important implications for the rational drug design of inhibitors for the S100 proteins, which are involved in a variety of cancers and neurodegenerative diseases.

No abstract

Analytical ultracentrifugation-sedimentation velocity (AUC-SV) is often used to quantify high molar mass species (HMMS) present in biopharmaceuticals. Although these species are often present in trace quantities, they have received significant attention due to their potential immunogenicity. Commonly, AUC-SV data is analyzed as a diffusion-corrected, sedimentation coefficient distribution, or c(s), using SEDFIT to numerically solve Lamm-type equations. SEDFIT also utilizes maximum entropy or Tikhonov-Phillips regularization to further allow the user to determine relevant sample information, including the number of species present, their sedimentation coefficients, and their relative abundance. However, this methodology has several, often unstated, limitations, which may impact the final analysis of protein therapeutics. These include regularization-specific effects, artificial "ripple peaks," and spurious shifts in the sedimentation coefficients. In this investigation, we experimentally verified that an explicit Bayesian approach, as implemented in SEDFIT, can largely correct for these effects. Clear guidelines on how to implement this technique and interpret the resulting data, especially for samples containing micro-heterogeneity (e.g., differential glycosylation), are also provided. In addition, we demonstrated how the Bayesian approach can be combined with F statistics to draw more accurate conclusions and rigorously exclude artifactual peaks. Numerous examples with an antibody and an antibody-drug conjugate were used to illustrate the strengths and drawbacks of each technique.