Equilibrium binding ligands usually increase protein thermal stability by an amount proportional to the concentration and affinity of the ligand. High-throughput screening for the discovery of drug-like compounds uses an assay based on thermal stabilization. The mathematical description of this stabilization is well developed, and the method is widely applicable to the characterization of ligand-protein binding equilibrium. However, numerous cases have been experimentally observed where equilibrium binding ligands destabilize proteins, i.e., diminish protein melting temperature by an amount proportional to the concentration and affinity of the ligand. Here, we present a thermodynamic model that describes ligand binding to the native and unfolded (denatured) protein states explaining the combined stabilization and destabilization effects. The model also explains nonsaturation and saturation effects on the protein melting temperature when the ligand concentration significantly exceeds the protein concentration. Several examples of the applicability of the model are presented, including specific sulfonamide binding to recombinant hCAII, peptide and ANS binding to the Polo-box domain of Plk1, and zinc ion binding to the recombinant porcine growth hormone. The same ligands may stabilize and destabilize different proteins, and the same proteins may be stabilized and destabilized by different ligands.

The analysis of tight protein-ligand binding reactions by isothermal titration calorimetry (ITC) and thermal shift assay (TSA) is presented. The binding of radicicol to the N-terminal domain of human heat shock protein 90 (Hsp90αN) and the binding of ethoxzolamide to human carbonic anhydrase (hCAII) were too strong to be measured accurately by direct ITC titration and therefore were measured by displacement ITC and by observing the temperature-denaturation transitions of ligand-free and ligand-bound protein. Stabilization of both proteins by their ligands was profound, increasing the melting temperature by more than 10 ºC, depending on ligand concentration. Analysis of the melting temperature dependence on the protein and ligand concentrations yielded dissociation constants equal to 1 nM and 2 nM for Hsp90αN-radicicol and hCAII-ethoxzolamide, respectively. The ligand-free and ligand-bound protein fractions melt separately, and two melting transitions are observed. This phenomenon is especially pronounced when the ligand concentration is equal to about half the protein concentration. The analysis compares ITC and TSA data, accounts for two transitions and yields the ligand binding constant and the parameters of protein stability, including the Gibbs free energy and the enthalpy of unfolding.

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Problem The current tumor immunology paradigm emphasizes the role of the immune tumor microenvironment and distinguishes several histologically and transcriptionally different immune tumor subtypes. However, the experimental validation of such classification is so far limited to selected cancer types. Here, we aimed to explore the existence of inflamed, excluded, and desert immune subtypes in ovarian cancer, as well as investigate their association with the disease outcome. Method of study We used the publicly available ovarian cancer dataset from The Cancer Genome Atlas for developing subtype assignment algorithm, which was next verified in a cohort of 32 real‐world patients of a known tumor subtype. Results Using clinical and gene expression data of 489 ovarian cancer patients in the publicly available dataset, we identified three transcriptionally distinct clusters, representing inflamed, excluded, and desert subtypes. We developed a two‐step subtyping algorithm with COL5A2 serving as a marker for separating excluded tumors, and CD2, TAP1, and ICOS for distinguishing between inflamed and desert tumors. The accuracy of gene expression–based subtyping algorithm in a real‐world cohort was 75%. Additionally, we confirmed that patients bearing inflamed tumors are more likely to survive longer. Conclusion Our results highlight the presence of transcriptionally and histologically distinct immune subtypes among ovarian tumors and emphasize the potential benefit of immune subtyping as a clinical tool for treatment tailoring.

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