Protein Science, the flagship journal of The Protein Society, serves an international forum for publishing original reports on all scientific aspects of protein molecules. The Journal publishes papers by leading scientists from all over the world that report on advances in the understanding of proteins in the broadest sense. Protein Science aims to unify this field by cutting across established disciplinary lines and focusing on “protein-centered” science.

Abstract Enzyme stability can be measured in a number of ways, including melting temperature, activity retention, and size analysis. However, these measurements are often conducted in an idealized storage buffer and not in the relevant enzymatic reaction media. Particularly for reactions that occur in alkaline, volatile, and high ionic strength media, typical analyses using differential scanning calorimetry, light scattering, and sodium dodecyl‐sulfate polyacrylamide gel electrophoresis are not satisfactory to track the stability of these enzymes. In this work, we monitor the stability of engineered and native dehydrogenases that require a high amount of ammonia for their reaction to occur. We demonstrate the benefits of analyzing these enzymes in their reaction buffer, uncovering trends that were not observable in the typical phosphate storage buffer. This work provides a framework for analyzing the stability of many other enzymes whose reaction media is not suitable for traditional techniques. We introduce several strategies for measuring the melting temperature, oligomeric state, and activity of these enzymes in their reaction media. Further, we have identified opportunities for integration of computational tools into this workflow to engineer enzymes more effectively for solvent tolerance and improved stability.

Extremophile organisms have adapted to extreme physicochemical conditions. Halophilic organisms, in particular, survive at very high salt concentrations. To achieve this, they have engineered the surface of their proteins to increase the number of short, polar and acidic amino acids, while decreasing large, hydrophobic and basic residues. While these adaptations initially decrease protein stability in the absence of salt, they grant halophilic proteins remarkable stability in environments with extremely high salt concentrations, where non-adapted proteins unfold and aggregate. The molecular mechanisms by which halophilic proteins achieve this, however, are not yet clear. Here, we test the hypothesis that the halophilic amino acid composition destabilizes the surface of the protein, but in exchange improves the stability in the presence of salts. To do that, we have measured the folding thermodynamics of various protein variants with different degrees of halophilicity in the absence and presence of different salts, and at different pH values to tune the ionization state of the acidic amino acids. Our results show that halophilic amino acids decrease the stability of halophilic proteins under mesophilic conditions, but in exchange improve salt-induced stabilization and solubility. We also find that, in contrast to traditional assumptions, contributions arising from hydrophobic effect and preferential ion exclusion are more relevant for haloadaptation than electrostatics. Overall, our findings suggest a trade-off between folding thermodynamics and halophilic adaptation to optimize proteins for hypersaline environments.

The identification of an effective inhibitor is an important starting step in drug development. Unfortunately, many issues such as the characterization of protein binding sites, the screening library, materials for assays, etc., make drug screening a difficult proposition. As the size of screening libraries increases, more resources will be inefficiently consumed. Thus, new strategies are needed to preprocess and focus a screening library towards a targeted protein. Herein, we report an ensemble machine learning (ML) model to generate a CDK8-focused screening library. The ensemble model consists of six different algorithms optimized for CDK8 inhibitor classification. The models were trained using a CDK8-specific fragment library along with molecules containing CDK8 activity. The optimized ensemble model processed a commercial library containing 1.6 million molecules. This resulted in a CDK8-focused screening library containing 1,672 molecules, a reduction of more than 99.90%. The CDK8-focused library was then subjected to molecular docking, and 25 candidate compounds were selected. Enzymatic assays confirmed six CDK8 inhibitors, with one compound producing an IC50 value of ≤100 nM. Analysis of the ensemble ML model reveals the role of the CDK8 fragment library during training. Structural analysis of molecules reveals the hit compounds to be structurally novel CDK8 inhibitors. Together, the results highlight a pipeline for curating a focused library for a specific protein target, such as CDK8.