Prospectively Validated Proteochemometric Models for the Prediction of Small-Molecule Binding to Bromodomain Proteins.

The bromodomain-containing proteins are a ligandable family of epigenetic readers, which play important roles in oncological, cardiovascular, and inflammatory diseases. Achieving selective inhibition of specific bromodomains is challenging, due to the limited understanding of compound and target selectivity features. In this study we build and benchmark proteochemometric (PCM) classification models on bioactivity data for 15,350 data points across 31 bromodomains, using both compound fingerprints and binding site protein descriptors as input variables, achieving a maximum performance as measured by the Matthew's Correlation Coefficient (MCC) of 0.83 on the external test set. We also find that histone peptide binding data can be used as a target descriptor to build a high performing PCM model (MCC 0.80), showing the transferability of peptide interaction information to modeling small-molecule bioactivity. 1,139 compounds were selected for prospective experimental testing by performing a virtual screen using model predictions and implementing conformal prediction, which resulted in 319 correctly predicted compound-target pair actives and the correct prediction for certain selectivity profile combinations of the four bromodomains tested against. We identify that conformal prediction can be used to fine-tune the balance between hit retrieval and hit structural diversity in a virtual screening setting. PCM can be applied to future virtual screening and compound design, including off-target prediction for bromodomains.

In Situ Target Engagement Studies in Adherent Cells.

A prerequisite for successful drugs is effective binding of the desired target protein in the complex environment of a living system. Drug-target engagement has typically been difficult to monitor in physiologically relevant models, and with current methods, especially, while maintaining spatial information. One recent technique for quantifying drug-target engagement is the cellular thermal shift assay (CETSA), in which ligand-induced protein stabilization is measured after a heat challenge. Here, we describe a CETSA protocol in live A431 cells for p38α (MAPK14), where remaining soluble protein is detected in situ, using high-content imaging in 384-well, microtiter plates. We validate this assay concept using a number of known p38α inhibitors and further demonstrate the potential of this technology for chemical probe and drug discovery purposes by performing a small pilot screen for novel p38α binders. Importantly, this protocol creates a workflow that is amenable to adherent cells in their native state and yields spatially resolved target engagement information measurable at the single-cell level.

Exploring Binding Mechanisms in Nuclear Hormone Receptors by Monte Carlo and X-ray-derived Motions.

In this study, we performed an extensive exploration of the ligand entry mechanism for members of the steroid nuclear hormone receptor family (androgen receptor, estrogen receptor α, glucocorticoid receptor, mineralocorticoid receptor, and progesterone receptor) and their endogenous ligands. The exploration revealed a shared entry path through the helix 3, 7, and 11 regions. Examination of the x-ray structures of the receptor-ligand complexes further showed two distinct folds of the helix 6-7 region, classified as "open" and "closed", which could potentially affect ligand binding. To improve sampling of the helix 6-7 loop, we incorporated motion modes based on principal component analysis of existing crystal structures of the receptors and applied them to the protein-ligand sampling. A detailed comparison with the anisotropic network model (an elastic network model) highlights the importance of flexibility in the entrance region. While the binding (interaction) energy of individual simulations can be used to score different ligands, extensive sampling further allows us to predict absolute binding free energies and analyze reaction kinetics using Markov state models and Perron-cluster cluster analysis, respectively. The predicted relative binding free energies for three ligands binding to the progesterone receptor are in very good agreement with experimental results and the Perron-cluster cluster analysis highlighted the importance of a peripheral binding site. Our analysis revealed that the flexibility of the helix 3, 7, and 11 regions represents the most important factor for ligand binding. Furthermore, the hydrophobicity of the ligand influences the transition between the peripheral and the active binding site.

Glucose and fatty acid metabolism in McA-RH7777 hepatoma cells vs. rat primary hepatocytes: responsiveness to nutrient availability.

The overabundance of dietary fats and simple carbohydrates contributes significantly to obesity and metabolic disorders associated with obesity. The liver balances glucose and lipid distribution, and disruption of this balance plays a key role in these metabolic syndromes. We investigated (1) how hepatocytes balance glucose and fatty acid metabolism when one or both nutrients are supplied in abundance and (2) whether rat hepatoma cells (McA-RH7777) reflect nutrient partitioning in a similar manner as compared with primary hepatocytes. Increasing media palmitate concentration increased fatty acid uptake, triglyceride synthesis and beta-oxidation. However, hepatoma cells had a 2-fold higher fatty acid uptake and a 2-fold lower fatty acid oxidation as compared with primary hepatocytes. McA-RH7777 cells did not synthesize significant amounts of glycogen and preferentially metabolized the glucose into lipids or into oxidation. In primary hepatocytes, the glucose was mostly spared from oxidation and instead partitioned into both de novo glycogen and lipid synthesis. Overall, lipid production was rapidly induced in response to either glucose or fatty acid excess and this may be one of the earliest indicators of metabolic syndrome development associated with nutrient excess.