Challenges Predicting Ligand-Receptor Interactions of Promiscuous Proteins: The Nuclear Receptor PXR

Transcriptional regulation of some genes involved in xenobiotic detoxification and apoptosis is performed via the human pregnane X receptor (PXR) which in turn is activated by structurally diverse agonists including steroid hormones. Activation of PXR has the potential to initiate adverse effects, altering drug pharmacokinetics or perturbing physiological processes. Reliable computational prediction of PXR agonists would be valuable for pharmaceutical and toxicological research. There has been limited success with structure-based modeling approaches to predict human PXR activators. Slightly better success has been achieved with ligand-based modeling methods including quantitative structure-activity relationship (QSAR) analysis, pharmacophore modeling and machine learning. In this study, we present a comprehensive analysis focused on prediction of 115 steroids for ligand binding activity towards human PXR. Six crystal structures were used as templates for docking and ligand-based modeling approaches (two-, three-, four- and five-dimensional analyses). The best success at external prediction was achieved with 5D-QSAR. Bayesian models with FCFP_6 descriptors were validated after leaving a large percentage of the dataset out and using an external test set. Docking of ligands to the PXR structure co-crystallized with hyperforin had the best statistics for this method. Sulfated steroids (which are activators) were consistently predicted as non-activators while, poorly predicted steroids were docked in a reverse mode compared to 5α-androstan-3β-ol. Modeling of human PXR represents a complex challenge by virtue of the large, flexible ligand-binding cavity. This study emphasizes this aspect, illustrating modest success using the largest quantitative data set to date and multiple modeling approaches

Probing the Mutational Interplay between Primary and Promiscuous Protein Functions: A Computational-Experimental Approach

Protein promiscuity is of considerable interest due its role in adaptive metabolic plasticity, its fundamental connection with molecular evolution and also because of its biotechnological applications. Current views on the relation between primary and promiscuous protein activities stem largely from laboratory evolution experiments aimed at increasing promiscuous activity levels. Here, on the other hand, we attempt to assess the main features of the simultaneous modulation of the primary and promiscuous functions during the course of natural evolution. The computational/experimental approach we propose for this task involves the following steps: a function-targeted, statistical coupling analysis of evolutionary data is used to determine a set of positions likely linked to the recruitment of a promiscuous activity for a new function; a combinatorial library of mutations on this set of positions is prepared and screened for both, the primary and the promiscuous activities; a partial-least-squares reconstruction of the full combinatorial space is carried out; finally, an approximation to the Pareto set of variants with optimal primary/promiscuous activities is derived. Application of the approach to the emergence of folding catalysis in thioredoxin scaffolds reveals an unanticipated scenario: diverse patterns of primary/promiscuous activity modulation are possible, including a moderate (but likely significant in a biological context) simultaneous enhancement of both activities. We show that this scenario can be most simply explained on the basis of the conformational diversity hypothesis, although alternative interpretations cannot be ruled out. Overall, the results reported may help clarify the mechanisms of the evolution of new functions. From a different viewpoint, the partial-least-squares-reconstruction/Pareto-set-prediction approach we have introduced provides the computational basis for an efficient directed-evolution protocol aimed at the simultaneous enhancement of several protein features and should therefore open new possibilities in the engineering of multi-functional enzymes

TransCent: Computational enzyme design by transferring active sites and considering constraints relevant for catalysis

BACKGROUND: Computational enzyme design is far from being applicable for the general case. Due to computational complexity and limited knowledge of the structure-function interplay, heuristic methods have to be used. RESULTS: We have developed TransCent, a computational enzyme design method supporting the transfer of active sites from one enzyme to an alternative scaffold. In an optimization process, it balances requirements originating from four constraints. These are 1) protein stability, 2) ligand binding, 3) pKa values of active site residues, and 4) structural features of the active site. Each constraint is handled by an individual software module. Modules processing the first three constraints are based on state-of-the-art concepts, i.e. RosettaDesign, DrugScore, and PROPKA. To account for the fourth constraint, knowledge-based potentials are utilized. The contribution of modules to the performance of TransCent was evaluated by means of a recapitulation test. The redesign of oxidoreductase cytochrome P450 was analyzed in detail. As a first application, we present and discuss models for the transfer of active sites in enzymes sharing the frequently encountered triosephosphate isomerase fold. CONCLUSION: A recapitulation test on native enzymes showed that TransCent proposes active sites that resemble the native enzyme more than those generated by RosettaDesign alone. Additional tests demonstrated that each module contributes to the overall performance in a statistically significant manner

Modeling metabolism of Mycobacterium tuberculosis

Approximately one-fourth of the Mycobacterium tuberculosis (Mtb) genome contains genes that encode enzymes directly involved in its metabolism. These enzymes represent potential drug targets that can be systematically probed with constraint based (CB) models through the prediction of genes essential (or the combination thereof) for the pathogen to grow. However, gene essentiality depends on the growth conditions and, so far, no in vitro model precisely mimics the host at the different stages of mycobacterial infection, limiting model predictions. A first step in creating such a model is a thoroughly curated and extended genome-scale CB metabolic model of Mtb metabolism. The history of genome-scale CB models of Mtb metabolism up to model sMtb are discussed and sMtb is quantitatively validated using 13C measurements. The human pathogen Mtb has the capacity to escape eradication by professional phagocytes. During infection, Mtb resists the harsh environment of phagosomes and actively manipulates macrophages and dendritic cells to ensure prolonged intracellular survival. In contrast to many other intracellular pathogens, it has remained difficult to capture the transcriptome of mycobacteria during infection due to an unfavorable host-to-pathogen ratio. The human macrophage-like cell line THP-1 was infected with the attenuated Mtb surrogate Mycobacterium bovis Bacillus Calmette–Guérin (M. bovis BCG). Mycobacterial RNA was up to 1000-fold underrepresented in total RNA preparations of infected host cells. By combining microbial enrichment with specific ribosomal RNA depletion the transcriptional responses of host and pathogen during infection were simultaneously analyzed using dual RNA sequencing. Mycobacterial pathways for cholesterol degradation and iron acquisition are upregulated during infection. In addition, genes involved in the methylcitrate cycle, aspartate metabolism and recycling of mycolic acids are induced. In response to M. bovis BCG infection, host cells upregulate de novo cholesterol biosynthesis presumably to compensate for the loss of this metabolite by bacterial catabolism. By systematically probing the metabolic network underpinning sMtb, the reactions that are essential for Mtb are identified. A majority of these reactions are catalyzed by enzymes and thus represent candidate drug targets to fight an Mtb infection. Modeling the behavior of the bacteria during infection requires knowledge of the so-called biomass reaction that represents bacterial biomass composition. This composition varies in different environments or bacterial growth phases. Accurate modeling of all fluxes through metabolism under a given condition at a moment in time, the so called metabolic state, requires a precise description of the biomass reaction for the described condition. The transcript abundance data obtained by dual RNA sequencing was used to develop a straightforward and systematic method to obtain a condition-specific biomass reaction for Mtb during in vitro growth and during infection of its host. The method described herein is virtually free of any pre-set assumptions on uptake rates of nutrients, making it suitable for exploring environments with limited accessibility. The condition-specific biomass reaction represents the 'metabolic objective' of Mtb in a given environment (in-host growth and growth on defined medium) at a specific time point, and as such allows modeling the bacterial metabolic state in these environments. Five different biomass reactions were used predict nutrient uptake rates and gene essentiality. Predictions were subsequently compared to available experimental data. Nutrient uptake can accurately be predicted, but accurate gene essentiality predictions remain difficult to obtain. By combining sMtb and a model of human metabolism, model sMtb-RECON was developed and used to predict the metabolic state of Mtb during infection of the host. Amino acids are predicted to be used for energy production as well as biomass formation. Subsequently the effect of increasing dosages of drugs, targeting metabolism, on the metabolic state of the pathogen was assessed and resulting metabolic adaptations and flux rerouting through various pathways is predicted. In particular, the TCA cycle becomes more important upon drug application, as well as alanine, aspartate, glutamate, proline, arginine and porphyrin metabolism, while glycine, serine and threonine metabolism become less important for survival. Notably, an effect of eight out of eleven metabolically active drugs could be recreated and two major profiles of the metabolic state were predicted. The profiles of the metabolic states of Mtb affected by the drugs BTZ043, cycloserine and its derivative terizidone, ethambutol, ethionamide, propionamide, and isoniazid were very similar, while TMC207 is predicted to have quite a different effect on metabolism as it inhibits ATP synthase and therefore indirectly interferes with a multitude of metabolic pathways.</p

Pareto optimization in computational protein design with multiple objectives.

International audienceThe optimization for function in computational design requires the treatment of, often competing, multiple objectives. Current algorithms reduce the problem to a single objective optimization problem, with the consequent loss of relevant solutions. We present a procedure, based on a variant of a Pareto algorithm, to optimize various competing objectives in protein design that allows reducing in several orders of magnitude the search of the solution space. Our methodology maintains the diversity of solutions and provides an iterative way to incorporate automatic design methods in the design of functional proteins. We have applied our systematic procedure to design enzymes optimized for both catalysis and stability. However, this methodology can be applied to any computational chemistry application requiring multi-objective combinatorial optimization techniques