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Catalases are among the main scavengers of reactive oxygen species (ROS) present in the peroxisome, thereby preventing oxidative cellular and tissular damage. In human, multiple diseases are associated with malfunction of these organelles, which causes accumulation of ROS species and consequently the inefficient detoxification of cells. Despite intense research, much remains to be clarified about the precise molecular role of catalase in cellular homeostasis. Yeast peroxisomes and their peroxisomal catalases have been used as eukaryotic models for oxidative metabolism, ROS generation and detoxification, and associated pathologies. In order to provide reliable models for oxidative metabolism research, we have determined the high-resolution crystal structures of peroxisomal catalase from two important biotechnology and basic biology yeast models, Pichia pastoris and Kluyveromyces lactis. We have performed an extensive functional, biochemical and stability characterization of both enzymes in order to establish their differential activity profiles. Furthermore, we have analyzed the role of the peroxisomal catalase under study in the survival of yeast to oxidative burst challenges combining methanol, water peroxide, and sodium chloride. Interestingly, whereas catalase activity was induced 200-fold upon challenging the methylotrophic P. pastoris cells with methanol, the increase in catalase activity in the non-methylotrophic K. lactis was only moderate. The inhibitory effect of sodium azide and β-mercaptoethanol over both catalases was analyzed, establishing IC50 values for both compounds that are consistent with an elevated resistance of both enzymes toward these inhibitors. Structural comparison of these two novel catalase structures allows us to rationalize the differential susceptibility to inhibitors and oxidative bursts. The inherent worth and validity of the P. pastoris and K. lactis yeast models for oxidative damage will be strengthened by the availability of reliable structural-functional information on these enzymes, which are central to our understanding of peroxisomal response toward oxidative stress., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Protozoan parasites have been one of the most significant public health problems for centuries and several human infections caused by them have massive global impact. Most of the current drugs used to treat these illnesses have been used for decades and have many limitations such as the emergence of drug resistance, severe side-effects, low-to-medium drug efficacy, administration routes, cost, etc. These drugs have been largely neglected as models for drug development because they are majorly used in countries with limited resources and as a consequence with scarce marketing possibilities. Nowadays, there is a pressing need to identify and develop new drug-based antiprotozoan therapies. In an effort to overcome this problem, the main purpose of this study is to develop a QSARs-based ensemble classifier for antiprotozoan drug-like entities from a heterogeneous compounds collection. Here, we use some of the TOMOCOMD-CARDD molecular descriptors and linear discriminant analysis (LDA) to derive individual linear classification functions in order to discriminate between antiprotozoan and non-antiprotozoan compounds as a way to enable the computational screening of virtual combinatorial datasets and/or drugs already approved. Firstly, we construct a wide-spectrum benchmark database comprising of 680 organic chemicals with great structural variability (254 of them antiprotozoan agents and 426 to drugs having other clinical uses). This series of compounds was processed by a k-means cluster analysis in order to design training and predicting sets. In total, seven discriminant functions were obtained, by using the whole set of atom-based linear indices. All the LDA-based QSAR models show accuracies above 85% in the training set and values of Matthews correlation coefficients (C) vary from 0.70 to 0.86. The external validation set shows rather-good global classifications of around 80% (92.05% for best equation). Later, we developed a multi-agent QSAR classification system, in which the individual QSAR outputs are the inputs of the aforementioned fusion approach. Finally, the fusion model was used for the identification of a novel generation of lead-like antiprotozoan compounds by using ligand-based virtual screening of 'available' small molecules (with synthetic feasibility) in our 'in-house' library. A new molecular subsystem (quinoxalinones) was then theoretically selected as a promising lead series, and its derivatives subsequently synthesized, structurally characterized, and experimentally assayed by using in vitro screening that took into consideration a battery of five parasite-based assays. The chemicals 11(12) and 16 are the most active (hits) against apicomplexa (sporozoa) and mastigophora (flagellata) subphylum parasites, respectively. Both compounds depicted good activity in every protozoan in vitro panel and they did not show unspecific cytotoxicity on the host cells. The described technical framework seems to be a promising QSAR-classifier tool for the molecular discovery and development of novel classes of broad-antiprotozoan-spectrum drugs, which may meet the dual challenges posed by drug-resistant parasites and the rapid progression of protozoan illnesses., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

The conduct of many trials for the successful production of large quantities of pure proteins for structural biology and biotechnology applications, particularly of active, authentically processed enzymes, large eukaryotic multi-subunit complexes and membrane proteins, has spurred the development of recombinant expression systems. Beyond the well-established Escherichia coli, mammalian cell culture and baculovirus-infected insect cell expression systems, a plethora of alternative expression systems has been discovered, engineered, matured and deployed, resulting in crystal, nuclear magnetic resonance, and electron microscopy structures. In this review, we visit alternative expression hosts for structural biology ranging from bacteria and archaea to filamentous and unicellular yeasts and protozoa, with particular emphasis on their applicability to the structural determination of high-value, challenging proteins and complexes., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

A non-stochastic quadratic fingerprints-based approach is introduced to classify and design, in a rational way, new antitrypanosomal compounds. A data set of 153 organic chemicals, 62 with antitrypanosomal activity and 91 having other clinical uses, was processed by a k-means cluster analysis to design training and predicting data sets. Afterwards, a linear classification function was derived allowing the discrimination between active and inactive compounds. The model classifies correctly more than 93% of chemicals in both training and external prediction groups. The predictability of this discriminant function was also assessed by a leave-group-out experiment, in which 10% of the compounds were removed at random at each time and their activity predicted a posteriori. In addition, a comparison with models generated using four well-known families of 2D molecular descriptors was carried out. As an experiment of virtual lead generation, the present TOMOCOMD approach was finally satisfactorily applied on the virtual evaluation of 10 already synthesized compounds. The in vitro antitrypanosomal activity of this series against epimastigotes forms of Trypanosomal cruzi was assayed. The model was able to predict correctly the behaviour of these compounds in 90% of the cases.