Álvaro Cortés-Cabrera

The Ubiquitin Proteasome System is the main proteolytic pathway in eukaryotic cells, playing a role in key cellular processes. The essentiality of the Plasmodium falciparum proteasome is well validated, underlying its potential as an antimalarial target, but selective compounds are required to avoid cytotoxic effects in humans. Almost 550000 compounds were tested for the inhibition of the chymotrypsin-like activity of the P. falciparum proteasome using a Proteasome-GLO luminescence assay. Hits were confirmed in an orthogonal enzyme assay using Rho110-labeled peptides, and selectivity was assessed against the human proteasome. Four nonpeptidomimetic chemical families with some selectivity for the P. falciparum proteasome were identified and characterized in assays of proteasome trypsin and caspase activities and in parasite growth inhibition assays. Target engagement studies were performed, validating our approach. Hits identified are good starting points for the development of new antimalarial drugs and as tools to better understand proteasome function in P. falciparum.

The Ubiquitin Proteasome System (UPS) is one of the main proteolytic pathways in eukaryotic cells, playing an essential role in key cellular processes such as cell cycling and signal transduction. Changes in some of the components of this pathway have been implicated in various conditions, including cancer and infectious diseases such as malaria. The success of therapies based on proteasome inhibitors has been shown in human clinical trials. In addition to its proven tractability, the essentiality of the Plasmodium falciparum UPS underlines its potential as a source of targets to identify new antimalarial treatments. Two assays, previously developed to quantify the parasite protein ubiquitylation levels in a high throughput format, have been used to identify compounds that inhibit parasite growth by targeting P. falciparum UPS. Among the positive hits, specific inhibitors of the P. falciparum proteasome have been identified and characterized. Hits identified using this approach may be used as starting points for development of new antimalarial drugs. They may also be used as tools to further understand proteasome function, and to identify new targets in P. falciparum UPS.

Many natural products target mammalian tubulin but only a few can form a covalent bond and hence irreversibly affect microtubule function. Among them, zampanolide (ZMP) and taccalonolide AJ (TAJ) stand out, not only because they are very potent antitumor agents but also because the adducts they form with β-tubulin have been structurally characterized in atomic detail. By applying model building techniques, molecular orbital calculations, molecular dynamics simulations and hybrid QM/MM methods, we have gained insight into the 1,2- and 1,4-addition reactions of His229 and Asp226 to ZMP and TAJ, respectively, in the taxane-binding site of β-tubulin. The experimentally inaccessible precovalent complexes strongly suggest a water-mediated proton shuttle mechanism for ZMP adduct formation and a direct nucleophilic attack by the carboxylate of Asp226 on C22 of the C22R,C23R epoxide in TAJ. The M-loop, which is crucially important for interprotofilament interactions, is structured into a short helix in both types of complexes, mostly as a consequence of the fixation of the phenol ring of Tyr283 and the guanidinium of Arg284. As a side benefit, we obtained evidence supporting the existence of a commonly neglected intramolecular disulfide bond between Cys241 and Cys356 in β-tubulin that contributes to protein compactness and is absent in the β_III isotype associated with resistance to taxanes and other drugs.

Lithium ion, commonly used as the carbonate salt in the treatment of bipolar disorders, has been identified as an inhibitor of several kinases, including Glycogen Synthase Kinase‐3β, for almost 20 years. However, both the exact mechanism of enzymatic inhibition and its apparent specificity for certain metalloenzymes are still a matter of debate. A data‐driven hypothesis is presented that accounts for the specificity profile of kinase inhibition by lithium in terms of the presence of a unique protein environment in the magnesium‐binding site. This hypothesis has been validated by the discovery of two novel potential targets for lithium, namely NEK3 and MOK, which are related to neuronal function.

The COMBINE method was designed to study congeneric series of compounds including structural information of ligand–protein complexes. Although very successful, the method has not received the same level of attention than other alternatives to study Quantitative Structure Active Relationships (QSAR) mainly because lack of ways to measure the uncertainty of the predictions and the need for large datasets. Active learning, a semi-supervised learning approach that makes use of uncertainty to enhance models’ performance while reducing the size of the training sets, has been used in this work to address both problems. We propose two estimators of uncertainty: the pool of regressors and the distance to the training set. The performance of the methods has been evaluated by testing the resulting active learning workflows in 3 diverse datasets: HIV-1 protease inhibitors, Taxol-derivatives and BRD4 inhibitors. The proposed strategies were successful in 80% of the cases for the taxol-derivatives and BRD4 inhibitors, while outperformed random selection in the case of the HIV-1 protease inhibitors time-split. Our results suggest that AL-COMBINE might be an effective way of producing consistently superior QSAR models with a limited number of samples.

Spread of parasite resistance to artemisinin threatens current frontline antimalarial therapies, highlighting the need for new drugs with alternative modes of action. Since only 0.2–1% of asexual parasites differentiate into sexual, transmission-competent forms, targeting this natural bottleneck provides a tangible route to interrupt disease transmission and mitigate resistance selection. Here we present a high-throughput screen of gametogenesis against a ~70,000 compound diversity library, identifying seventeen drug-like molecules that target transmission. Hit molecules possess varied activity profiles including male-specific, dual acting male–female and dual-asexual-sexual, with one promising N-((4-hydroxychroman-4-yl)methyl)-sulphonamide scaffold found to have sub-micromolar activity in vitro and in vivo efficacy. Development of leads with modes of action focussed on the sexual stages of malaria parasite development provide a previously unexplored base from which future therapeutics can be developed, capable of preventing parasite transmission through the population.

Compound biological fingerprints built on data from high throughput screening (HTS) campaigns, or HTS fingerprints, are a novel cheminformatics method of representing compounds by integrating chemical and biological activity data that is gaining momentum in its application to drug discovery, including hit expansion, target identification and virtual screening. HTS fingerprints present two major limitations, noise and missing data, which are intrinsic to the high-throughput data acquisition technologies and to the assay availability or assay selection procedure used for their construction. In this work, we present a methodology to define an optimal set of HTS fingerprints by using a desirability function that encodes the principles of maximum biological and chemical space coverage, and minimum redundancy between HTS assays. We use a genetic algorithm to optimize such desirability function and obtain an optimal fingerprint that is evaluated for performance in a test set of 33 diverse assays. Our results show that the optimal HTS fingerprint represent compounds in chemical-biology space using 25% less assays. Used for virtual screening, the optimal HTS fingerprint obtained equivalent performance, in terms of both AUC and enrichment factors, to full fingerprints for 27 out of 33 test assays, while randomly assembled fingerpints could achieve equivalent performance in only 23 test assays.

Water molecules in protein binding sites play essential roles in biological processes. The popular 3D-RISM prediction method can calculate the solvent density distribution within minutes, but is difficult to convert it into explicit water molecules. We present GAsol, a tool that is capable of finding the network of water molecules that best fits a particular 3D-RISM density distribution in a fast and accurate manner and that outperforms other available tools by finding the globally optimal solution thanks to its genetic algorithm. https://github.com/accsc/GAsol. BSD 3-clauses license Supplementary data are available at Bioinformatics online.