High Throughput Screening to Identify Selective and Nonpeptidomimetic Proteasome Inhibitors As Antimalarials.

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.

Collaborative drug discovery and the Tres Cantos Antimalarial Set (TCAMS).

Malaria affects a population of over 200 million people worldwide. New drugs are needed because of widespread resistance, and the hunt for such drugs involves a coordinated research effort from the scientific community. The release of the Tres Cantos Antimalarial Set (TCAMS) in 2010 represented a landmark in the field of collaborative drug discovery for malaria. This set of >13 000 molecules with confirmed activity against several strains of Plasmodium falciparum was publicly released with the goal of fostering additional research beyond the GlaxoSmithKline (GSK) network of collaborators. Here, we examine the outcomes realized from TCAMS over the past 8 years and whether the expectations surrounding this initiative have become a reality.

Active learning strategies with COMBINE analysis: new tricks for an old dog.

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.

HtrA1 Mediated Intracellular Effects on Tubulin Using a Polarized RPE Disease Model.

Age-related macular degeneration (AMD) is the leading cause of irreversible vision loss. The protein HtrA1 is enriched in retinal pigment epithelial (RPE) cells isolated from AMD patients and in drusen deposits. However, it is poorly understood how increased levels of HtrA1 affect the physiological function of the RPE at the intracellular level. Here, we developed hfRPE (human fetal retinal pigment epithelial) cell culture model where cells fully differentiated into a polarized functional monolayer. In this model, we fine-tuned the cellular levels of HtrA1 by targeted overexpression. Our data show that HtrA1 enzymatic activity leads to intracellular degradation of tubulin with a corresponding reduction in the number of microtubules, and consequently to an altered mechanical cell phenotype. HtrA1 overexpression further leads to impaired apical processes and decreased phagocytosis, an essential function for photoreceptor survival. These cellular alterations correlate with the AMD phenotype and thus highlight HtrA1 as an intracellular target for therapeutic interventions towards AMD treatment.

HtrA1 activation is driven by an allosteric mechanism of inter-monomer communication.

The human protease family HtrA is responsible for preventing protein misfolding and mislocalization, and a key player in several cellular processes. Among these, HtrA1 is implicated in several cancers, cerebrovascular disease and age-related macular degeneration. Currently, HtrA1 activation is not fully characterized and relevant for drug-targeting this protease. Our work provides a mechanistic step-by-step description of HtrA1 activation and regulation. We report that the HtrA1 trimer is regulated by an allosteric mechanism by which monomers relay the activation signal to each other, in a PDZ-domain independent fashion. Notably, we show that inhibitor binding is precluded if HtrA1 monomers cannot communicate with each other. Our study establishes how HtrA1 trimerization plays a fundamental role in proteolytic activity. Moreover, it offers a structural explanation for HtrA1-defective pathologies as well as mechanistic insights into the degradation of complex extracellular fibrils such as tubulin, amyloid beta and tau that belong to the repertoire of HtrA1.

VSDMIP 1.5: an automated structure- and ligand-based virtual screening platform with a PyMOL graphical user interface.

A graphical user interface (GUI) for our previously published virtual screening (VS) and data management platform VSDMIP (Gil-Redondo et al. J Comput Aided Mol Design, 23:171-184, 2009) that has been developed as a plugin for the popular molecular visualization program PyMOL is presented. In addition, a ligand-based VS module (LBVS) has been implemented that complements the already existing structure-based VS (SBVS) module and can be used in those cases where the receptor's 3D structure is not known or for pre-filtering purposes. This updated version of VSDMIP is placed in the context of similar available software and its LBVS and SBVS capabilities are tested here on a reduced set of the Directory of Useful Decoys database. Comparison of results from both approaches confirms the trend found in previous studies that LBVS outperforms SBVS. We also show that by combining LBVS and SBVS, and using a cluster of ~100 modern processors, it is possible to perform complete VS studies of several million molecules in less than a month. As the main processes in VSDMIP are 100% scalable, more powerful processors and larger clusters would notably decrease this time span. The plugin is distributed under an academic license upon request from the authors.