Computational Perspectives into Plasmepsins Structure—Function Relationship: Implications to Inhibitors Design

The development of efficient and selective antimalariais remains a challenge for the pharmaceutical industry. The aspartic proteases plasmepsins, whose inhibition leads to parasite death, are classified as targets for the design of potent drugs. Combinatorial synthesis is currently being used to generate inhibitor libraries for these enzymes, and together with computational methodologies have been demonstrated capable for the selection of lead compounds. The high structural flexibility of plasmepsins, revealed by their X-ray structures and molecular dynamics simulations, made even more complicated the prediction of putative binding modes, and therefore, the use of common computational tools, like docking and free-energy calculations. In this review, we revised the computational strategies utilized so far, for the structure-function relationship studies concerning the plasmepsin family, with special focus on the recent advances in the improvement of the linear interaction estimation (LIE) method, which is one of the most successful methodologies in the evaluation of plasmepsin-inhibitor binding affinity

Structures of a bi-functional Kunitz-type STI family inhibitor of serine and aspartic proteases: could the aspartic protease inhibition have evolved from a canonical serine protease-binding loop?

Bi-functional inhibitors from the Kunitz-type soybean trypsin inhibitor (STI) family are glycosylated proteins able to inhibit serine and aspartic proteases. Here we report six crystal structures of the wild-type and a non-glycosylated mutant of the bifunctional inhibitor E3Ad obtained at different pH values and space groups. The crystal structures show that E3Ad adopts the typical β-trefoil fold of the STI family exhibiting some conformational changes due to pH variations and crystal packing. Despite the high sequence identity with a recently reported potato cathepsin D inhibitor (PDI), three-dimensional structures obtained in this work show a significant conformational change in the protease-binding loop proposed for aspartic protease inhibition. The E3Ad binding loop for serine protease inhibition is also proposed, based on structural similarity with a novel non-canonical conformation described for the double-headed inhibitor API-A from the Kunitz-type STI family. In addition, structural and sequence analyses suggest that bifunctional inhibitors of serine and aspartic proteases from the Kunitz-type STI family are more similar to double-headed inhibitor API-A than other inhibitors with a canonical protease-binding loop

Design, Implementation and Evaluation of SPOCs at the Universidad Carlos III de Madrid

The Universidad Carlos III de Madrid has been offering several face-to-face remedial courses for new students to review or learn concepts and practical skills that they should know before starting their degree program. During 2012 and 2013, our University adopted MOOC-like technologies to support some of these courses so that a blended learning methodology could be applied in a particular educational context, i.e. by using SPOCs (Small Private Online Courses). This paper gathers a list of issues, challenges and solutions when implementing these SPOCs. Based on these challenges and issues, a design process is proposed for the implementation of SPOCs. In addition, an evaluation is presented of the different use of the offered courses based on indicators such as the number of videos accessed, number of exercises accessed, number of videos completed, number of exercises correctly solved or time spent on the platform.Work partially funded by the RESET project under grant no. TIN2014-53199-C3-1-R (funded by the Spanish Ministry of Economy and Competitiveness), the REMEDISS project under grant no. IPT-2012-0882-430000 (funded by the Spanish Ministry of Economy and Competitiveness) and the “eMadrid” project (funded by the Regional Government of Madrid) under grant no. S2013/ICE-2715. Carlos Delgado Kloos wishes to acknowledge support from Fundación CajaMadrid to visit Harvard University and MIT in the academic year 2012-13

Beyond species loss: The extinction of ecological interactions in a changing world

© 2014 The Authors. The effects of the present biodiversity crisis have been largely focused on the loss of species. However, a missed component of biodiversity loss that often accompanies or even precedes species disappearance is the extinction of ecological interactions. Here, we propose a novel model that (i) relates the diversity of both species and interactions along a gradient of environmental deterioration and (ii) explores how the rate of loss of ecological functions, and consequently of ecosystem services, can be accelerated or restrained depending on how the rate of species loss covaries with the rate of interactions loss. We find that the loss of species and interactions are decoupled, such that ecological interactions are often lost at a higher rate. This implies that the loss of ecological interactions may occur well before species disappearance, affecting species functionality and ecosystems services at a faster rate than species extinctions. We provide a number of empirical case studies illustrating these points. Our approach emphasizes the importance of focusing on species interactions as the major biodiversity component from which the 'health' of ecosystems depends.Peer Reviewe

Discovery of potent and selective inhibitors of the Escherichia coli M1-aminopeptidase via multicomponent solid-phase synthesis of tetrazole-peptidomimetics

The Escherichia coli neutral M1-aminopeptidase (ePepN) is a novel target identified for the development of antimicrobials. Here we describe a solid-phase multicomponent approach which enabled the discovery of potent ePepN inhibitors. The on-resin protocol, developed in the frame of the Distributed Drug Discovery (D3) program, comprises the implementation of parallel Ugi-azide four-component reactions with resin-bound amino acids, thus leading to the rapid preparation of a focused library of tetrazole-peptidomimetics (TPMs) suitable for biological screening. By dose-response studies, three compounds were identified as potent and selective ePepN inhibitors, as little inhibitory effect was exhibited for the porcine ortholog aminopeptidase. The study allowed for the identification of the key structural features required for a high ePepN inhibitory activity. The most potent and selective inhibitor (TPM 11) showed a non-competitive inhibition profile of ePepN. We predicted that both diastereomers of compound TPM 11 bind to a site distinct from that occupied by the substrate. Theoretical models suggested that TPM 11 has an alternative inhibition mechanism that doesn't involve Zn coordination. On the other hand, the activity landscape analysis provided a rationale for our findings. Of note, compound TMP 2 showed in vitro antibacterial activity against Escherichia coli. Furthermore, none of the three identified inhibitors is a potent haemolytic agent, and only two compounds showed moderate cytotoxic activity toward the murine myeloma P3X63Ag cells. These results point to promising compounds for the future development of rationally designed TPMs as antibacterial agents

Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study

Background Iatrogenic bile duct injury (IBDI) is a challenging surgical complication. IBDI management can be guided by artificial intelligence models. Our study identified the factors associated with successful initial repair of IBDI and predicted the success of definitive repair based on patient risk levels. Methods This is a retrospective multi-institution cohort of patients with IBDI after cholecystectomy conducted between 1990 and 2020. We implemented a decision tree analysis to determine the factors that contribute to successful initial repair and developed a risk-scoring model based on the Comprehensive Complication Index. Results We analyzed 748 patients across 22 hospitals. Our decision tree model was 82.8% accurate in predicting the success of the initial repair. Non-type E (p < 0.01), treatment in specialized centers (p < 0.01), and surgical repair (p < 0.001) were associated with better prognosis. The risk-scoring model was 82.3% (79.0-85.3%, 95% confidence interval [CI]) and 71.7% (63.8-78.7%, 95% CI) accurate in predicting success in the development and validation cohorts, respectively. Surgical repair, successful initial repair, and repair between 2 and 6 weeks were associated with better outcomes. Discussion Machine learning algorithms for IBDI are a novel tool may help to improve the decision-making process and guide management of these patients