Protein engineering: the present and the future

Yes, we are made of proteins, and yes, we can profit from them [...

A comparative study of the characteristics and physical behaviour of different packing materials commonly used in biofiltration

In this study, the characteristics and physical behaviour of 8 different packing materials were compared. The materials were selected according to previous works in the field of biofiltration including organic and inorganic or synthetic materials. Results pre-selected those more acceptable support materials for the main function they have to perform in the biological system: high surface contact, rugosity to immobilize the biomass, low pressure drop, nutrients supply, water retentivity or a commitment among them. Otherwise, pressure drop have been described by means of the respective mathematic expressions in order to include phenomena in the classical biofiltration models.Peer ReviewedPostprint (author's final draft

Comparison of organic packing materials for toluene biofiltration

he paper focuses on the operation of a pilot plant with four biofilters operated in parallel for determining the suitability of coconut fiber, peat, compost from the digested sludge of a wastewater treatment plant and pine leaves as packing materials for biofiltration of toluene. Physical characteristics of packing materials such as specific surface area, density, pore size and elemental composition were determined for each packing material. Biological activity and packing capabilities related to toluene removal were determined during the startup and operation of the four biofilters under different conditions of nutrients, watering and inlet air relative humidity supply. Nutrient addition was key in improving removal efficiency (RE) and elimination capacity (EC) of biofilters. Feeding of medium with nutrients increased the RE and the EC by a factor of 2 to 4 than these found when supplying only tap water. Additionally, when extra nitrogen was supplied in the medium, RE and EC increased by a factor of 2. Nutrient addition also lead to a microbial population change from bacterial to fungal biofilters. It was denoted that watering control is necessary to improve fungal biofilters performance in terms of ensuring a proper washout of acidic by-products to avoid fungi inhibition and consequent lowered removal capacities.Peer ReviewedPostprint (published version

Flavodoxins as novel therapeutic targets against helicobacter pylori and other gastric pathogens

Flavodoxins are small soluble electron transfer proteins widely present in bacteria and absent in vertebrates. Flavodoxins participate in different metabolic pathways and, in some bacteria, they have been shown to be essential proteins representing promising therapeutic targets to fight bacterial infections. Using purified flavodoxin and chemical libraries, leads can be identified that block flavodoxin function and act as bactericidal molecules, as it has been demonstrated for Helicobacter pylori (Hp), the most prevalent human gastric pathogen. Increasing antimicrobial resistance by this bacterium has led current therapies to lose effectiveness, so alternative treatments are urgently required. Here, we summarize, with a focus on flavodoxin, opportunities for pharmacological intervention offered by the potential protein targets described for this bacterium and provide information on other gastrointestinal pathogens and also on bacteria from the gut microbiota that contain flavodoxin. The process of discovery and development of novel antimicrobials specific for Hp flavodoxin that is being carried out in our group is explained, as it can be extrapolated to the discovery of inhibitors specific for other gastric pathogens. The high specificity for Hp of the antimicrobials developed may be of help to reduce damage to the gut microbiota and to slow down the development of resistant Hp mutants

Activation by calcium of AMP deaminase from the human red cell

AbstractWe have investigated the effects of Ca2+ on AMP deaminase from human red cells. At variance with the other known modulators, Ca2+ increased the apparent affinity for AMP without modifying the characteristic positive cooperativity of the enzyme towards the substrate. Ca2+ sensitivity was not modified by dialysis, but dilution of the haemolysate produced an activation of the enzyme similar to that induced by Ca2+. Simultaneously, the Ca2+ dependence was lost. The sensitivity to other modulators, such as ATP, diphosphoglycerate or phosphate, was not modified by dilution. Partial purification of the enzyme produced the same effects as haemolysate dilution. These results may be interpreted to mean that Ca2+ acts by antagonizing an endogenous inhibitor present in red cell lysates

The coupling of plasma membrane calcium entry to calcium uptake by endoplasmic reticulum and mitochondria

Producción CientíficaCross-talk between organelles and plasma membrane Ca2+ channels is essential for modulation of the cytosolic Ca2+ ([Ca2+]C) signals, but such modulation may differ among cells. In chromaffin cells Ca2+ entry through voltage-operated channels induces calcium release from the endoplasmic reticulum (ER) that amplifies the signal. [Ca2+]C microdomains as high as 20–50 μM are sensed by subplasmalemmal mitochondria, which accumulate large amounts of Ca2+ through the mitochondrial Ca2+ uniporter (MCU). Mitochondria confine the high-Ca2+ microdomains (HCMDs) to beneath the plasma membrane,where exocytosis of secretory vesicles happens. Cell core [Ca2+]C is much smaller (1–2 μM). By acting as a Ca2+ sink, mitochondria stabilise theHCMDin space and time. In non-excitableHEK293 cells, activation of store-operated Ca2+ entry, triggered by ERCa2+ emptying, also generated subplasmalemmal HCMDs, but, in this case, most of the Ca2+ was taken up by the ER rather than bymitochondria. The smaller size of the [Ca2+]C peak in this case (about 2 μM)may contribute to this outcome, as the sarco-endoplasmic reticulum Ca2+ ATPase has much higher Ca2+ affinity than MCU. It is also possible that the relative positioning of organelles, channels and effectors, as well as cytoskeleton and accessory proteins plays an important role.2015-09-3

Unravelling the Complex Denaturant and Thermal-Induced Unfolding Equilibria of Human Phenylalanine Hydroxylase

Human phenylalanine hydroxylase (PAH) is a metabolic enzyme involved in the catabolism of L-Phe in liver. Loss of conformational stability and decreased enzymatic activity in PAH variants result in the autosomal recessive disorder phenylketonuria (PKU), characterized by developmental and psychological problems if not treated early. One current therapeutic approach to treat PKU is based on pharmacological chaperones (PCs), small molecules that can displace the folding equilibrium of unstable PAH variants toward the native state, thereby rescuing the physiological function of the enzyme. Understanding the PAH folding equilibrium is essential to develop new PCs for different forms of the disease. We investigate here the urea and the thermal-induced denaturation of full-length PAH and of a truncated form lacking the regulatory and the tetramerization domains. For either protein construction, two distinct transitions are seen in chemical denaturation followed by fluorescence emission, indicating the accumulation of equilibrium unfolding intermediates where the catalytic domains are partly unfolded and dissociated from each other. According to analytical centrifugation, the chemical denaturation intermediates of either construction are not well-defined species but highly polydisperse ensembles of protein aggregates. On the other hand, each protein construction similarly shows two transitions in thermal denaturation measured by fluorescence or differential scanning calorimetry, also indicating the accumulation of equilibrium unfolding intermediates. The similar temperatures of mid denaturation of the two constructions, together with their apparent lack of response to protein concentration, indicate the catalytic domains are unfolded in the full-length PAH thermal intermediate, where they remain associated. That the catalytic domain unfolds in the first thermal transition is relevant for the choice of PCs identified in high throughput screening of chemical libraries using differential scanning fluorimetry

Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power production as a function of these environmental conditions, in order to maximize the profit. The research community has solved this problem by applying Machine Learning techniques, and has managed to reduce the computational and time costs in comparison with the traditional thermodynamical analysis. Until now, this challenge has been tackled from a batch learning perspective, in which data is assumed to be at rest, and where models do not continuously integrate new information into already constructed models. We present an approach closer to the Big Data and Internet of Things paradigms, in which data are continuously arriving and where models learn incrementally, achieving significant enhancements in terms of data processing (time, memory and computational costs), and obtaining competitive performances. This work compares and examines the hourly electrical power prediction of several streaming regressors, and discusses about the best technique in terms of time processing and predictive performance to be applied on this streaming scenario.This work has been partially supported by the EU project iDev40. This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 783163. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Austria, Germany, Belgium, Italy, Spain, Romania. It has also been supported by the Basque Government (Spain) through the project VIRTUAL (KK-2018/00096), and by Ministerio de Economía y Competitividad of Spain (Grant Ref. TIN2017-85887-C2-2-P)

Aprendizaje de representaciones desenredadas de escenas a partir de imágenes.

Artificial intelligence is at the forefront of a technological revolution, in particular as a key component to build autonomous agents. However, not only training such agents come at a great computational cost, but they also end up lacking human basic abilities like generalization, information extrapolation, knowledge transfer between contexts, or improvisation. To overcome current limitations, agents need a deeper understanding of their environment, and more efficiently learning it from data. There are very recent works that propose novel approaches to learn representations of the world: instead of learning invariant object encodings, they learn to isolate, or disentangle, the different variable properties which form an object. This would not only enable agents to understand object changes as modifications of one of their properties, but also to transfer such knowledge on the properties between different categories. This Master Thesis aims to develop a new machine learning model for disentangling object properties on monocular images of scenes. Our model is based on a state-of-the-art architecture for disentangled representations learning, and our goal is to reduce the computational complexity of the base model while also improving its performance. To achieve this, we will replace a recursive unsupervised segmentation network by an encoder-decoder segmentation network. Furthermore, before training such overparametrized neural model without supervision, we will profit from transfer learning of pre-trained weights from a supervised segmentation task. After developing a first vanilla model, we have tuned it to improve its performance and generalization capability. Then, an experimental validation has been performed on two commonly used synthetic datasets, evaluating both its disentanglement performance and computational efficiency, and on a more realistic dataset to analyze the model capability on real data. The results show that our model outperforms the state of the art, while reducing its computational footprint. Nevertheless, further research is needed to bridge the gap with real world applications.<br /