In silico assessment of potential druggable pockets on the surface of α1-Antitrypsin conformers

The search for druggable pockets on the surface of a protein is often performed on a single conformer, treated as a rigid body. Transient druggable pockets may be missed in this approach. Here, we describe a methodology for systematic in silico analysis of surface clefts across multiple conformers of the metastable protein α1-antitrypsin (A1AT). Pathological mutations disturb the conformational landscape of A1AT, triggering polymerisation that leads to emphysema and hepatic cirrhosis. Computational screens for small molecule inhibitors of polymerisation have generally focused on one major druggable site visible in all crystal structures of native A1AT. In an alternative approach, we scan all surface clefts observed in crystal structures of A1AT and in 100 computationally produced conformers, mimicking the native solution ensemble. We assess the persistence, variability and druggability of these pockets. Finally, we employ molecular docking using publicly available libraries of small molecules to explore scaffold preferences for each site. Our approach identifies a number of novel target sites for drug design. In particular one transient site shows favourable characteristics for druggability due to high enclosure and hydrophobicity. Hits against this and other druggable sites achieve docking scores corresponding to a Kd in the µM–nM range, comparing favourably with a recently identified promising lead. Preliminary ThermoFluor studies support the docking predictions. In conclusion, our strategy shows considerable promise compared with the conventional single pocket/single conformer approach to in silico screening. Our best-scoring ligands warrant further experimental investigation

Is the impact of hospital performance data greater in patients who have compared hospitals?

<p>Abstract</p> <p>Background</p> <p>Public information on average has limited impact on patients' hospital choice. However, the impact may be greater in consumers who have compared hospitals prior to their hospital choice. We therefore assessed whether patients who have compared hospitals based their hospital choice mainly on public information, rather than e.g. advice of their general practitioner and consider other information important than patients who have not compared hospitals.</p> <p>Methods</p> <p>337 new surgical patients completed an internet-based questionnaire. They were asked whether they had compared hospitals prior to their hospital choice and which factors influenced their choice. They were also asked to select between four and ten items of hospital information (total: 41 items) relevant for their future hospital choice. These were subsequently used in a hospital choice experiment in which participants were asked to compare hospitals in an Adaptive Choice-Based Conjoint analysis to estimate which of the hospital characteristics had the highest Relative Importance (RI).</p> <p>Results</p> <p>Patients who have compared hospitals more often used public information for their hospital choice than patients who have not compared hospitals (12.7% vs. 1.5%, p < 0.001). However, they still mostly relied on their own (47.9%) and other people's experiences (31%) rather than to base their decision on public information. Both groups valued physician's expertise (RI 20.2 [16.6-24.8] in patients comparing hospitals vs. 16.5 [14.2-18.8] in patients not comparing hospitals) and waiting time (RI 15.1 [10.7-19.6] vs. 15.6 [13.2-17.9] respectively) as most important public information. Patients who have compared hospitals assigned greater importance to information on wound infections (p = 0.010) and respect for patients (p = 0.022), but lower importance to hospital distance (p = 0.041).</p> <p>Conclusion</p> <p>Public information has limited impact on patient's hospital choice, even in patients who have actually compared hospitals prior to hospital choice.</p

Electrophysiological characteristics of permanent atrial fibrillation: insights from research models of cardiac remodeling

[EN] Atrial fibrillation (AF) results in a remodeling of the electrical and structural characteristics of the cardiac tissue which dramatically reduces the efficacy of pharmacological and catheter-based ablation therapies. Recent experimental and clinical results have demonstrated that the complexity of the fibrillatory process significantly differs in paroxysmal versus persistent AF; however, the lack of appropriate research models of remodeled atrial tissue precludes the elucidation of the underlying AF mechanisms and the identification of appropriated therapeutic targets. Here, we summarize the different research models used to date, highlighting the lessons learned from them and pointing to the new doors that should be open for the development of innovative treatments for AF.The authors were supported by grants from the Spanish Ministry of Science and Innovation (PLE2009-0152), the Instituto de Salud Carlos III (Ministry of Economy and Competitiveness, Spain: PI13-01882 and PI13-00903) the Red de Investigacion Cardiovacular (RIC) from Instituto de Salud Carlos III (Ministry of Economy and Competitiveness, Spain). F Atienza served on the advisory board of Medtronic and has received research funding from St. Jude Medical Spain. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.Climent, A.; Guillem Sánchez, MS.; Atienza Fernández, F.; Fernandez-Aviles, F. (2014). Electrophysiological characteristics of permanent atrial fibrillation: insights from research models of cardiac remodeling. Expert Review of Cardiovascular Therapy. 13(1):1-3. https://doi.org/10.1586/14779072.2015.986465S1313

Insulin utilizes the PI 3-kinase pathway to inhibit SP-A gene expression in lung epithelial cells

BACKGROUND: It has been proposed that high insulin levels may cause delayed lung development in the fetuses of diabetic mothers. A key event in lung development is the production of adequate amounts of pulmonary surfactant. Insulin inhibits the expression of surfactant protein A (SP-A), the major surfactant-associated protein, in lung epithelial cells. In the present study, we investigated the signal transduction pathways involved in insulin inhibition of SP-A gene expression. METHODS: H441 cells, a human lung adenocarcinoma cell line, or human fetal lung explants were incubated with or without insulin. Transcription run-on assays were used to determine SP-A gene transcription rates. Northern blot analysis was used to examine the effect of various signal transduction inhibitors on SP-A gene expression. Immunoblot analysis was used to evaluate the levels and phosphorylation states of signal transduction protein kinases. RESULTS: Insulin decreased SP-A gene transcription in human lung epithelial cells within 1 hour. Insulin did not affect p44/42 mitogen-activated protein kinase (MAPK) phosphorylation and the insulin inhibition of SP-A mRNA levels was not affected by PD98059, an inhibitor of the p44/42 MAPK pathway. In contrast, insulin increased p70 S6 kinase Thr389 phosphorylation within 15 minutes. Wortmannin or LY294002, both inhibitors of phosphatidylinositol 3-kinase (PI 3-kinase), or rapamycin, an inhibitor of the activation of p70 S6 kinase, a downstream effector in the PI 3-kinase pathway, abolished or attenuated the insulin-induced inhibition of SP-A mRNA levels. CONCLUSION: Insulin inhibition of SP-A gene expression in lung epithelial cells probably occurs via the rapamycin-sensitive PI 3-kinase signaling pathway

Probing the Interplay between Quantum Charge Fluctuations and Magnetic Ordering in LuFe2O4

Ferroelectric and ferromagnetic materials possess spontaneous electric and magnetic order, respectively, which can be switched by the corresponding applied electric and magnetic fields. Multiferroics combine these properties in a single material, providing an avenue for controlling electric polarization with a magnetic field and magnetism with an electric field. These materials have been intensively studied in recent years, both for their fundamental scientific interest as well as their potential applications in a broad range of magnetoelectric devices [1, 2, 3, 4]. However, the microscopic origins of magnetism and ferroelectricity are quite different, and the mechanisms producing strong coupling between them are not always well understood. Hence, gaining a deeper understanding of magnetoelectric coupling in these materials is the key to their rational design. Here, we use ultrafast optical spectroscopy to show that quantum charge fluctuations can govern the interplay between electric polarization and magnetic ordering in the charge-ordered multiferroic LuFe2O4

Post-genomic approaches to understanding interactions between fungi and their environment

Fungi inhabit every natural and anthropogenic environment on Earth. They have highly varied life-styles including saprobes (using only dead biomass as a nutrient source), pathogens (feeding on living biomass), and symbionts (co-existing with other organisms). These distinctions are not absolute as many species employ several life styles (e.g. saprobe and opportunistic pathogen, saprobe and mycorrhiza). To efficiently survive in these different and often changing environments, fungi need to be able to modify their physiology and in some cases will even modify their local environment. Understanding the interaction between fungi and their environments has been a topic of study for many decades. However, recently these studies have reached a new dimension. The availability of fungal genomes and development of post-genomic technologies for fungi, such as transcriptomics, proteomics and metabolomics, have enabled more detailed studies into this topic resulting in new insights. Based on a Special Interest Group session held during IMC9, this paper provides examples of the recent advances in using (post-)genomic approaches to better understand fungal interactions with their environments

Ultrafast transient absorption spectroscopy: principles and application to photosynthetic systems

The photophysical and photochemical reactions, after light absorption by a photosynthetic pigment–protein complex, are among the fastest events in biology, taking place on timescales ranging from tens of femtoseconds to a few nanoseconds. The advent of ultrafast laser systems that produce pulses with femtosecond duration opened up a new area of research and enabled investigation of these photophysical and photochemical reactions in real time. Here, we provide a basic description of the ultrafast transient absorption technique, the laser and wavelength-conversion equipment, the transient absorption setup, and the collection of transient absorption data. Recent applications of ultrafast transient absorption spectroscopy on systems with increasing degree of complexity, from biomimetic light-harvesting systems to natural light-harvesting antennas, are presented. In particular, we will discuss, in this educational review, how a molecular understanding of the light-harvesting and photoprotective functions of carotenoids in photosynthesis is accomplished through the application of ultrafast transient absorption spectroscopy

Detection of Functional Modes in Protein Dynamics

Proteins frequently accomplish their biological function by collective atomic motions. Yet the identification of collective motions related to a specific protein function from, e.g., a molecular dynamics trajectory is often non-trivial. Here, we propose a novel technique termed “functional mode analysis” that aims to detect the collective motion that is directly related to a particular protein function. Based on an ensemble of structures, together with an arbitrary “functional quantity” that quantifies the functional state of the protein, the technique detects the collective motion that is maximally correlated to the functional quantity. The functional quantity could, e.g., correspond to a geometric, electrostatic, or chemical observable, or any other variable that is relevant to the function of the protein. In addition, the motion that displays the largest likelihood to induce a substantial change in the functional quantity is estimated from the given protein ensemble. Two different correlation measures are applied: first, the Pearson correlation coefficient that measures linear correlation only; and second, the mutual information that can assess any kind of interdependence. Detecting the maximally correlated motion allows one to derive a model for the functional state in terms of a single collective coordinate. The new approach is illustrated using a number of biomolecules, including a polyalanine-helix, T4 lysozyme, Trp-cage, and leucine-binding protein