Dynamics of ligand-protein interactions - impact on drug discovery

Introducing a new drug to market is a lengthy and expensive process (typically 10-15 years and $1.7 billion). Better understanding of how and why a drug molecule binds to a target and what changes in the atomistic structure and chemistry could improve the binding affinity and shorten the process. In addition to structure-based approaches, the role of thermodynamics and molecular motions in binding selectivity and efficiency have attracted increasing attention. Whilst calorimetric methods can quantify total free energy and entropy change, it is difficult to estimate contributions from the different components of entropy, one of the largest unknowns being the magnitude of the configurational entropy. Molecular dynamics (MD) simulations of the drug and target protein can provide more details of the different atomistic movements contributing to the total entropy change, thus potentially providing valuable clues for lead optimisation. In this study we use the well characterised N-terminal domain of the Hsp90 chaperone protein as a model system to study the changes in conformational flexibility (configurational entropy) upon binding of small molecule inhibitors using MD simulations, NMR and ITC. We show that the two inhibitors studied cause different changes in the protein dynamics. These effects were seen with NMR relaxation dispersion methods and with MD but the dynamic changes however are not reflected in the global ITC parameters. Here the water is assumed to have a dominating effect in the overall entropy change. However, as some Hsp90 clients have been shown to preferentially interact with only one conformation of the protein, we propose that the changes seen with NMR and MD could be of interest for drug design. Manipulating the dynamics by small molecules could favour interaction with a subset of client proteins, without affecting the interaction of others, all together providing specificity and potentially allowing to design an ‘ideal’ drug that only prevents the folding of ‘bad’ cancer related proteins without affecting Hsp90 functions in the normal cells. As the MD simulations also reflect these dynamic changes, we propose that simulations could be also used as a screening tool for selecting which inhibitors could be taken for further development in the lab

Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters

Standard RGB-D trackers treat the target as an inherently 2D structure, which makes modelling appearance changes related even to simple out-of-plane rotation highly challenging. We address this limitation by proposing a novel long-term RGB-D tracker - Object Tracking by Reconstruction (OTR). The tracker performs online 3D target reconstruction to facilitate robust learning of a set of view-specific discriminative correlation filters (DCFs). The 3D reconstruction supports two performance-enhancing features: (i) generation of accurate spatial support for constrained DCF learning from its 2D projection and (ii) point cloud based estimation of 3D pose change for selection and storage of view-specific DCFs which are used to robustly localize the target after out-of-view rotation or heavy occlusion. Extensive evaluation of OTR on the challenging Princeton RGB-D tracking and STC Benchmarks shows it outperforms the state-of-the-art by a large margin

A multi-user virtual environment to support students’ self-efficacy and interest in science: A latent growth model analysis

Using latent growth models, we explored: (a) The effect of middle school students’ (n=189) preintervention science self-efficacy and science interest on their initial interest in an Ecosystems Multi-User Virtual Environment (EcoMUVE) and the rate of change in their interest in EcoMUVE; and (b) the mediating effect of students’ initial interest in EcoMUVE and rate of change in interest on students’ post-intervention science self-efficacy and interest in science. Results showed that: (1) students’ pre-intervention self-efficacy for science had an effect both on students’ triggered situational interest for EcoMUVE and on students’ maintained situational interest for EcoMUVE; (2) both triggering and maintaining situational interest in EcoMUVE were important in developing students’ science self-efficacy. In fact, maintained situational interest was the stronger predictor; and (3) maintained situational interest for EcoMUVE translated into individual interest for the science content. Results support and extend social cognitive theory as well as models of interest development

Spatiotemporal lagging of predictors improves machine learning estimates of atmosphere–forest CO2 exchange

Accurate estimates of net ecosystem CO2 exchange (NEE) would improve the understanding of natural carbon sources and sinks and their role in the regulation of global atmospheric carbon. In this work, we use and compare the random forest (RF) and the gradient boosting (GB) machine learning (ML) methods for predicting year-round 6 h NEE over 1996-2018 in a pine-dominated boreal forest in southern Finland and analyze the predictability of NEE. Additionally, aggregation to weekly NEE values was applied to get information about longer term behavior of the method. The meteorological ERA5 reanalysis variables were used as predictors. Spatial and temporal neighborhood (predictor lagging) was used to provide the models more data to learn from, which was found to improve considerably the accuracy of both ML approaches compared to using only the nearest grid cell and time step. Both ML methods can explain temporal variability of NEE in the observational site of this study with meteorological predictors, but the GB method was more accurate. Only minor signs of overfitting could be detected for the GB algorithm when redundant variables were included. The accuracy of the approaches, measured mainly using cross-validated R-2 score between the model result and the observed NEE, was high, reaching a best estimate value of 0.92 for GB and 0.88 for RF. In addition to the standard RF approach, we recommend using GB for modeling the CO2 fluxes of the ecosystems due to its potential for better performance.Peer reviewe

Post resuscitation care of out-of-hospital cardiac arrest patients in the Nordic countries : a questionnaire study

Background: Aim of this study was to compare post resuscitation care of out-of-hospital cardiac arrest (OHCA) patients in Nordic (Denmark, Finland, Iceland, Norway, Sweden) intensive care units (ICUs). Methods: An online questionnaire was sent to Nordic ICUs in 2012 and was complemented by an additional one in 2014. Results: The first questionnaire was sent to 188 and the second one to 184 ICUs. Response rates were 51 % and 46 %. In 2012, 37 % of the ICUs treated all patients resuscitated from OHCA with targeted temperature management (TTM) at 33 degrees C. All OHCA patients admitted to the ICU were treated with TTM at 33 degrees C more often in Norway (69 %) compared to Finland (20 %) and Sweden (25 %), p 0.02 and 0.014. In 2014, 63 % of the ICUs still use TTM at 33 degrees C, but 33 % use TTM at 36 degrees C. Early coronary angiography (CAG) and possible percutaneous coronary intervention (PCI) was routinely provided for all survivors of OHCA in 39 % of the hospitals in 2012 and in 28 % of the hospitals in 2014. Routine CAG for all actively treated victims of OHCA was performed more frequently in Sweden (51 %) and in Norway (54 %) compared to Finland (13 %), p 0.014 and 0.042. Conclusions: Since 2012, TTM at 36 degrees C has been implemented in some ICUs, but TTM at 33 degrees C is used in majority of the ICUs. TTM at 33 or 36 degrees C and primary CAG are not routinely provided for all OHCA survivors and the criteria for these and ICU admission are variable. Best practices as a uniform approach to the optimal care of the resuscitated patient should be sought in the Nordic Countries.Peer reviewe

Exploring Ecosystems from the Inside: How Immersive Multi-user Virtual Environments Can Support Development of Epistemologically Grounded Modeling Practices in Ecosystem Science Instruction

Recent reform efforts and the next generation science standards emphasize the importance of incorporating authentic scientific practices into science instruction. Modeling can be a particularly challenging practice to address because modeling occurs within a socially structured system of representation that is specific to a domain. Further, in the process of modeling, experts interact deeply with domain-specific content knowledge and integrate modeling with other scientific practices in service of a larger investigation. It can be difficult to create learning experiences enabling students to engage in modeling practices that both honor the position of the novice along a spectrum toward more expert understanding and align well with the practices and reasoning used by experts in the domain. In this paper, we outline the challenges in teaching modeling practices specific to the domain of ecosystem science, and we present a description of a curriculum built around an immersive virtual environment that offers unique affordances for supporting student engagement in modeling practices. Illustrative examples derived from pilot studies suggest that the tools and context provided within the immersive virtual environment helped support student engagement in modeling practices that are epistemologically grounded in the field of ecosystem science

Identification of potential organ donors after aneurysmal subarachnoid hemorrhage in a population-based neurointensive care in Eastern Finland

To analyze the organ donation action in population-based neurointensive care of acute aneurysmal subarachnoid hemorrhage (aSAH) and to seek factors that would improve the identification of potential organ donors (PODs) and increase the donor conversion rate (DCR) after aSAH. The Kuopio Intracranial Aneurysm Database, prospective since 1995, includes all aSAH patients admitted to the Kuopio University Hospital (KUH) from its defined Eastern Finnish catchment population. We analyzed 769 consecutive acute aSAH patients from 2005 to 2015, including their data from the Finnish Transplantation Unit and the national clinical registries. We analyzed PODs vs. actual donors among the 145 (19%) aSAH patients who died within 14 days of admission. Finland had implemented the national presumed consent (opt-out) within the study period in the end of 2010. We retrospectively identified 83 (57%) PODs while only 49 (34%) had become actual donors (total DCR 59%); the causes for non-donorship were 15/34 (44%) refusals of consent, 18/34 (53%) medical contraindications for donation, and 1/34 (3%) failure of recognition. In 2005-2010, there were 11 refusals by near relatives with DCR 52% (29/56) and only three in 2011-2015 with DCR 74% (20/27). Severe condition on admission (Hunt and Hess grade IV or V) independently associated with the eventual POD status. Nearly 20% of all aSAH patients acutely admitted to neurointensive care from a defined catchment population died within 14 days, almost half from cardiopulmonary causes at a median age of 69 years. Of all aSAH patients, 11% were considered as potential organ donors (PODs). Donor conversion rate (DCR) was increased from 52 to 74% after the national presumed consent (opt-out). Implicitly, DCR among aSAH patients could be increased by admitting them to the intensive care regardless of dismal prognosis for the survival, along a dedicated organ donation program for the catchment population.Peer reviewe