Exploiting protein flexibility to predict the location of allosteric sites

Background: Allostery is one of the most powerful and common ways of regulation of protein activity. However, for most allosteric proteins identified to date the mechanistic details of allosteric modulation are not yet well understood. Uncovering common mechanistic patterns underlying allostery would allow not only a better academic understanding of the phenomena, but it would also streamline the design of novel therapeutic solutions. This relatively unexplored therapeutic potential and the putative advantages of allosteric drugs over classical active-site inhibitors fuel the attention allosteric-drug research is receiving at present. A first step to harness the regulatory potential and versatility of allosteric sites, in the context of drug-discovery and design, would be to detect or predict their presence and location. In this article, we describe a simple computational approach, based on the effect allosteric ligands exert on protein flexibility upon binding, to predict the existence and position of allosteric sites on a given protein structure. Results: By querying the literature and a recently available database of allosteric sites, we gathered 213 allosteric proteins with structural information that we further filtered into a non-redundant set of 91 proteins. We performed normal-mode analysis and observed significant changes in protein flexibility upon allosteric-ligand binding in 70% of the cases. These results agree with the current view that allosteric mechanisms are in many cases governed by changes in protein dynamics caused by ligand binding. Furthermore, we implemented an approach that achieves 65% positive predictive value in identifying allosteric sites within the set of predicted cavities of a protein (stricter parameters set, 0.22 sensitivity), by combining the current analysis on dynamics with previous results on structural conservation of allosteric sites. We also analyzed four biological examples in detail, revealing that this simple coarse-grained methodology is able to capture the effects triggered by allosteric ligands already described in the literature. Conclusions: We introduce a simple computational approach to predict the presence and position of allosteric sites in a protein based on the analysis of changes in protein normal modes upon the binding of a coarse-grained ligand at predicted cavities. Its performance has been demonstrated using a newly curated non-redundant set of 91 proteins with reported allosteric properties. The software developed in this work is available upon request from the authors

Active Nuclear Receptors Exhibit Highly Correlated AF-2 Domain Motions

Nuclear receptor ligand binding domains (LBDs) convert ligand binding events into changes in gene expression by recruiting transcriptional coregulators to a conserved activation function-2 (AF-2) surface. While most nuclear receptor LBDs form homo- or heterodimers, the human nuclear receptor pregnane X receptor (PXR) forms a unique and essential homodimer and is proposed to assemble into a functional heterotetramer with the retinoid X receptor (RXR). How the homodimer interface, which is located 30 Å from the AF-2, would affect function at this critical surface has remained unclear. By using 20- to 30-ns molecular dynamics simulations on PXR in various oligomerization states, we observed a remarkably high degree of correlated motion in the PXR–RXR heterotetramer, most notably in the four helices that create the AF-2 domain. The function of such correlation may be to create “active-capable” receptor complexes that are ready to bind to transcriptional coactivators. Indeed, we found in additional simulations that active-capable receptor complexes involving other orphan or steroid nuclear receptors also exhibit highly correlated AF-2 domain motions. We further propose a mechanism for the transmission of long-range motions through the nuclear receptor LBD to the AF-2 surface. Taken together, our findings indicate that long-range motions within the LBD scaffold are critical to nuclear receptor function by promoting a mobile AF-2 state ready to bind coactivators

Locking of Turing patterns in the chlorine dioxide-iodine-malonic acid reaction with one-dimensional spatial periodic forcing

We use the photosensitive chlorine dioxide-iodine-malonic acid reaction-diffusion system to study wavenumber locking of Turing patterns with spatial periodic forcing. Wavenumber-locked stripe patterns are the typical resonant structures that labyrinthine patterns exhibit in response to one-dimensional forcing by illumination when images of stripes are projected on a working medium. Our experimental results reveal that segmented oblique, hexagonal and rectangular patterns can also be obtained. However, these two-dimensional resonant structures only develop in a relatively narrow range of forcing parameters, where the unforced stripe pattern is in close proximity to the domain of hexagonal patterns. Numerical simulations based on a model that incorporates the forcing by illumination using an additive term reproduce well the experimental observations. These findings confirm that additive one-dimensional forcing can generate a two-dimensional resonant response. However, such a response is considerably less robust than the effect of multiplicative forcing

Machine learning methods for anomaly detection in IoT networks, with illustrations

International audienceIoT devices have been the target of 100 million attacks in the first half of 2019 [1]. According to [2], there will be more than 64 billion Internet of Things (IoT) devices by 2025. It is thus crucial to secure IoT networks and devices, which include significant devices like medical kit or autonomous car. The problem is complicated by the wide range of possible attacks and their evolution, by the limited computing resources and storage resources available on devices. We begin by introducing the context and a survey of Intrusion Detection System (IDS) for IoT networks with a state of the art. So as to test and compare solutions, we consider available public datasets and select the CIDDS-001 Dataset. We implement and test several machine learning algorithms and show that it is relatively easy to obtain reproducible results [20] at the state-of-the-art. Finally, we discuss embedding such algorithms in the IoT context and point-out the possible interest of very simple rules

Predicting Outpatient Process Flows to Minimise the Cost of Handling Returning Patients: A Case Study

We describe an application of process predictive monitoring at an outpatient clinic in a large hospital. A model is created to predict which patients will wrongly refer to the outpatient clinic, instead of directly to other departments, when returning to get treatment after an initial visit. Four variables are identified to minimise the cost of handling these patients: the cost of giving appropriate guidance to them, the cost of handling patients taking a non-compliant flow by wrongly referring to the outpatient clinic, and the false positive/negative rates of the predictive model adopted. The latter determine the situations in which patients have not received guidance when they should have had or have been guided even though not necessary, respectively. Using these variables, a cost model is built to identify which combinations of process intervention/redesign options and predictive models are likely to minimise the cost overhead of handling the returning patients

Adama Diop

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Sunu Rew

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