Structure-based stabilization of insulin as a therapeutic protein assembly via enhanced aromatic-aromatic interactions

Key contributions to protein structure and stability are provided by weakly polar interactions, which arise from asymmetric electronic distributions within amino acids and peptide bonds. Of particular interest are aromatic side chains whose directional π-systems commonly stabilize protein interiors and interfaces. Here, we consider aromatic-aromatic interactions within a model protein assembly: the dimer interface of insulin. Semi-classical simulations of aromatic-aromatic interactions at this interface suggested that substitution of residue TyrB26 by Trp would preserve native structure while enhancing dimerization (and hence hexamer stability). The crystal structure of a [TrpB26]insulin analog (determined as a T3Rf3 zinc hexamer at a resolution of 2.25 Å) was observed to be essentially identical to that of WT insulin. Remarkably and yet in general accordance with theoretical expectations, spectroscopic studies demonstrated a 150-fold increase in the in vitro lifetime of the variant hexamer, a critical pharmacokinetic parameter influencing design of long-acting formulations. Functional studies in diabetic rats indeed revealed prolonged action following subcutaneous injection. The potency of the TrpB26-modified analog was equal to or greater than an unmodified control. Thus, exploiting a general quantum-chemical feature of protein structure and stability, our results exemplify a mechanism-based approach to the optimization of a therapeutic protein assembly

How is Gaze Influenced by Image Transformations? Dataset and Model

Data size is the bottleneck for developing deep saliency models, because collecting eye-movement data is very time consuming and expensive. Most of current studies on human attention and saliency modeling have used high quality stereotype stimuli. In real world, however, captured images undergo various types of transformations. Can we use these transformations to augment existing saliency datasets? Here, we first create a novel saliency dataset including fixations of 10 observers over 1900 images degraded by 19 types of transformations. Second, by analyzing eye movements, we find that observers look at different locations over transformed versus original images. Third, we utilize the new data over transformed images, called data augmentation transformation (DAT), to train deep saliency models. We find that label preserving DATs with negligible impact on human gaze boost saliency prediction, whereas some other DATs that severely impact human gaze degrade the performance. These label preserving valid augmentation transformations provide a solution to enlarge existing saliency datasets. Finally, we introduce a novel saliency model based on generative adversarial network (dubbed GazeGAN). A modified UNet is proposed as the generator of the GazeGAN, which combines classic skip connections with a novel center-surround connection (CSC), in order to leverage multi level features. We also propose a histogram loss based on Alternative Chi Square Distance (ACS HistLoss) to refine the saliency map in terms of luminance distribution. Extensive experiments and comparisons over 3 datasets indicate that GazeGAN achieves the best performance in terms of popular saliency evaluation metrics, and is more robust to various perturbations. Our code and data are available at: https://github.com/CZHQuality/Sal-CFS-GAN

Identification of G-quadruplex DNA/RNA binders: Structure-based virtual screening and biophysical characterization

Background Recent findings demonstrated that, in mammalian cells, telomere DNA (Tel) is transcribed into telomeric repeat-containing RNA (TERRA), which is involved in fundamental biological processes, thus representing a promising anticancer target. For this reason, the discovery of dual (as well as selective) Tel/TERRA G-quadruplex (G4) binders could represent an innovative strategy to enhance telomerase inhibition. Methods Initially, docking simulations of known Tel and TERRA active ligands were performed on the 3D coordinates of bimolecular G4 Tel DNA (Tel2) and TERRA (TERRA2). Structure-based pharmacophore models were generated on the best complexes and employed for the virtual screening of ~ 257,000 natural compounds. The 20 best candidates were submitted to biophysical assays, which included circular dichroism and mass spectrometry at different K+ concentrations. Results Three hits were here identified and characterized by biophysical assays. Compound 7 acts as dual Tel2/TERRA2 G4-ligand at physiological KCl concentration, while hits 15 and 17 show preferential thermal stabilization for Tel2 DNA. The different molecular recognition against the two targets was also discussed. Conclusions Our successful results pave the way to further lead optimization to achieve both increased selectivity and stabilizing effect against TERRA and Tel DNA G4s. General significance The current study combines for the first time molecular modelling and biophysical assays applied to bimolecular DNA and RNA G4s, leading to the identification of innovative ligand chemical scaffolds with a promising anticancer profile. This article is part of a Special Issue entitled "G-quadruplex" Guest Editor: Dr. Concetta Giancola and Dr. Daniela Montesarchio

Engineered ferritin for lanthanide binding

Ferritin H-homopolymers have been extensively used as nanocarriers for diverse applications in the targeted delivery of drugs and imaging agents, due to their unique ability to bind the transferrin receptor (CD71), highly overexpressed in most tumor cells. In order to incorporate novel fluorescence imaging properties, we have fused a lanthanide binding tag (LBT) to the C-terminal end of mouse H-chain ferritin, HFt. The HFt-LBT possesses one high affinity Terbium binding site per each of the 24 subunits provided by six coordinating aminoacid side chains and a tryptophan residue in its close proximity and is thus endowed with strong FRET sensitization properties. Accordingly, the characteristic Terbium emission band at 544 nm for the HFt-LBT Tb(III) complex was detectable upon excitation of the tag enclosed at two order of magnitude higher intensity with respect to the wtHFt protein. X-ray data at 2.9 Å and cryo-EM at 7 Å resolution demonstrated that HFt-LBT is correctly assembled as a 24-mer both in crystal and in solution. On the basis of the intrinsic Tb(III) binding properties of the wt protein, 32 additional Tb(III) binding sites, located within the natural iron binding sites of the protein, were identified besides the 24 Tb(III) ions coordinated to the LBTs. HFt-LBT Tb(III) was demonstrated to be actively uptaken by selected tumor cell lines by confocal microscopy and FACS analysis of their FITC derivatives, although direct fluorescence from Terbium emission could not be singled out with conventional, 295–375 nm, fluorescence excitation

Internal combustion engine sensor network analysis using graph modeling

In recent years there has been a rapid development in technologies for smart monitoring applied to many different areas (e.g. building automation, photovoltaic systems, etc.). An intelligent monitoring system employs multiple sensors distributed within a network to extract useful information for decision-making. The management and the analysis of the raw data derived from the sensor network includes a number of specific challenges still unresolved, related to the different communication standards, the heterogeneous structure and the huge volume of data. In this paper we propose to apply a method based on complex network theory, to evaluate the performance of an Internal Combustion Engine. Data are gathered from the OBD sensor subset and from the emission analyzer. The method provides for the graph modeling of the sensor network, where the nodes are represented by the sensors and the edge are evaluated with non-linear statistical correlation functions applied to the time series pairs. The resulting functional graph is then analyzed with the topological metrics of the network, to define characteristic proprieties representing useful indicator for the maintenance and diagnosis