Square Key Matrix Management Scheme in Wireless Sensor Networks

In this paper we propose a symmetric cryptographic approach named Square Key Matrix Management Scheme (SKMaS) in which a sensor node named Key Distribution Server (KDS) is responsible for the security of key management. When the system starts up, the KDS sends its individual key and two sets of keys to sensor nodes. With the IDs, any two valid sensor nodes, e.g. i and j, can individually identify the corresponding communication keys (CKs) to derive a dynamic shared key (DSK) for encrypting/decrypting messages transmitted between them. When i leaves the underlying network, the CKs and the individually keys currently utilized by i can be reused by a newly joining sensor, e.g. h. However, when h joins the network, if no such previously-used IDs are available, h will be given a new ID, CKs and the individually key by the KDS. The KDS encrypts the CKs, with which an existing node q can communicate with h, with individual key so that only q rather than h can correctly decrypt the CKs. The lemmas and security analyses provided in this paper prove that the proposed system can protect at least three common attacks

From proteomics to discovery of first-in-class ST2 inhibitors active in vivo

Soluble cytokine receptors function as decoy receptors to attenuate cytokine-mediated signaling and modulate downstream cellular responses. Dysregulated overproduction of soluble receptors can be pathological, such as soluble ST2 (sST2), a prognostic biomarker in cardiovascular diseases, ulcerative colitis, and graft-versus-host disease (GVHD). Although intervention using an ST2 antibody improves survival in murine GVHD models, sST2 is a challenging target for drug development because it binds to IL-33 via an extensive interaction interface. Here, we report the discovery of small-molecule ST2 inhibitors through a combination of high-throughput screening and computational analysis. After in vitro and in vivo toxicity assessment, 3 compounds were selected for evaluation in 2 experimental GVHD models. We show that the most effective compound, iST2-1, reduces plasma sST2 levels, alleviates disease symptoms, improves survival, and maintains graft-versus-leukemia activity. Our data suggest that iST2-1 warrants further optimization to develop treatment for inflammatory diseases mediated by sST2

Pyrimido[4,5‐ d ]pyrimidin‐4(1 H )‐one Derivatives as Selective Inhibitors of EGFR Threonine 790 to Methionine 790 (T790M) Mutants

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/99681/1/8387_ftp.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/99681/2/anie_201302313_sm_miscellaneous_information.pd

Pyrimido[4,5‐ d ]pyrimidin‐4(1 H )‐one Derivatives as Selective Inhibitors of EGFR Threonine 790 to Methionine 790 (T790M) Mutants

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/99673/1/8545_ftp.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/99673/2/ange_201302313_sm_miscellaneous_information.pd

The similarity of class II HLA genotypes defines patterns of autoreactivity in idiopathic bone marrow failure disorders

Abstract Idiopathic aplastic anemia (IAA) is a rare autoimmune bone marrow failure (BMF) disorder initiated by a human leukocyte antigen (HLA)-restricted T-cell response to unknown antigens. As in other autoimmune disorders, the predilection for certain HLA profiles seems to represent an etiologic factor; however, the structure-function patterns involved in the self-presentation in this disease remain unclear. Herein, we analyzed the molecular landscape of HLA complexes of a cohort of 300 IAA patients and almost 3000 healthy and disease controls by deeply dissecting their genotypic configurations, functional divergence, self-antigen binding capabilities, and T-cell receptor (TCR) repertoire specificities. Specifically, analysis of the evolutionary divergence of HLA genotypes (HED) showed that IAA patients carried class II HLA molecules whose antigen-binding sites were characterized by a high level of structural homology, only partially explained by specific risk allele profiles. This pattern implies reduced HLA binding capabilities, confirmed by binding analysis of hematopoietic stem cell (HSC)-derived self-peptides. IAA phenotype was associated with the enrichment in a few amino acids at specific positions within the peptide-binding groove of DRB1 molecules, affecting the interface HLA-antigen-TCR β and potentially constituting the basis of T-cell dysfunction and autoreactivity. When analyzing associations with clinical outcomes, low HED was associated with risk of malignant progression and worse survival, underlying reduced tumor surveillance in clearing potential neoantigens derived from mechanisms of clonal hematopoiesis. Our data shed light on the immunogenetic risk associated with IAA etiology and clonal evolution and on general pathophysiological mechanisms potentially involved in other autoimmune disorders.Peer reviewe

CSAR Benchmark Exercise of 2010: Selection of the Protein–Ligand Complexes

ABSTRACT: A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) aims to collect available data from industry and academia which may be used for this purpose (www.csardock.org). Also, CSAR is charged with organizing community-wide exercises based on the collected data. The first of these exercises was aimed to gauge the overall state of docking and scoring, using a large and diverse data set of protein ligand complexes. Participants were asked to calculate the affinity of the complexes as provided and then recalculate with changes which may improve their specific method. This first data set was selected from existing PDB entries which had binding data (Kd or Ki) in Binding MOAD, augmented with entries from PDBbind. The final data set contains 343 diverse protein ligand complexes and spans 14 pKd. Sixteen proteins have three or more complexes in the data set, from which a user could start an inspection of congeneric series. Inherent experimental error limits the possible correlation between scores and measured affinity; R 2 is limited to ∼0.9 when fitting to the data set without over parametrizing. R 2 is limited to ∼0.8 when scoring the data set with a method trained on outside data. The details of how the data set was initially selected, and the process by which it matured t

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery