An Unusual Ring-Contraction/Rearrangement Sequence for Making Functionalized Di- and Triquinanes

A novel ring contraction/rearrangement sequence leading to functionalized 2,8-oxymethano-bridged di- and triquinane compounds is observed in the reaction of various substituted 1-methyl-4-isopropenyl-6-oxabicylo3.2.1]octan-8-ones with Lewis acids. The reaction is novel and is unprecedented for the synthesis of di- and triquinane frameworks.

On the Maximum Number of Linearly Independent Cycles and Paths in a Network

Central to network tomography is the problem of identifiability, the ability to identify internal network characteristics uniquely from end-to-end measurements. This problem is often underconstrained even when internal network characteristics such as link delays are modeled as additive constants. While it is known that the network topology can play a role in determining the extent of identifiability, there is a lack in the fundamental understanding of being able to quantify it for a given network. In this paper, we consider the problem of identifying additive link metrics in an arbitrary undirected network using measurement nodes and establishing paths/cycles between them. For a given placement of measurement nodes, we define and derive the ``link rank'' of the network-the maximum number of linearly independent cycles/paths that may be established between the measurement nodes. We achieve this in linear time. The link rank helps quantify the exact extent of identifiability in a network. We also develop a quadratic time algorithm to compute a set of cycles/paths that achieves the maximum rank.

CLAP: A web-server for automatic classification of proteins with special reference to multi-domain proteins

Background: The function of a protein can be deciphered with higher accuracy from its structure than from its amino acid sequence. Due to the huge gap in the available protein sequence and structural space, tools that can generate functionally homogeneous clusters using only the sequence information, hold great importance. For this, traditional alignment-based tools work well in most cases and clustering is performed on the basis of sequence similarity. But, in the case of multi-domain proteins, the alignment quality might be poor due to varied lengths of the proteins, domain shuffling or circular permutations. Multi-domain proteins are ubiquitous in nature, hence alignment-free tools, which overcome the shortcomings of alignment-based protein comparison methods, are required. Further, existing tools classify proteins using only domain-level information and hence miss out on the information encoded in the tethered regions or accessory domains. Our method, on the other hand, takes into account the full-length sequence of a protein, consolidating the complete sequence information to understand a given protein better. Results: Our web-server, CLAP (Classification of Proteins), is one such alignment-free software for automatic classification of protein sequences. It utilizes a pattern-matching algorithm that assigns local matching scores (LMS) to residues that are a part of the matched patterns between two sequences being compared. CLAP works on full-length sequences and does not require prior domain definitions. Pilot studies undertaken previously on protein kinases and immunoglobulins have shown that CLAP yields clusters, which have high functional and domain architectural similarity. Moreover, parsing at a statistically determined cut-off resulted in clusters that corroborated with the sub-family level classification of that particular domain family. Conclusions: CLAP is a useful protein-clustering tool, independent of domain assignment, domain order, sequence length and domain diversity. Our method can be used for any set of protein sequences, yielding functionally relevant clusters with high domain architectural homogeneity. The CLAP web server is freely available for academic use at http://nslab.mbu.iisc.ernet.in/clap/.

Sequence and conformational preferences at termini of alpha-helices in membrane proteins: Role of the helix environment

-helices are amongst the most common secondary structural elements seen in membrane proteins and are packed in the form of helix bundles. These -helices encounter varying external environments (hydrophobic, hydrophilic) that may influence the sequence preferences at their N and C-termini. The role of the external environment in stabilization of the helix termini in membrane proteins is still unknown. Here we analyze -helices in a high-resolution dataset of integral -helical membrane proteins and establish that their sequence and conformational preferences differ from those in globular proteins. We specifically examine these preferences at the N and C-termini in helices initiating/terminating inside the membrane core as well as in linkers connecting these transmembrane helices. We find that the sequence preferences and structural motifs at capping (Ncap and Ccap) and near-helical (N' and C') positions are influenced by a combination of features including the membrane environment and the innate helix initiation and termination property of residues forming structural motifs. We also find that a large number of helix termini which do not form any particular capping motif are stabilized by formation of hydrogen bonds and hydrophobic interactions contributed from the neighboring helices in the membrane protein. We further validate the sequence preferences obtained from our analysis with data from an ultradeep sequencing study that identifies evolutionarily conserved amino acids in the rat neurotensin receptor. The results from our analysis provide insights for the secondary structure prediction, modeling and design of membrane proteins. Proteins 2014; 82:3420-3436. (c) 2014 Wiley Periodicals, Inc.

Frequency and Temperature-Independent Electrical Transport Properties of 2BaO-0.5Na(2)O-2.5Nb(2)O(5)-4.5B(2)O(3) Glass-Ceramics

The temperature (300-973K) and frequency (100Hz-10MHz) response of the dielectric and impedance characteristics of 2BaO-0.5Na(2)O-2.5Nb(2)O(5)-4.5B(2)O(3) glasses and glass nanocrystal composites were studied. The dielectric constant of the glass was found to be almost independent of frequency (100Hz-10MHz) and temperature (300-600K). The temperature coefficient of dielectric constant was 8 +/- 3ppm/K in the 300-600K temperature range. The relaxation and conduction phenomena were rationalized using modulus formalism and universal AC conductivity exponential power law, respectively. The observed relaxation behavior was found to be thermally activated. The complex impedance data were fitted using the least square method. Dispersion of Barium Sodium Niobate (BNN) phase at nanoscale in a glass matrix resulted in the formation of space charge around crystal-glass interface, leading to a high value of effective dielectric constant especially for the samples heat-treated at higher temperatures. The fabricated glass nanocrystal composites exhibited P versus E hysteresis loops at room temperature and the remnant polarization (P-r) increased with the increase in crystallite size.

Use of a structural alphabet to find compatible folds for amino acid sequences

The structural annotation of proteins with no detectable homologs of known 3D structure identified using sequence-search methods is a major challenge today. We propose an original method that computes the conditional probabilities for the amino-acid sequence of a protein to fit to known protein 3D structures using a structural alphabet, known as Protein Blocks (PBs). PBs constitute a library of 16 local structural prototypes that approximate every part of protein backbone structures. It is used to encode 3D protein structures into 1D PB sequences and to capture sequence to structure relationships. Our method relies on amino acid occurrence matrices, one for each PB, to score global and local threading of query amino acid sequences to protein folds encoded into PB sequences. It does not use any information from residue contacts or sequence-search methods or explicit incorporation of hydrophobic effect. The performance of the method was assessed with independent test datasets derived from SCOP 1.75A. With a Z-score cutoff that achieved 95% specificity (i.e., less than 5% false positives), global and local threading showed sensitivity of 64.1% and 34.2%, respectively. We further tested its performance on 57 difficult CASP10 targets that had no known homologs in PDB: 38 compatible templates were identified by our approach and 66% of these hits yielded correctly predicted structures. This method scales-up well and offers promising perspectives for structural annotations at genomic level. It has been implemented in the form of a web-server that is freely available at http://www.bo-protscience.fr/forsa.

Structure-specific nuclease activity of RAGs is modulated by sequence, length and phase position of flanking double-stranded DNA

RAGs (recombination activating genes) are responsible for the generation of antigen receptor diversity through the process of combinatorial joining of different V (variable), D (diversity) and J (joining) gene segments. In addition to its physiological property, wherein RAG functions as a sequence-specific nuclease, it can also act as a structure-specific nuclease leading to genomic instability and cancer. In the present study, we investigate the factors that regulate RAG cleavage on non-B DNA structures. We find that RAG binding and cleavage on heteroduplex DNA is dependent on the length of the double-stranded flanking region. Besides, the immediate flanking double-stranded region regulates RAG activity in a sequence-dependent manner. Interestingly, the cleavage efficiency of RAGs at the heteroduplex region is influenced by the phasing of DNA. Thus, our results suggest that sequence, length and phase positions of the DNA can affect the efficiency of RAG cleavage when it acts as a structure-specific nuclease. These findings provide novel insights on the regulation of the pathological functions of RAGs.

Improved subject-independent acoustic-to-articulatory inversion

In subject-independent acoustic-to-articulatory inversion, the articulatory kinematics of a test subject are estimated assuming that the training corpus does not include data from the test subject. The training corpus in subject-independent inversion (SII) is formed with acoustic and articulatory kinematics data and the acoustic mismatch between training and test subjects is then estimated by an acoustic normalization using acoustic data drawn from a large pool of speakers called generic acoustic space (GAS). In this work, we focus on improving the SII performance through better acoustic normalization and adaptation. We propose unsupervised and several supervised ways of clustering GAS for acoustic normalization. We perform an adaptation of acoustic models of GAS using the acoustic data of the training and test subjects in SII. It is found that SII performance significantly improves (similar to 25% relative on average) over the subject-dependent inversion when the acoustic clusters in GAS correspond to phonetic units (or states of 3-state phonetic HMMs) and when the acoustic model built on GAS is adapted to training and test subjects while optimizing the inversion criterion. (C) 2014 Elsevier B.V. All rights reserved.

NrichD database: sequence databases enriched with computationally designed protein-like sequences aid in remote homology detection

NrichD ( ext-link-type=''uri'' xlink:href=''http://proline.biochem.iisc.ernet.in/NRICHD/'' xlink:type=''simple''>http://proline.biochem.iisc.ernet.in/NRICHD/)< /named-content> is a database of computationally designed protein-like sequences, augmented into natural sequence databases that can perform hops in protein sequence space to assist in the detection of remote relationships. Establishing protein relationships in the absence of structural evidence or natural `intermediately related sequences' is a challenging task. Recently, we have demonstrated that the computational design of artificial intermediary sequences/linkers is an effective approach to fill naturally occurring voids in protein sequence space. Through a large-scale assessment we have demonstrated that such sequences can be plugged into commonly employed search databases to improve the performance of routinely used sequence search methods in detecting remote relationships. Since it is anticipated that such data sets will be employed to establish protein relationships, two databases that have already captured these relationships at the structural and functional domain level, namely, the SCOP database and the Pfam database, have been `enriched' with these artificial intermediary sequences. NrichD database currently contains 3 611 010 artificial sequences that have been generated between 27 882 pairs of families from 374 SCOP folds. The data sets are freely available for download. Additional features include the design of artificial sequences between any two protein families of interest to the user.

Characterization of rice small heat shock proteins targeted to different cellular organelles

Small heat shock proteins (sHSPs) are a family of ATP-independent molecular chaperones which prevent cellular protein aggregation by binding to misfolded proteins. sHSPs form large oligomers that undergo drastic rearrangement/dissociation in order to execute their chaperone activity in protecting substrates from stress. Substrate-binding sites on sHSPs have been predominantly mapped on their intrinsically disordered N-terminal arms. This region is highly variable in sequence and length across species, and has been implicated in both oligomer formation and in mediating chaperone activity. Here, we present our results on the functional and structural characterization of five sHSPs in rice, each differing in their subcellular localisation, viz., cytoplasm, nucleus, chloroplast, mitochondria and peroxisome. We performed activity assays and dynamic light scattering studies to highlight differences in the chaperone activity and quaternary assembly of sHSPs targeted to various organelles. By cloning constructs that differ in the length and sequence of the tag in the N-terminal region, we have probed the sensitivity of sHSP oligomer assembly and chaperone activity to the length and amino acid composition of the N-terminus. In particular, we have shown that the incorporation of an N-terminal tag has significant consequences on sHSP quaternary structure.

Independent Positioning of Magnetic Nanomotors

There is considerable interest in powering and maneuvering nanostructures remotely in fluidic media using noninvasive fuel-free methods, for which small homogeneous magnetic fields are ideally suited. Current strategies include helical propulsion of chiral nanostructures, cilia-like motion of flexible filaments, and surface assisted translation of asymmetric colloidal doublets and magnetic nanorods, in all of which the individual structures are moved in a particular direction that is completely tied to the characteristics of the driving fields. As we show in this paper, when we use appropriate magnetic field configurations and actuation time scales, it is possible to maneuver geometrically identical nanostructures in different directions, and subsequently position them at arbitrary locations with respect to each other. The method reported here requires proximity of the nanomotors to a solid surface, and could be useful in applications that require remote and independent control over individual components in microfluidic environments.

Database Independent Human Emotion Recognition with Meta-Cognitive Neuro-Fuzzy Inference System

Facial emotions are the most expressive way to display emotions. Many algorithms have been proposed which employ a particular set of people (usually a database) to both train and test their model. This paper focuses on the challenging task of database independent emotion recognition, which is a generalized case of subject-independent emotion recognition. The emotion recognition system employed in this work is a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). McFIS has two components, a neuro-fuzzy inference system, which is the cognitive component and a self-regulatory learning mechanism, which is the meta-cognitive component. The meta-cognitive component, monitors the knowledge in the neuro-fuzzy inference system and decides on what-to-learn, when-to-learn and how-to-learn the training samples, efficiently. For each sample, the McFIS decides whether to delete the sample without being learnt, use it to add/prune or update the network parameter or reserve it for future use. This helps the network avoid over-training and as a result improve its generalization performance over untrained databases. In this study, we extract pixel based emotion features from well-known (Japanese Female Facial Expression) JAFFE and (Taiwanese Female Expression Image) TFEID database. Two sets of experiment are conducted. First, we study the individual performance of both databases on McFIS based on 5-fold cross validation study. Next, in order to study the generalization performance, McFIS trained on JAFFE database is tested on TFEID and vice-versa. The performance The performance comparison in both experiments against SVNI classifier gives promising results.

Hemagglutinin sequence conservation guided stem immunogen design from influenza A H3 subtype

Seasonal epidemics caused by influenza A (H1 and H3 subtypes) and B viruses are a major global health threat. The traditional, trivalent influenza vaccines have limited efficacy because of rapid antigenic evolution of the circulating viruses. This antigenic variability mediates viral escape from the host immune responses, necessitating annual vaccine updates. Influenza vaccines elicit a protective antibody response, primarily targeting the viral surface glycoprotein hemagglutinin (HA). However, the predominant humoral response is against the hypervariable head domain of HA, thereby restricting the breadth of protection. In contrast, the conserved, subdominant stem domain of HA is a potential ``universal'' vaccine candidate. We designed an HA stem-fragment immunogen from the 1968 pandemic H3N2 strain (A/Hong Kong/1/68) guided by a comprehensive H3 HA sequence conservation analysis. The biophysical properties of the designed immunogen were further improved by C-terminal fusion of a trimerization motif, ``isoleucine-zipper'', or ``foldon''. These immunogens elicited cross-reactive, antiviral antibodies and conferred partial protection against a lethal, homologous HK68 virus challenge in vivo. Furthermore, bacterial expression of these immunogens is economical and facilitates rapid scale-up.

Global response sensitivity analysis using probability distance measures and generalization of Sobol's analysis

The study introduces two new alternatives for global response sensitivity analysis based on the application of the L-2-norm and Hellinger's metric for measuring distance between two probabilistic models. Both the procedures are shown to be capable of treating dependent non-Gaussian random variable models for the input variables. The sensitivity indices obtained based on the L2-norm involve second order moments of the response, and, when applied for the case of independent and identically distributed sequence of input random variables, it is shown to be related to the classical Sobol's response sensitivity indices. The analysis based on Hellinger's metric addresses variability across entire range or segments of the response probability density function. The measure is shown to be conceptually a more satisfying alternative to the Kullback-Leibler divergence based analysis which has been reported in the existing literature. Other issues addressed in the study cover Monte Carlo simulation based methods for computing the sensitivity indices and sensitivity analysis with respect to grouped variables. Illustrative examples consist of studies on global sensitivity analysis of natural frequencies of a random multi-degree of freedom system, response of a nonlinear frame, and safety margin associated with a nonlinear performance function. (C) 2015 Elsevier Ltd. All rights reserved.