CancerLectinDB: a database of lectins relevant to cancer

The role of lectins in mediating cancer metastasis, apoptosis as well as various other signaling events has been well established in the past few years. Data on various aspects of the role of lectins in cancer is being accumulated at a rapid pace. The data on lectins available in the literature is so diverse, that it becomes difficult and time-consuming, if not impossible to comprehend the advances in various areas and obtain the maximum benefit. Not only do the lectins vary significantly in their individual functional roles, but they are also diverse in their sequences, structures, binding site architectures, quaternary structures, carbohydrate affinities and specificities as well as their potential applications. An organization of these seemingly independent data into a common framework is essential in order to achieve effective use of all the data towards understanding the roles of different lectins in different aspects of cancer and any resulting applications. An integrated knowledge base (CancerLectinDB) together with appropriate analytical tools has therefore been developed for lectins relevant for any aspect of cancer, by collating and integrating diverse data. This database is unique in terms of providing sequence, structural, and functional annotations for lectins from all known sources in cancer and is expected to be a useful addition to the number of glycan related resources now available to the community. The database has been implemented using MySQL on a Linux platform and web-enabled using Perl-CGI and Java tools. Data for individual lectins pertain to taxonomic, biochemical, domain architecture, molecular sequence and structural details as well as carbohydrate specificities. Extensive links have also been provided for relevant bioinformatics resources and analytical tools. Availability of diverse data integrated into a common framework is expected to be of high value for various studies on lectin cancer biology.

Computational analysis of proteome of H5N1 avian influenza virus to define T cell epitopes with vaccine potential

The existing vaccines against influenza are based on the generation of neutralizing antibody primarily directed against surface proteins-hernagglutinin and neuraminidase. In this work, we have computationally defined conserved T cell epitopes of proteins of influenza virus H5N1 to help in the design of a vaccine with haplotype specificity for a target population. The peptides from the proteome of H5NI irus which are predicted to bind to different HLAs, do not show similarity with peptides of human proteorne and are also identified to be generated by proteolytic cleavage. These peptides could be made use of in the design of either a DNA vaccine or a subunit vaccine against V influenza. (c) 2007 Elsevier Ltd. All rights reserved.

Conformationally locked thiosugars alpha-mannosidase inhibitors: Synthesis, and docking studies as potent biochemical

series of thiosugar derivatives (thiolevomannosans) derived from mannose were synthesized and their inhibitory activity was tested against alpha-mannosidase (jack bean). These inhibitors were found to be more potent than the well-known inhibitors like kifunensine and deoxymannojirimycin based on docking and biochemical studies. The sulfone derivative 10 was shown to be the best inhibitor of alpha-mannosidase with the K-i value of 350 nM. (c) 2007 Elsevier Ltd. All rights reserved.

Functional interaction of diphenols with polyphenol oxidase - Molecular determinants of substrate/inhibitor specificity SO FEBS JOURNAL

Polyphenol oxidase (PPO) catalyzes the oxidation of o-diphenols to their respective quinones. The quinones autopolymerize to form dark pigments, an undesired effect. PPO is therefore the target for the development of antibrowning and antimelanization agents. A series of phenolic compounds experimentally evaluated for their binding affinity and inhibition constants were computationally docked to the active site of catechol oxidase. Docking studies suggested two distinct modes of binding, dividing the docked ligands into two groups. Remarkably, the first group corresponds to ligands determined to be substrates and the second group corresponds to reversible inhibitors. Analyses of the complexes provide structural explanations for correlating subtle changes in the position and nature of the substitutions on o-diphenols to their functional properties as substrates and inhibitors. Higher reaction rates and binding are reckoned by additional interactions of the substrates with key residues that line the hydrophobic cavity. The docking results suggest that inhibition of oxidation stems from an interaction between the aromatic carboxylic acid group and the apical His 109 of the four coordinates of the trigonal pyramidal coordination polyhedron of CuA. The spatial orientation of the hydroxyl in relation to the carboxylic group either allows a perfect fit in the substrate cavity, leading to inhibition, or because of a steric clash flips the molecule vertically, facilitating oxidation. This is the first study to explain, at the molecular level, the determinants Of substrate and inhibitor specificity of a catechol oxidase, thereby providing a platform for the design of selective inhibitors useful to both the food and pharmaceutical industries.

Two- and Three-Dimensional Open-Framework Uranium Arsenates: Synthesis, Structure, and Characterization

Hydrothermal reactions between uranium salts and arsenic pentoxide in the presence of two different amines yielded six new uranium arsenate phases exhibiting open-framework structures, ethylenediamine (en): [C2N2H9]-[(UO2)(ASO(4))] I; [C2N2H10][(UO2)F(HASO(4))]2 center dot 4H(2)O, II; [C2N2H9][U2F5(HASO(4))(2)], III; [C2N2H9][UF2(ASO(4))], IV; diethylenetriamine (DETA), [C4N3H16][U2F3(ASO(4))(2)(HAsO4)] V; and [C4N3H16][U2F6(AsO4)(HAsO4)], VI. The structures were determined using single crystal studies, which revealed two- (I, II, V) and three-dimensional (III, IV, VI) structures for the uranium arsenates. The uranium atom, in these compounds, exhibits considerable variations in the coordination (6 to 9) that appears to have some correlation with the synthetic conditions. The water molecules in [C2N2H10][(UO2)F(HAsO4)](2 center dot)4H(2)O, II, could be reversibly removed, and the dehydrated phase, [C2N2H10][(UO2)F(HAsO4)](2), IIa, was also characterized using single crystal studies. The observation of many mineralogical structures in the present compounds suggests that the hydrothermal method could successfully replicate the geothermal conditions. As part of this study, we have observed autunite, Ca[(UO2)(PO4)](2)(H2O)(11), metavauxite, [Fe(H2O)(6)][Al(OH)(H2O)(PO4)](2), finarite, PbCU(SO4)(OH)(2), and tancoite, LiNa2H[Al(PO4)(2)(OH)], structures. The repeated observation of the secondary building unit, SBU-4, in many of the uranium arsenate structures suggests that these are viable building units. Optical studies on the uranium arsenate compound, [C4N3H16][U2F6(AsO4)(HASO(4))), VI, containing uranium in the +4 oxidation state indicates a blue emission through an upconversion process. The compound also exhibits antiferromagnetic behavior.

Perpetuation of immunological memory through common MHC-I binding modes of peptidomimic and antigenic peptides

Understanding the molecular mechanisms of immunological memory assumes importance in vaccine design. We had earlier hypothesized a mechanism for the maintenance of immunological memory through the operation of a network of idiotypic and anti-idiotypic antibodies (Ab2). Peptides derived from an internal image carrying anti-idiotypic antibody are hypothesized to facilitate the perpetuation of antigen specific T cell memory through similarity in peptide-MHC binding as that of the antigenic peptide. In the present work, the existence of such peptidomimics of the antigen in the Ab2 variable region and their similarity of MHC-I binding was examined by bioinformatics approaches. The analysis employing three known viral antigens and one tumor-associated antigen shows that peptidomimics from Ab2 variable regions have structurally similar MHC-I binding patterns as compared to antigenic peptides, indicating a structural basis for memory perpetuation. (C)) 2007 Elsevier Inc. All rights reserved.

Prediction of fatigue strength in plain and reinforced concrete beams

A fatigue crack propagation model for concrete is proposed based on the concepts of fracture mechanics. This model takes into account the loading history, frequency of applied load, and size, effect parameters. Using this model, a method is described based on linear elastic fracture mechanics to assess the residual strength of cracked plain and reinforced concrete (RC) beams. This could be used to predict the residual strength (load carrying capacity) of cracked or damaged plain and reinforced concrete beams at a given level of damage. It has been seen that the fatigue crack propagation rate increases as. the size of plain concrete, beam increases indicating an increase in brittleness. In reinforced concrete (RC) beams, the fracture process becomes stable only when the beam is sufficiently reinforced.

Parallel implementation of AutoDock

Computational docking of ligands to protein structures is a key step in structure-based drug design. Currently, the time required for each docking run is high and thus limits the use of docking in a high-throughput manner, warranting parallelization of docking algorithms. AutoDock, a widely used tool, has been chosen for parallelization. Near-linear increases in speed were observed with 96 processors, reducing the time required for docking ligands to HIV-protease from 81 min, as an example, on a single IBM Power-5 processor ( 1.65 GHz), to about 1 min on an IBM cluster, with 96 such processors. This implementation would make it feasible to perform virtual ligand screening using AutoDock.

Robust heuristics for scalable optimization of complex SQL queries

Modern database systems incorporate a query optimizer to identify the most efficient "query execution plan" for executing the declarative SQL queries submitted by users. A dynamic-programming-based approach is used to exhaustively enumerate the combinatorially large search space of plan alternatives and, using a cost model, to identify the optimal choice. While dynamic programming (DP) works very well for moderately complex queries with up to around a dozen base relations, it usually fails to scale beyond this stage due to its inherent exponential space and time complexity. Therefore, DP becomes practically infeasible for complex queries with a large number of base relations, such as those found in current decision-support and enterprise management applications. To address the above problem, a variety of approaches have been proposed in the literature. Some completely jettison the DP approach and resort to alternative techniques such as randomized algorithms, whereas others have retained DP by using heuristics to prune the search space to computationally manageable levels. In the latter class, a well-known strategy is "iterative dynamic programming" (IDP) wherein DP is employed bottom-up until it hits its feasibility limit, and then iteratively restarted with a significantly reduced subset of the execution plans currently under consideration. The experimental evaluation of IDP indicated that by appropriate choice of algorithmic parameters, it was possible to almost always obtain "good" (within a factor of twice of the optimal) plans, and in the few remaining cases, mostly "acceptable" (within an order of magnitude of the optimal) plans, and rarely, a "bad" plan. While IDP is certainly an innovative and powerful approach, we have found that there are a variety of common query frameworks wherein it can fail to consistently produce good plans, let alone the optimal choice. This is especially so when star or clique components are present, increasing the complexity of th- e join graphs. Worse, this shortcoming is exacerbated when the number of relations participating in the query is scaled upwards.

An automated framework for understanding structural variations in the binding grooves of MHC class II molecules

Background: MHC/HLA class II molecules are important components of the immune system and play a critical role in processes such as phagocytosis. Understanding peptide recognition properties of the hundreds of MHC class II alleles is essential to appreciate determinants of antigenicity and ultimately to predict epitopes. While there are several methods for epitope prediction, each differing in their success rates, there are no reports so far in the literature to systematically characterize the binding sites at the structural level and infer recognition profiles from them. Results: Here we report a new approach to compare the binding sites of MHC class II molecules using their three dimensional structures. We use a specifically tuned version of our recent algorithm, PocketMatch. We show that our methodology is useful for classification of MHC class II molecules based on similarities or differences among their binding sites. A new module has been used to define binding sites in MHC molecules. Comparison of binding sites of 103 MHC molecules, both at the whole groove and individual sub-pocket levels has been carried out, and their clustering patterns analyzed. While clusters largely agree with serotypic classification, deviations from it and several new insights are obtained from our study. We also present how differences in sub-pockets of molecules associated with a pair of autoimmune diseases, narcolepsy and rheumatoid arthritis, were captured by PocketMatch(13). Conclusion: The systematic framework for understanding structuralvariations in MHC class II molecules enables large scale comparison of binding grooves and sub-pockets, which is likely to have direct implications towards predicting epitopes and understanding peptide binding preferences.

An automated framework for understanding structural variations in the binding grooves of MHC class II molecules

Background: MHC/HLA class II molecules are important components of the immune system and play a critical role in processes such as phagocytosis. Understanding peptide recognition properties of the hundreds of MHC class II alleles is essential to appreciate determinants of antigenicity and ultimately to predict epitopes. While there are several methods for epitope prediction, each differing in their success rates, there are no reports so far in the literature to systematically characterize the binding sites at the structural level and infer recognition profiles from them. Results: Here we report a new approach to compare the binding sites of MHC class II molecules using their three dimensional structures. We use a specifically tuned version of our recent algorithm, PocketMatch. We show that our methodology is useful for classification of MHC class II molecules based on similarities or differences among their binding sites. A new module has been used to define binding sites in MHC molecules. Comparison of binding sites of 103 MHC molecules, both at the whole groove and individual sub-pocket levels has been carried out, and their clustering patterns analyzed. While clusters largely agree with serotypic classification, deviations from it and several new insights are obtained from our study. We also present how differences in sub-pockets of molecules associated with a pair of autoimmune diseases, narcolepsy and rheumatoid arthritis, were captured by PocketMatch(13). Conclusion: The systematic framework for understanding structural variations in MHC class II molecules enables large scale comparison of binding grooves and sub-pockets, which is likely to have direct implications towards predicting epitopes and understanding peptide binding preferences.

A systems perspective of host-pathogen interactions: predicting disease outcome in tuberculosis

The complex web of interactions between the host immune system and the pathogen determines the outcome of any infection. A computational model of this interaction network, which encodes complex interplay among host and bacterial components, forms a useful basis for improving the understanding of pathogenesis, in filling knowledge gaps and consequently to identify strategies to counter the disease. We have built an extensive model of the Mycobacterium tuberculosis host-pathogen interactome, consisting of 75 nodes corresponding to host and pathogen molecules, cells, cellular states or processes. Vaccination effects, clearance efficiencies due to drugs and growth rates have also been encoded in the model. The system is modelled as a Boolean network. Virtual deletion experiments, multiple parameter scans and analysis of the system's response to perturbations, indicate that disabling processes such as phagocytosis and phagolysosome fusion or cytokines such as TNF-alpha and IFN-gamma, greatly impaired bacterial clearance, while removing cytokines such as IL-10 alongside bacterial defence proteins such as SapM greatly favour clearance. Simulations indicate a high propensity of the pathogen to persist under different conditions.

Fracture behavior of concrete–concrete interface using acoustic emission technique

The fracture behavior of concrete–concrete interface is characterized using acoustic emission (AE). Beams of different sizes having jointed interface between two different strengths of concrete are tested. The results of load, displacement, CMOD, AE-events and AE-energy are analyzed. The width of fracture process zone and damage zone are computed using AE-data and are found to be independent of size. It is observed that, as the difference in compressive strength of concrete on either side of interface increases, the load carrying capacity, number of AE-events, AE-energy, width of fracture process zone and damage zone decreases.

Vibrational force constant and electron relaxation in H2 and Li2

The force constants of H2 and Li2 are evaluated employing their extended Hartree-Fock wavefunctions by a polynomial fit of their force curves. It is suggested that, based on incomplete multiconfiguration Hartree-Fock wavefunctions, force constants calculated from the energy derivatives are numerically more accurate than those obtained from the derivatives of the Hellmann-Feynman forces. It is observed that electrons relax during the nuclear vibrations in such a fashion as to facilitate the nuclear motions.

A fast time-domain algorithm for the assessment of tissue blood flow in laser-Doppler flowmetry

In this study, we derive a fast, novel time-domain algorithm to compute the nth-order moment of the power spectral density of the photoelectric current as measured in laser-Doppler flowmetry (LDF). It is well established that in the LDF literature these moments are closely related to fundamental physiological parameters, i.e. concentration of moving erythrocytes and blood flow. In particular, we take advantage of the link between moments in the Fourier domain and fractional derivatives in the temporal domain. Using Parseval's theorem, we establish an exact analytical equivalence between the time-domain expression and the conventional frequency-domain counterpart. Moreover, we demonstrate the appropriateness of estimating the zeroth-, first- and second-order moments using Monte Carlo simulations. Finally, we briefly discuss the feasibility of implementing the proposed algorithm in hardware.