Specificity and affinity motifs for Grb2 SH2-ligand interactions

Protein–protein interactions are often mediated by the recognition of short continuous amino acid stretches on target proteins by specific binding domains. Affinity-based selection strategies have successfully been used to define recognition motifs for a large series of such protein domains. However, in many biological systems specificity of interaction may be of equal or greater importance than affinity. To address this issue we have developed a peptide library screening technology that can be used to directly define ligands for protein domains based on both affinity and specificity of interaction. We demonstrate the value of this approach by the selection of peptide ligands that are either highly specific for the Grb2 Src homology 2 (SH2) domain or that are cross-reactive between a group of related SH2 domains. Examination of previously identified physiological ligands for the Grb2 SH2 domain suggests that for these ligands regulation of the specificity of interaction may be an important factor for in vivo ligand selection.

Exploiting affinity propagation for energy-efficient information discovery in sensor networks

Wireless sensor networks (WSN) are attractive for information gathering in large-scale data rich environments. Emerging WSN applications require dissemination of information to interested clients within the network requiring support for differing traffic patterns. Further, in-network query processing capabilities are required for autonomic information discovery. In this paper, we formulate the information discovery problem as a load-balancing problem, with the combined aim being to maximize network lifetime and minimize query processing delay. We propose novel methods for data dissemination, information discovery and data aggregation that are designed to provide significant QoS benefits. We make use of affinity propagation to group "similar" sensors and have developed efficient mechanisms that can resolve both ALL-type and ANY-type queries in-network with improved energy-efficiency and query resolution time. Simulation results prove the proposed method(s) of information discovery offer significant QoS benefits for ALL-type and ANY-type queries in comparison to previous approaches.

Ni2+-based immobilized metal ion affinity chromatography of lactose operon repressor protein from Escherichia coli

A two-step chromatographic sequence is described for the purification of native lactose operon repressor protein from Escherichia coli cells. The first step involves Ni2+-based immobilized metal ion affinity chromatography of the soluble cytoplasmic extract. This method provides superior speed, resolution and yield than the established phosphocellulose cation-exchange chromatographic procedure. Anion-exchange chromatography is used for further purification to >95% purity. The identity and purity of the lactose repressor protein were demonstrated using sodium dodecylsulphate polyacrylamide electrophoresis, crystallization, tryptic finger-printing mass spectrometry, and inducer binding assays. The purified lac repressor exhibited inducer sensitivity for operator DNA binding and undergoes a conformational change upon inducer binding. By all these extensive biochemical criteria, the purified protein behaves exactly as that described for the Escherichia coli lactose operon repressor.

Apparent in vivo Δ-6 desaturase activity, efficiency, and affinity are affected by total dietary C18 PUFA in the freshwater fish Murray cod

Dietary fatty acids are known to modulate fatty acid metabolism in fish. However, the innate capability of fish to bioconvert short chain fatty acids to health promoting long chain fatty acids (LCPUFA) is insufficient to compensate for a reduced dietary intake. While many studies have focused on the dietary regulation of the fatty acid bioconversion pathways, there is little known regarding the effects of the dietary levels of C18 polyunsaturated fatty acids (PUFA) on fatty acid metabolism. Here, we show a greater degree of apparent enzyme activity (Δ-6 desaturase) in fish fed a diet with higher amounts of dietary C18 PUFA. In particular, fish receiving high amounts of dietary C18 PUFA had a greater amount of Δ-6 desaturase activity acting on 18:3n-3 than 18:2n-6. However, with the gradual reduction of dietary C18 PUFA there was a shift in substrate preference of Δ-6 desaturase from 18:3n-3 to 18:2n-6. This information will provide valuable insight for the implementation of low fish oil diets, which permit the maintenance of n-3 LCPUFA levels in farmed Murray cod.

Brand affinity and consumer repurchase behaviour : an empirical analysis

This research established a construct termed brand affinity, comprising the measures of anticipation of usage, social attraction, commitment, loyalty, and trust. In the context of a highly uncertain, intermittently available service, it was found that brand affinity had greater influence over consumers' repurchase intentions than the typical antecedents of overall and attribute level satisfaction.

Unification of the copper(I) binding affinities of the metallo-chaperones Atx1, Atox1, and related proteins : Detection probes and affinity standards

Literature estimates of metal-protein affinities are widely scattered for many systems, as highlighted by the class of metallo-chaperone proteins, which includes human Atox1. The discrepancies may be attributed to unreliable detection probes and/or inconsistent affinity standards. In this study, application of the four CuI ligand probes bicinchoninate, bathocuproine disulfonate, dithiothreitol (Dtt), and glutathione (GSH) is reviewed, and their CuI affinities are re-estimated and unified. Excess bicinchoninate or bathocuproine disulfonate reacts with CuI to yield distinct 1:2 chromatophoric complexes [CuIL2] 3- with formation constants β2 = 1017.2 and 1019.8 M-2, respectively. These constants do not depend on proton concentration for pH ≥7.0. Consequently, they are a pair of complementary and stable probes capable of detecting free Cu+ concentrations from 10-12 to 10-19 M. Dtt binds CuI with KD∼10-15 M at pH 7, but it is air-sensitive, and its CuI affinity varies with pH. The CuI binding properties of Atox1 and related proteins (including the fifth and sixth domains at the N terminus of the Wilson protein ATP7B) were assessed with these probes. The results demonstrate the following: (i) their use permits the stoichiometry of high affinity CuI binding and the individual quantitative affinities (KD values) to be determined reliably via noncompetitive and competitive reactions, respectively; (ii) the scattered literature values are unified by using reliable probes on a unified scale; and (iii) Atox1-type proteins bind CuI with sub-femtomolar affinities, consistent with tight control of labile Cu+ concentrations in living cells.

Overcoming interfacial affinity issues in natural fiber reinforced polylactide biocomposites by surface adsorption of amphiphilic block copolymers

This work demonstrates that the interfacial properties in a natural fiber reinforced polylactide biocomposite can be tailored through surface adsorption of amphiphilic and biodegradable poly (ethylene glycol)-b-poly-(L-lactide) (PEG-PLLA) block copolymers. The deposition from solvent solution of PEG-PLLA copolymers onto the fibrous substrate induced distinct mechanisms of molecular organization at the cellulosic interface, which are correlated to the hydrophobic/hydrophilic ratios and the type of solvent used. The findings of the study evidenced that the performance of the corresponding biocomposites with polylactide were effectively enhanced by using these copolymers as interfacial coupling agents. During the fabrication stage, diffusion of the polylactide in the melt induced a change in the environment surrounding block copolymers which became hydrophobic. It is proposed that molecular reorganization of the block copolymers at the interface occurred, which favored the interactions with both the hydrophilic fibers and hydrophobic polylactide matrix. The strong interactions such as intra- and intermolecular hydrogen bonds formed across the fiber−matrix interface can be accounted for the enhancement in properties displayed by the biocomposites. Although the results reported here are confined, this concept is unique as it shows that by tuning the amphiphilicity and the type of building blocks, it is possible to control the surface properties of the substrate by self-assembly and disassembly of the amphiphiles for functional materials.

A framework for identifying affinity classes of inorganic materials binding peptide sequences

With the rapid development of bionanotechnology, there has been a growing interest recently in identifying the affinity classes of the inorganic materials binding peptide sequences. However, there are some distinct characteristics of inorganic materials binding sequence data that limit the performance of many widely-used classification methods. In this paper, we propose a novel framework to predict the affinity classes of peptide sequences with respect to an associated inorganic material. We first generate a large set of simulated peptide sequences based on our new amino acid transition matrix, and then the probability of test sequences belonging to a specific affinity class is calculated through solving an objective function. In addition, the objective function is solved through iterative propagation of probability estimates among sequences and sequence clusters. Experimental results on a real inorganic material binding sequence dataset show that the proposed framework is highly effective on identifying the affinity classes of inorganic material binding sequences.

Identifying affinity classes of inorganic materials binding sequences via a graph-based model

Rapid advances in bionanotechnology have recently generated growing interest in identifying peptides that bind to inorganic materials and classifying them based on their inorganic material affinities. However, there are some distinct characteristics of inorganic materials binding sequence data that limit the performance of many widely-used classification methods when applied to this problem. In this paper, we propose a novel framework to predict the affinity classes of peptide sequences with respect to an associated inorganic material. We first generate a large set of simulated peptide sequences based on an amino acid transition matrix tailored for the specific inorganic material. Then the probability of test sequences belonging to a specific affinity class is calculated by minimizing an objective function. In addition, the objective function is minimized through iterative propagation of probability estimates among sequences and sequence clusters. Results of computational experiments on two real inorganic material binding sequence data sets show that the proposed framework is highly effective for identifying the affinity classes of inorganic material binding sequences. Moreover, the experiments on the structural classification of proteins (SCOP) data set shows that the proposed framework is general and can be applied to traditional protein sequences.

Identifying inorganic material affinity classes for peptide sequences based on context learning

There is a growing interest in identifying inorganic material affinity classes for peptide sequences due to the development of bionanotechnology and its wide applications. In particular, a selective model capable of learning cross-material affinity patterns can help us design peptide sequences with desired binding selectivity for one inorganic material over another. However, as a newly emerging topic, there are several distinct challenges of it that limit the performance of many existing peptide sequence classification algorithms. In this paper, we propose a novel framework to identify affinity classes for peptide sequences across inorganic materials. After enlarging our dataset by simulating peptide sequences, we use a context learning based method to obtain the vector representation of each amino acid and each peptide sequence. By analyzing the structure and affinity class of each peptide sequence, we are able to capture the semantics of amino acids and peptide sequences in a vector space. At the last step we train our classifier based on these vector features and the heuristic rules. The construction of our models gives us the potential to overcome the challenges of this task and the empirical results show the effectiveness of our models.

Truncation and mutation of a transferrin receptor aptamer enhance binding affinity

Aptamers are proving their utility in a number of applications. However, to be easily functionalized, their structure needs to be simplified. Therefore, we sought to truncate a 50-nucleotide aptamer specific to the transferrin receptor to its smallest functional unit using rational engineering of the predicted two-dimensional structure of the longer parent sequence. In addition, mutations were introduced into the binding loop to determine their effect on the selectivity of the aptamers. These base mutations enhanced the binding affinity of the aptamer, while retaining its specificity. The equilibrium dissociation constant (Kd) was reduced sixfold following the substitution of all four bases in the binding region. In addition, these aptamers were efficiently internalized into transferrin receptor-positive cells in a similar manner to the transferrin receptor antibody and demonstrated colocalization with this antibody. This study has shown that the smallest functional unit of this aptamer was 14 nucleotides. This small size will be advantageous for future applications, such as drug delivery or functionalization of other therapeutic modalities.