Comparative mapping of host–pathogen protein–protein interactions

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Advances in protein–amino acid nutrition of poultry

The ideal protein concept has allowed progress in defining requirements as well as the limiting order of amino acids in corn, soybean meal, and a corn–soybean meal mixture for growth of young chicks. Recent evidence suggests that glycine (or serine) is a key limiting amino acid in reduced protein [23% crude protein (CP) reduced to 16% CP] corn–soybean meal diets for broiler chicks. Research with sulfur amino acids has revealed that small excesses of cysteine are growth depressing in chicks fed methionine-deficient diets. Moreover, high ratios of cysteine:methionine impair utilization of the hydroxy analog of methionine, but not of methionine itself. A high level of dietary l-cysteine (2.5% or higher) is lethal for young chicks, but a similar level of dl-methionine, l-cystine or N-acetyl-l-cysteine causes no mortality. A supplemental dietary level of 3.0% l-cysteine (7× requirement) causes acute metabolic acidosis that is characterized by a striking increase in plasma sulfate and decrease in plasma bicarbonate. S-Methylmethionine, an analog of S-adenosylmethionine, has been shown to have choline-sparing activity, but it only spares methionine when diets are deficient in choline and(or) betaine. Creatine, or its precursor guanidinoacetic acid, can spare dietary arginine in chicks.     

Site-specific ¹⁹F NMR chemical shift and side chain relaxation analysis of a membrane protein labeled with an unnatural amino acid

Site‐specific ¹⁹F chemical shift and side chain relaxation analysis can be applied on large size proteins. Here, one‐dimensional ¹⁹F spectra and T₁, T₂ relaxation data were acquired on a SH3 domain in aqueous buffer containing 60% glycerol, and a nine‐transmembrane helices membrane protein diacyl‐glycerol kinase (DAGK) in dodecyl phosphochoine (DPC) micelles. The high quality of the data indicates that this method can be applied to site‐specifically analyze side chain internal mobility of membrane proteins or large size proteins.     

Accuracy and precision of protein–ligand interaction kinetics determined from chemical shift titrations

NMR-monitored chemical shift titrations for the study of weak protein?Cligand interactions represent a rich source of information regarding thermodynamic parameters such as dissociation constants (K _D ) in the micro- to millimolar range, populations for the free and ligand-bound states, and the kinetics of interconversion between states, which are typically within the fast exchange regime on the NMR timescale. We recently developed two chemical shift titration methods wherein co-variation of the total protein and ligand concentrations gives increased precision for the K _D value of a 1:1 protein?Cligand interaction (Markin and Spyracopoulos in J Biomol NMR 53: 125?C138, 2012). In this study, we demonstrate that classical line shape analysis applied to a single set of ¹H?C¹⁵N 2D HSQC NMR spectra acquired using precise protein?Cligand chemical shift titration methods we developed, produces accurate and precise kinetic parameters such as the off-rate (k _off ). For experimentally determined kinetics in the fast exchange regime on the NMR timescale, k _off ?~?3,000?s^?1 in this work, the accuracy of classical line shape analysis was determined to be better than 5?% by conducting quantum mechanical NMR simulations of the chemical shift titration methods with the magnetic resonance toolkit GAMMA. Using Monte Carlo simulations, the experimental precision for k _off from line shape analysis of NMR spectra was determined to be 13?%, in agreement with the theoretical precision of 12?% from line shape analysis of the GAMMA simulations in the presence of noise and protein concentration errors. In addition, GAMMA simulations were employed to demonstrate that line shape analysis has the potential to provide reasonably accurate and precise k _off values over a wide range, from 100 to 15,000?s^?1. The validity of line shape analysis for k _off values approaching intermediate exchange (~100?s^?1), may be facilitated by more accurate K _D measurements from NMR-monitored chemical shift titrations, for which the dependence of K _D on the chemical shift difference (????) between free and bound states is extrapolated to ?????=?0. The demonstrated accuracy and precision for k _off will be valuable for the interpretation of biological kinetics in weakly interacting protein?Cprotein networks, where a small change in the magnitude of the underlying kinetics of a given pathway may lead to large changes in the associated downstream signaling cascade.     

Recognizing protein–protein interfaces with empirical potentials and reduced amino acid alphabets

Background  

In structural genomics, an important goal is the detection and classification of protein–protein interactions, given the structures of the interacting partners. We have developed empirical energy functions to identify native structures of protein–protein complexes among sets of decoy structures. To understand the role of amino acid diversity, we parameterized a series of functions, using a hierarchy of amino acid alphabets of increasing complexity, with 2, 3, 4, 6, and 20 amino acid groups. Compared to previous work, we used the simplest possible functional form, with residue–residue interactions and a stepwise distance-dependence. We used increased computational ressources, however, constructing 290,000 decoys for 219 protein–protein complexes, with a realistic docking protocol where the protein partners are flexible and interact through a molecular mechanics energy function. The energy parameters were optimized to correctly assign as many native complexes as possible. To resolve the multiple minimum problem in parameter space, over 64000 starting parameter guesses were tried for each energy function. The optimized functions were tested by cross validation on subsets of our native and decoy structures, by blind tests on series of native and decoy structures available on the Web, and on models for 13 complexes submitted to the CAPRI structure prediction experiment.     

Peptides and peptidomimetics as regulators of protein–protein interactions

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Assessment and significance of protein–protein interactions during development of protein biopharmaceuticals

Early development of protein biotherapeutics using recombinant DNA technology involved progress in the areas of cloning, screening, expression and recovery/purification. As the biotechnology industry matured, resulting in marketed products, a greater emphasis was placed on development of formulations and delivery systems requiring a better understanding of the chemical and physical properties of newly developed protein drugs. Biophysical techniques such as analytical ultracentrifugation, dynamic and static light scattering, and circular dichroism were used to study protein–protein interactions during various stages of development of protein therapeutics. These studies included investigation of protein self-association in many of the early development projects including analysis of highly glycosylated proteins expressed in mammalian CHO cell cultures. Assessment of protein–protein interactions during development of an IgG1 monoclonal antibody that binds to IgE were important in understanding the pharmacokinetics and dosing for this important biotherapeutic used to treat severe allergic IgE-mediated asthma. These studies were extended to the investigation of monoclonal antibody–antigen interactions in human serum using the fluorescent detection system of the analytical ultracentrifuge. Analysis by sedimentation velocity analytical ultracentrifugation was also used to investigate competitive binding to monoclonal antibody targets. Recent development of high concentration protein formulations for subcutaneous administration of therapeutics posed challenges, which resulted in the use of dynamic and static light scattering, and preparative analytical ultracentrifugation to understand the self-association and rheological properties of concentrated monoclonal antibody solutions.     

Fractionation profiling: a fast and versatile approach for mapping vesicle proteomes and protein–protein interactions

We developed “fractionation profiling,” a method for rapid proteomic analysis of membrane vesicles and protein particles. The approach combines quantitative proteomics with subcellular fractionation to generate signature protein abundance distribution profiles. Functionally associated groups of proteins are revealed through cluster analysis. To validate the method, we first profiled >3500 proteins from HeLa cells and identified known clathrin-coated vesicle proteins with >90% accuracy. We then profiled >2400 proteins from Drosophila S2 cells, and we report the first comprehensive insect clathrin-coated vesicle proteome. Of importance, the cluster analysis extends to all profiled proteins and thus identifies a diverse range of known and novel cytosolic and membrane-associated protein complexes. We show that it also allows the detailed compositional characterization of complexes, including the delineation of subcomplexes and subunit stoichiometry. Our predictions are presented in an interactive database. Fractionation profiling is a universal method for defining the clathrin-coated vesicle proteome and may be adapted for the analysis of other types of vesicles and particles. In addition, it provides a versatile tool for the rapid generation of large-scale protein interaction maps.     

Systems for the detection and analysis of protein–protein interactions

The analysis of protein–protein interactions is important for developing a better understanding of the functional annotations of proteins that are involved in various biochemical reactions in vivo. The discovery that a protein with an unknown function binds to a protein with a known function could provide a significant clue to the cellular pathway concerning the unknown protein. Therefore, information on protein–protein interactions obtained by the comprehensive analysis of all gene products is available for the construction of interactive networks consisting of individual protein–protein interactions, which, in turn, permit elaborate biological phenomena to be understood. Systems for detecting protein–protein interactions in vitro and in vivo have been developed, and have been modified to compensate for limitations. Using these novel approaches, comprehensive and reliable information on protein–protein interactions can be determined. Systems that permit this to be achieved are described in this review.K. Kuroda, M. Kato and J. Mima contributed equally to this work.     

Synthesis and antibacterial evaluation of amino acid–antibiotic conjugates

Amino acid conjugates of quinolone, metronidazole and sulfadiazine antibiotics were synthesized in good yields using benzotriazole methodology. All the conjugates were screened for their antibacterial activity using methods adapted from the Clinical and Laboratory Standards Institute. Antibiotic conjugates were tested for activity in four medically relevant organisms; Staphylococcus aureus (RN4220), Escherichia coli (DH5α), Pseudomonas aeruginosa (PAO1), and Bacillus subtilis (168). Several antibiotic conjugates show promising results against several of the strains screened.     

Characterization of protein–protein interactions between rice viruses and vector insects

Planthoppers are the most notorious rice pests, because they transmit various rice viruses in a persistent-propagative manner. Protein–protein interactions (PPIs) between virus and vector are crucial for virus transmission by vector insects. However, the number of known PPIs for pairs of rice viruses and planthoppers is restricted by low throughput research methods. In this study, we applied DeNovo, a virus-host sequence-based PPI predictor, to predict potential PPIs at a genome-wide scale between three planthoppers and five rice viruses. PPIs were identified at two different confidence thresholds, referred to as low and high modes. The number of PPIs for the five planthopper-virus pairs ranged from 506 to 1985 in the low mode and from 1254 to 4286 in the high mode. After eliminating the “one-too-many” redundant interacting information, the PPIs with unique planthopper proteins were reduced to 343–724 in the low mode and 758–1671 in the high mode. Homologous analysis showed that 11 sets and 31 sets of homologous planthopper proteins were shared by all planthopper-virus interactions in the two modes, indicating that they are potential conserved vector factors essential for transmission of rice viruses. Ten PPIs between small brown planthopper and rice stripe virus (RSV) were verified using glutathione-S-transferase (GST)/His-pull down or co-immunoprecipitation assay. Five of the ten PPIs were proven positive, and three of the five SBPH proteins were confirmed to interact with RSV. The predicted PPIs provide new clues for further studies of the complicated relationship between rice viruses and their vector insects.     

Analysis of secondary structural and physicochemical changes in protein–protein complexes

Conformation switching in protein–protein complexes is considered important for the molecular recognition process. Overall analysis of 123 protein–protein complexes in a benchmark data-set showed that 6.8% of residues switched over their secondary structure conformation upon complex formation. Amino acid residue-wise preference for conformation change has been analyzed in binding and non-binding site residues separately. In this analysis, residues such as Ser, Leu, Glu, and Lys had higher frequency of secondary structural conformation change. The change of helix to coil and sheet to coil conformation and vice versa has been observed frequently, whereas the conformation change of helix to extended sheet occurred rarely in the studied complexes. Influence of conformation change toward the N and C terminal on either side of the binding site residues has been analyzed. Further, analysis on φ and ψ angle variation, conservation, stability, and solvent accessibility have been performed on binding site residues. Knowledge obtained from the present study could be effectively employed in the protein–protein modeling and docking studies.     

Membrane phosphoinositides and protein–membrane interactions

Proteins with polybasic clusters bind to negatively charged phosphoinositides at the cell membrane. In this review, I have briefly discussed the types of phosphoinositides naturally found on membrane surfaces and how they recruit protein complexes for carrying out the process of signal transduction. A large number of researchers from around the world are now focusing their attention on protein–membrane binding, as these interactions have started to offer us a much better insight into the process of cell signaling. The main areas discussed in this brief review article include the phosphoinositide binding specificities of proteins and the role of their lipid binding in signaling processes downstream of membrane recruitment.     

Quantifying sequence and structural features of protein–RNA interactions

Increasing awareness of the importance of protein–RNA interactions has motivated many approaches to predict residue-level RNA binding sites in proteins based on sequence or structural characteristics. Sequence-based predictors are usually high in sensitivity but low in specificity; conversely structure-based predictors tend to have high specificity, but lower sensitivity. Here we quantified the contribution of both sequence- and structure-based features as indicators of RNA-binding propensity using a machine-learning approach. In order to capture structural information for proteins without a known structure, we used homology modeling to extract the relevant structural features. Several novel and modified features enhanced the accuracy of residue-level RNA-binding propensity beyond what has been reported previously, including by meta-prediction servers. These features include: hidden Markov model-based evolutionary conservation, surface deformations based on the Laplacian norm formalism, and relative solvent accessibility partitioned into backbone and side chain contributions. We constructed a web server called aaRNA that implements the proposed method and demonstrate its use in identifying putative RNA binding sites.     

Increased precision for analysis of protein–ligand dissociation constants determined from chemical shift titrations

NMR is ideally suited for the analysis of protein-protein and protein ligand interactions with dissociation constants ranging from ~2 μM to ~1 mM, and with kinetics in the fast exchange regime on the NMR timescale. For the determination of dissociation constants (K ( D )) of 1:1 protein-protein or protein-ligand interactions using NMR, the protein and ligand concentrations must necessarily be similar in magnitude to the K ( D ), and nonlinear least squares analysis of chemical shift changes as a function of ligand concentration is employed to determine estimates for the parameters K ( D ) and the maximum chemical shift change (Δδ(max)). During a typical NMR titration, the initial protein concentration, [P (0)], is held nearly constant. For this condition, to determine the most accurate parameters for K ( D ) and Δδ(max) from nonlinear least squares analyses requires initial protein concentrations that are ~0.5 × K ( D ), and a maximum concentration for the ligand, or titrant, of ~10 × [P (0)]. From a practical standpoint, these requirements are often difficult to achieve. Using Monte Carlo simulations, we demonstrate that co-variation of the ligand and protein concentrations during a titration leads to an increase in the precision of the fitted K ( D ) and Δδ(max) values when [P (0)] > K ( D ). Importantly, judicious choice of protein and ligand concentrations for a given NMR titration, combined with nonlinear least squares analyses using two independent variables (ligand and protein concentrations) and two parameters (K ( D ) and Δδ(max)) is a straightforward approach to increasing the accuracy of measured dissociation constants for 1:1 protein-ligand interactions.