Whey protein mouth drying influenced by thermal denaturation

Whey proteins are becoming an increasingly popular functional food ingredient. There are, however, sensory properties associated with whey protein beverages that may hinder the consumption of quantities sufficient to gain the desired nutritional benefits. One such property is mouth drying. The influence of protein structure on the mouthfeel properties of milk proteins has been previously reported. This paper investigates the effect of thermal denaturation of whey proteins on physicochemical properties (viscosity, particle size, zeta-potential, pH), and relates this to the observed sensory properties measured by qualitative descriptive analysis and sequential profiling. Mouthcoating, drying and chalky attributes built up over repeated consumption, with higher intensities for samples subjected to longer heating times (p < 0.05). Viscosity, pH, and zeta-potential were found to be similar for all samples, however particle size increased with longer heating times. As the pH of all samples was close to neutral, this implies that neither the precipitation of whey proteins at low pH, nor their acidity, as reported in previous literature, can be the drying mechanisms in this case. The increase in mouth drying with increased heating time suggests that protein denaturation is a contributing factor and a possible mucoadhesive mechanism is discussed.

In silico identification and characterization of protein-ligand binding sites

Protein–ligand binding site prediction methods aim to predict, from amino acid sequence, protein–ligand interactions, putative ligands, and ligand binding site residues using either sequence information, structural information, or a combination of both. In silico characterization of protein–ligand interactions has become extremely important to help determine a protein’s functionality, as in vivo-based functional elucidation is unable to keep pace with the current growth of sequence databases. Additionally, in vitro biochemical functional elucidation is time-consuming, costly, and may not be feasible for large-scale analysis, such as drug discovery. Thus, in silico prediction of protein–ligand interactions must be utilized to aid in functional elucidation. Here, we briefly discuss protein function prediction, prediction of protein–ligand interactions, the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated EvaluatiOn (CAMEO) competitions, along with their role in shaping the field. We also discuss, in detail, our cutting-edge web-server method, FunFOLD for the structurally informed prediction of protein–ligand interactions. Furthermore, we provide a step-by-step guide on using the FunFOLD web server and FunFOLD3 downloadable application, along with some real world examples, where the FunFOLD methods have been used to aid functional elucidation.

Aligning sequences to structures

Most newly sequenced proteins are likely to adopt a similar structure to one which has already been experimentally determined. For this reason, the most successful approaches to protein structure prediction have been template-based methods. Such prediction methods attempt to identify and model the folds of unknown structures by aligning the target sequences to a set of representative template structures within a fold library. In this chapter, I discuss the development of template-based approaches to fold prediction, from the traditional techniques to the recent state-of-the-art methods. I also discuss the recent development of structural annotation databases, which contain models built by aligning the sequences from entire proteomes against known structures. Finally, I run through a practical step-by-step guide for aligning target sequences to known structures and contemplate the future direction of template-based structure prediction.

Amino acid pairing preferences in parallel beta-sheets in proteins

Statistical approaches have been applied to examine amino acid pairing preferences within parallel beta-sheets. The main chain hydrogen bonding pattern in parallel beta-sheets means that, for each residue pair, only one of the residues is involved in main chain hydrogen bonding with the strand containing the partner residue. We call this the hydrogen bonded (HB) residue and the partner residue the non-hydrogen bonded (nHB) residue, and differentiate between the favorability of a pair and that of its reverse pair, e.g. Asn(HB)-Thr(nHB)versus Thr(HB)-Asn(nHB). Significantly (p < or = 0.000001) favoured pairings were rationalised using stereochemical arguments. For instance, Asn(HB)-Thr(nHB) and Arg(HB)-Thr(nHB) were favoured pairs, where the residues adopted favoured chi1 rotamer positions that allowed side-chain interactions to occur. In contrast, Thr(HB)-Asn(nHB) and Thr(HB)-Arg(nHB) were not significantly favoured, and could only form side-chain interactions if the residues involved adopted less favourable chi1 conformations. The favourability of hydrophobic pairs e.g. Ile(HB)-Ile(nHB), Val(HB)-Val(nHB) and Leu(HB)-Ile(nHB) was explained by the residues adopting their most preferred chi1 and chi2 conformations, which enabled them to form nested arrangements. Cysteine-cysteine pairs are significantly favoured, although these do not form intrasheet disulphide bridges. Interactions between positively and negatively charged residues were asymmetrically preferred: those with the negatively charged residue at the HB position were more favoured. This trend was accounted for by the presence of general electrostatic interactions, which, based on analysis of distances between charged atoms, were likely to be stronger when the negatively charged residue is the HB partner. The Arg(HB)-Asp(nHB) interaction was an exception to this trend and its favorability was rationalised by the formation of specific side-chain interactions. This research provides rules that could be applied to protein structure prediction, comparative modelling and protein engineering and design. The methods used to analyse the pairing preferences are automated and detailed results are available (http://www.rubic.rdg.ac.uk/betapairprefsparallel/).

Amino acid pairing preferences in parallel beta-sheets in proteins

Statistical approaches have been applied to examine amino acid pairing preferences within parallel beta-sheets. The main chain hydrogen bonding pattern in parallel beta-sheets means that, for each residue pair, only one of the residues is involved in main chain hydrogen bonding with the strand containing the partner residue. We call this the hydrogen bonded (HB) residue and the partner residue the non-hydrogen bonded (nHB) residue, and differentiate between the favourability of a pair and that of its reverse pair, e.g. Asn(HB)-Thr(nHB) versus Thr(HB)-Asn(nHB). Significantly (p <= 0.000001) favoured pairings were rationalised using stereochemical arguments. For instance, Asn(HB)-Thr(nHB) and Arg(HB)-Thr(nHB) were favoured pairs, where the residues adopted favoured chi(1) rotamer positions that allowed side-chain interactions to occur. In contrast, Thr(HB)-Asn(nHB) and Thr(HB)-Arg(nHB) were not significantly favoured, and could only form side-chain interactions if the residues involved adopted less favourable chi(1) conformations. The favourability of hydrophobic pairs e.g. Ile(HB)-Ile(nHB), Val(HB)-Val(nHB) and Leu(HB)-Ile(nHB) was explained by the residues adopting their most preferred chi(1) and chi(2) conformations, which enabled them to form nested arrangements. Cysteine-cysteine pairs are significantly favoured, although these do not form intrasheet disulphide bridges. Interactions between positively and negatively charged residues were asymmetrically preferred: those with the negatively charged residue at the HB position were more favoured. This trend was accounted for by the presence of general electrostatic interactions, which, based on analysis of distances between charged atoms, were likely to be stronger when the negatively charged residue is the HB partner. The Arg(HB)-Asp(nHB) interaction was an exception to this trend and its favourability was rationalised by the formation of specific side-chain interactions. This research provides rules that could be applied to protein structure prediction, comparative modelling and protein engineering and design. The methods used to analyse the pairing preferences are automated and detailed results are available (http:// www.rubic.rdg.ac.uk/betapairprefsparallel/). (c) 2005 Elsevier Ltd. All rights reserved.

The adrenal secretory serine protease AsP is a short secretory isoform of the transmembrane airway trypsin-like protease

To further elucidate the role of proteases capable of cleaving N-terminal proopiomelanocortin (N-POMC)-derived peptides, we have cloned two cDNAs encoding isoforms of the airway trypsin-like protease (AT) from mouse (MAT) and rat ( RAT), respectively. The open reading frames comprise 417 amino acids (aa) and 279 aa. The mouse AT gene was located at chromosome 5E1 and contains 10 exons. The longer isoform, which we designated MAT1 and RAT1, has a simple type II transmembrane protein structure, consisting of a short cytoplasmic domain, a transmembrane domain, a SEA (63-kDa sea urchin sperm protein, enteropeptidase, agrin) module, and a serine protease domain. The human homolog of MAT1 and RAT1 is the human AT ( HAT). The shorter isoform, designated MAT2 and RAT2, which contains an alternative N terminus, was formerly described in the rat as adrenal secretory serine protease (AsP) and has been shown to be involved in the processing of N-POMC-derived peptides. In contrast to the long isoform, neither MAT2 and RAT2 ( AsP) contain a transmembrane domain nor a SEA domain but an N-terminal signal peptide to direct the enzyme to the secretory pathway. The C terminus, covering the catalytic triad, is identical in both isoforms. Immunohistochemically, MAT/RAT was predominantly expressed in tissues of the upper gastrointestinal and the respiratory tract - but also in the adrenal gland. Moreover, isoform-specific RT-PCR and quantitative PCR analysis revealed a complex expression pattern of the two isoforms with differences between mice and rats. These findings indicate a multifunctional role of these proteases beyond adrenal proliferation.

Fermentation of heated gluten systems by gut microflora

The Maillard reaction causes changes to protein structure and occurs in foods mainly during thermal treatment. Melanoidins, the final products of the Maillard reaction, may enter the gastrointestinal tract, which is populated by different species of bacteria. In this study, melanoidins were prepared from gluten and glucose. Their effect on the growth of faecal bacteria was determined in culture with genotype and phenotype probes to identify the different species involved. Analysis of peptic and tryptic digests showed that low molecular mass products are formed from the degradation of melanoidins. Results showed a change in the growth of bacteria. This in vitro study demonstrated that melanoidins, prepared from gluten and glucose, affect the growth of the gut microflora.

Proteomic analysis of arginine adducts on glyoxal-modified ribonuclease

Accumulation of advanced glycation end-products (AGEs) on proteins is associated with the development of diabetic complications. Although the overall extent of modification of protein by AGEs is limited, localization of these modifications at a few critical sites might have a significant effect on protein structure and function. In the present study, we describe the sites of modification of RNase by glyoxal under physiological conditions. Arg(39) and Arg(85), which are closest to the active site of the enzyme, were identified as the primary sites of formation of the glyoxal-derived dihydroxyimidazolidine and hydroimidazolone adducts. Lower amounts of modification were detected at Arg(10), while Arg(33) appeared to be unmodified. We conclude that dihydroxyimidazolidine adducts are the primary products of modification of protein by glyoxal, that Arg(39) and Arg(85) are the primary sites of modification of RNase by glyoxal, and that modification of arginine residues during Maillard reactions of proteins is a highly selective process.

The use of proteomics to identify novel therapeutic targets for the treatment of disease

The completion of the Human Genome Project has revealed a multitude of potential avenues for the identification of therapeutic targets. Extensive sequence information enables the identification of novel genes but does not facilitate a thorough understanding of how changes in gene expression control the molecular mechanisms underlying the development and regulation of a cell or the progression of disease. Proteomics encompasses the study of proteins expressed by a population of cells, and evaluates changes in protein expression, post-translational modifications, protein interactions, protein structure and splice variants, all of which are imperative for a complete understanding of protein function within the cell. From the outset, proteomics has been used to compare the protein profiles of cells in healthy and diseased states and as such can be used to identify proteins associated with disease development and progression. These candidate proteins might provide novel targets for new therapeutic agents or aid the development of assays for disease biomarkers. This review provides an overview of the current proteomic techniques available and focuses on their application in the search for novel therapeutic targets for the treatment of disease.

Prediction of novel and analogous folds using fragment assembly and fold recognition

A number of new and newly improved methods for predicting protein structure developed by the Jones–University College London group were used to make predictions for the CASP6 experiment. Structures were predicted with a combination of fold recognition methods (mGenTHREADER, nFOLD, and THREADER) and a substantially enhanced version of FRAGFOLD, our fragment assembly method. Attempts at automatic domain parsing were made using DomPred and DomSSEA, which are based on a secondary structure parsing algorithm and additionally for DomPred, a simple local sequence alignment scoring function. Disorder prediction was carried out using a new SVM-based version of DISOPRED. Attempts were also made at domain docking and “microdomain” folding in order to build complete chain models for some targets.

Predicting metal-binding site residues in low-resolution structural models

The accurate prediction of the biochemical function of a protein is becoming increasingly important, given the unprecedented growth of both structural and sequence databanks. Consequently, computational methods are required to analyse such data in an automated manner to ensure genomes are annotated accurately. Protein structure prediction methods, for example, are capable of generating approximate structural models on a genome-wide scale. However, the detection of functionally important regions in such crude models, as well as structural genomics targets, remains an extremely important problem. The method described in the current study, MetSite, represents a fully automatic approach for the detection of metal-binding residue clusters applicable to protein models of moderate quality. The method involves using sequence profile information in combination with approximate structural data. Several neural network classifiers are shown to be able to distinguish metal sites from non-sites with a mean accuracy of 94.5%. The method was demonstrated to identify metal-binding sites correctly in LiveBench targets where no obvious metal-binding sequence motifs were detectable using InterPro. Accurate detection of metal sites was shown to be feasible for low-resolution predicted structures generated using mGenTHREADER where no side-chain information was available. High-scoring predictions were observed for a recently solved hypothetical protein from Haemophilus influenzae, indicating a putative metal-binding site.

Prediction and functional analysis of native disorder in proteins from the Three Kingdoms of Life

An automatic method for recognizing natively disordered regions from amino acid sequence is described and benchmarked against predictors that were assessed at the latest critical assessment of techniques for protein structure prediction (CASP) experiment. The method attains a Wilcoxon score of 90.0, which represents a statistically significant improvement on the methods evaluated on the same targets at CASP. The classifier, DISOPRED2, was used to estimate the frequency of native disorder in several representative genomes from the three kingdoms of life. Putative, long (>30 residue) disordered segments are found to occur in 2.0% of archaean, 4.2% of eubacterial and 33.0% of eukaryotic proteins. The function of proteins with long predicted regions of disorder was investigated using the gene ontology annotations supplied with the Saccharomyces genome database. The analysis of the yeast proteome suggests that proteins containing disorder are often located in the cell nucleus and are involved in the regulation of transcription and cell signalling. The results also indicate that native disorder is associated with the molecular functions of kinase activity and nucleic acid binding.

Effect of high-hydrostatic pressure and pH treatments on the emulsification properties of gum arabic

This study investigated the emulsification properties of the native gums and those treated at high pressure (800 MPa) both at their “natural” pH (4.49 and 4.58, respectively) and under “acidic and basic” pH (2.8 and 8.0) conditions. The emulsification behaviour of KLTA gum was found to be superior to that of the GCA gum. High pressure and pH treatment changed the emulsification properties of both gums. The acidic amino acids in gum arabic were shown to play an important role in their emulsification behaviour, and mechanisms of emulsification for the two gums were suggested to be different. The highly “branched” nature of the carbohydrate in GCA gum was also thought to be responsible for the “spreading” of droplet size distributions observed. Coomassie brilliant blue binding was used to indicate conformational changes in protein structure and Ellman’s assay was used to estimate any changes in levels of free thiols present.

Proteins and their interacting partners: an introduction to protein–ligand binding site prediction methods

Elucidating the biological and biochemical roles of proteins, and subsequently determining their interacting partners, can be difficult and time consuming using in vitro and/or in vivo methods, and consequently the majority of newly sequenced proteins will have unknown structures and functions. However, in silico methods for predicting protein–ligand binding sites and protein biochemical functions offer an alternative practical solution. The characterisation of protein–ligand binding sites is essential for investigating new functional roles, which can impact the major biological research spheres of health, food, and energy security. In this review we discuss the role in silico methods play in 3D modelling of protein–ligand binding sites, along with their role in predicting biochemical functionality. In addition, we describe in detail some of the key alternative in silico prediction approaches that are available, as well as discussing the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated Model EvaluatiOn (CAMEO) projects, and their impact on developments in the field. Furthermore, we discuss the importance of protein function prediction methods for tackling 21st century problems.