Purification and characterization of alpha-galactosidase from sunflower seeds

From 100 g sunflower seeds, 1.2 mg purified -galactosidase was obtained with an overall yield of 51%. The -galactosidase acted on both terminal -galactosyl residues and side-chain -galactosyl residues of the galactomanno-oligosaccharides and galactomannans. The cDNA coding for sunflower -galactosidase was cloned and the deduced amino acid sequence revealed that the mature enzyme consisted of 363 amino acid residues with a molecular weight of 40263. Seven cysteine residues were found but no putative N-glycosylation sites were present in the sequence. The deduced amino acid sequences of mature enzyme and -galactosidases from coffee, guar and Mortierella vinacea -galactosidase II showed over 81%, 77%, and 47% homology, respectively. These enzymes are classified into the third group in which the enzyme has no insertion sequence and a broad specificity on galactomanno-oligosaccharides compared to the other groups.     

Evolutionary relationships among photosynthetic bacteria

To understand the evolution of photosynthetic bacteria it is necessary to understand how the main groups within Bacteria have evolved from a common ancestor, a critical issue that has not been resolved in the past. Recent analysis of shared conserved inserts or deletions (indels) in protein sequences has provided a powerful means to resolve this long-standing problem in microbiology. Based on a set of 25 indels in highly conserved and widely distributed proteins, all main groups within bacteria can now be defined in clear molecular terms and their relative branching orders logically deduced. For the 82 presently completed bacterial genomes, the presence or absence of these signatures in various proteins was found to be almost exactly as predicted by the indel model, with only 11 exceptions observed in 1842 observations. The branching order of different bacterial groups as deduced using this approach is as follows: low G+C Gram-positive (Heliobacterium chlorum) ↔ high G+C Gram-positive ↔ Clostridium–Fusobacterium–Thermotoga ↔ Deinococcus–Thermus ↔ green nonsulfur bacteria (Chloroflexus aurantiacus) ↔ Cyanobacteria ↔ Spirochetes ↔ Chlamydia–Cytophaga–Flavobacteria–green sulfur bacteria (Chlorobium tepidum) ↔ Aquifex ↔ Proteobacteria (δ and ∈) ↔ Proteobacteria (α) ↔ Proteobacteria (β) and ↔ Proteobacteria (γ). The Heliobacterium species, which contain an Fe–S type of reaction center (RC 1) and represent the sole photosynthetic phylum from the Gram-positive or monoderm bacteria (i.e., bounded by only a single membrane), is indicated to be the most ancestral of the photosynthetic lineages. Among the Gram-negative or diderm bacteria (containing both inner and outer cell membranes) the green nonsulfur bacteria, which contain a pheophytin-quinone type of reaction center (RC 2), are indicated to have evolved first. The later emerging photosynthetic groups which contain either one or both of these reaction centers could have acquired such genes from the earlier branching lineages by either direct descent or by means of lateral gene transfer. This revised version was published online in August 2006 with corrections to the Cover Date.     

The cDNA Microarray Profiling of Protein Kinases and Phosphatases: Molecular Portrait of Human Prostate Carcinomas

Hybridization with cDNA arrays was used to obtain expression profiles of 263 protein-tyrosine kinase (PTK), protein-tyrosine phosphatase (PTP), dual-specific phosphatase (DuSP), and other genes for the normal prostate tissue, primary prostate carcinomas (PC) of 84 patients, 7 xenografts, and 5 carcinoma cell lines. Analysis of 96 profiles revealed eight clusters of genes coexpressed in PC (coefficient of correlation r > 0.7). According to the known functions of their genes, the clusters were designated as proliferating-cell (CDC42, TOP2A, FGFR3, MYC, etc.), neoangiogenesis and blood-cell (LCK, VAV1, KDR, VEGF, MMP9, SYK, PTPRS, and FLT4), invasion-1 and invasion-2 (ADAM17, TRPM2, DUSP6, VIM, CAV1, CAV2, JAK1, PTPNS1, FYN, and PDGFB), HER2, and PSA/PSM/HER3. Basing on expression profiles of 66 genes, a molecular classification of PC was generated and allowed discrimination between PC and cell lines or xenografts at 98.9% probability. The results suggested that, along with PSA, PSM (FOLH1), callicreine-2, and -2-macroglobulin, cell signaling genes EGFR, HER2, HER3, TOP2, KRT8, KRT18, VEGF, CD44, VIM, CAV1, and CAV2 may serve as diagnostic and prognostic markers in PC. The HER2, VEGF, and CD44 genes and the MMP and ADAM families were assumed to be promising targets for inhibitors of PC cell proliferation and metastasis.     

Relationship between protein structures and disulfide-bonding patterns

We found that that disulfide-bonding patterns can be used to discriminate structure similarity. Our method, based on the hierarchical clustering scheme, is applicable to proteins with two or more disulfide bonds and is able to detect the structural similarities of proteins of low sequence identities (<25%). Our results show the surprisingly close relationship between disulfide-bonding patterns and proteins structures. Our findings should be useful in protein structure modeling.     

Hydrophobic moments of tertiary protein structures

The helical hydrophobic moment is a measure of the amphiphilicity of a segment of protein secondary structure. Such measure yields information of potential relevance for issues relating to cell surface binding and secondary structure function. The present article describes a global analog of the helical hydrophobic moment. The global moment provides a concise measure of the degree and direction of the amphiphilicity or hydrophobic imbalance across the entire protein tertiary structure. Therefore, this measure is a succinct representation of the spatial organization of residue hydrophobicity for each protein. With this measure, a simple comparison of the hydrophobic imbalance or segregation of different protein structures can be made. For example, two structures having the same fold and close in root-mean-square deviation may exhibit very different overall hydrophobic organization. Such difference is classified simply by the global moment. Furthermore, the direction of the global moment may point to regions of functional interest. Certain formal issues in the development of such moment are described, and a number of applications to particular protein structures are discussed.     

A general clustering approach with application to the Miyazawa-Jernigan potentials for amino acids

In this article, we address the problem of classification of amino acids. Starting from the Miyazawa-Jernigan matrix obtained from the relative positions of amino acids in the crystal structure of globular proteins, we develop a fully unsupervised method of classification for the amino acids. The method is based in the subdominant ultrametric associated to the distance induced by the Miyazawa-Jernigan matrix and the maximum likelihood principle to determine the cluster structure. We obtain a classification consistent with the five groups used in the literature, although with some peculiarities. We also show the stability of our results against changes of the method used to classify the amino acids. Proteins 2004.     

Evaluation of protein fold comparison servers

When a new protein structure has been determined, comparison with the database of known structures enables classification of its fold as new or belonging to a known class of proteins. This in turn may provide clues about the function of the protein. A large number of fold comparison programs have been developed, but they have never been subjected to a comprehensive and critical comparative analysis. Here we describe an evaluation of 11 publicly available, Web-based servers for automatic fold comparison. Both their functionality (e.g., user interface, presentation, and annotation of results) and their performance (i.e., how well established structural similarities are recognized) were assessed. The servers were subjected to a battery of performance tests covering a broad spectrum of folds as well as special cases, such as multidomain proteins, Calpha-only models, new folds, and NMR-based models. The CATH structural classification system was used as a reference. These tests revealed the strong and weak sides of each server. On the whole, CE, DALI, MATRAS, and VAST showed the best performance, but none of the servers achieved a 100% success rate. Where no structurally similar proteins are found by any individual server, it is recommended to try one or two other servers before any conclusions concerning the novelty of a fold are put on paper.     

A simple topological representation of protein structure: implications for new, fast, and robust structural classification

A topological representation of proteins is developed that makes use of two metrics: the Euclidean metric for identifying natural nearest neighboring residues via the Delaunay tessellation in Cartesian space and the distance between residues in sequence space. Using this representation, we introduce a quantitative and computationally inexpensive method for the comparison of protein structural topology. The method ultimately results in a numerical score quantifying the distance between proteins in a heuristically defined topological space. The properties of this scoring scheme are investigated and correlated with the standard Calpha distance root-mean-square deviation measure of protein similarity calculated by rigid body structural alignment. The topological comparison method is shown to have a characteristic dependence on protein conformational differences and secondary structure. This distinctive behavior is also observed in the comparison of proteins within families of structural relatives. The ability of the comparison method to successfully classify proteins into classes, superfamilies, folds, and families that are consistent with standard classification methods, both automated and human-driven, is demonstrated. Furthermore, it is shown that the scoring method allows for a fine-grained classification on the family, protein, and species level that agrees very well with currently established phylogenetic hierarchies. This fine classification is achieved without requiring visual inspection of proteins, sequence analysis, or the use of structural superimposition methods. Implications of the method for a fast, automated, topological hierarchical classification of proteins are discussed.     

Folding of beta/alpha-unit scrambled forms of S. cerevisiae triosephosphate isomerase: Evidence for autonomy of substructure formation and plasticity of hydrophobic and hydrogen bonding interactions in core of (beta/alpha)8-barrel

We show that residues at the interfaces of protein-protein complexes have higher side-chain energy than other surface residues. Eight different sets of protein complexes were analyzed. For each protein pair, the complex structure was used to identify the interface residues in the unbound monomer structures. Side-chain energy was calculated for each surface residue in the unbound monomer using our previously developed scoring function.1 The mean energy was calculated for the interface residues and the other surface residues. In 15 of the 16 monomers, the mean energy of the interface residues was higher than that of other surface residues. By decomposing the scoring function, we found that the energy term of the buried surface area of non-hydrogen-bonded hydrophilic atoms is the most important factor contributing to the high energy of the interface regions. In spite of lacking hydrophilic residues, the interface regions were found to be rich in buried non-hydrogen-bonded hydrophilic atoms. Although the calculation results could be affected by the inaccuracy of the scoring function, patch analysis of side-chain energy on the surface of an isolated protein may be helpful in identifying the possible protein-protein interface. A patch was defined as 20 residues surrounding the central residue on the protein surface, and patch energy was calculated as the mean value of the side-chain energy of all residues in the patch. In 12 of the studied monomers, the patch with the highest energy overlaps with the observed interface. The results are more remarkable when only three residues with the highest energy in a patch are averaged to derive the patch energy. All three highest-energy residues of the top energy patch belong to interfacial residues in four of the eight small protomers. We also found that the residue with the highest energy score on the surface of a small protomer is very possibly the key interaction residue.     

Enzyme family classification by support vector machines

One approach for facilitating protein function prediction is to classify proteins into functional families. Recent studies on the classification of G-protein coupled receptors and other proteins suggest that a statistical learning method, Support vector machines (SVM), may be potentially useful for protein classification into functional families. In this work, SVM is applied and tested on the classification of enzymes into functional families defined by the Enzyme Nomenclature Committee of IUBMB. SVM classification system for each family is trained from representative enzymes of that family and seed proteins of Pfam curated protein families. The classification accuracy for enzymes from 46 families and for non-enzymes is in the range of 50.0% to 95.7% and 79.0% to 100% respectively. The corresponding Matthews correlation coefficient is in the range of 54.1% to 96.1%. Moreover, 80.3% of the 8,291 correctly classified enzymes are uniquely classified into a specific enzyme family by using a scoring function, indicating that SVM may have certain level of unique prediction capability. Testing results also suggest that SVM in some cases is capable of classification of distantly related enzymes and homologous enzymes of different functions. Effort is being made to use a more comprehensive set of enzymes as training sets and to incorporate multi-class SVM classification systems to further enhance the unique prediction accuracy. Our results suggest the potential of SVM for enzyme family classification and for facilitating protein function prediction. Our software is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.