Comparison of Free Total Amino Acid Compositions and Their Functional Classifications in 13 Wild Edible Mushrooms.

Thirteen popular wild edible mushroom species in Yunnan Province, Boletus bicolor, Boletus speciosus, Boletus sinicus, Boletus craspedius, Boletus griseus, Boletus ornatipes, Xerocomus, Suillus placidus, Boletinus pinetorus, Tricholoma terreum, Tricholomopsis lividipileata, Termitomyces microcarpus, and Amanita hemibapha, were analyzed for their free amino acid compositions by online pre-column derivazation reversed phase high-performance liquid chromatography (RP-HPLC) analysis. Twenty free amino acids, aspartic acid, glutamic acid, serine, glycine, alanine, praline, cysteine, valine, methionine, phenylalanine, isoleucine, leucine, lysine, histidine, threonine, asparagines, glutamine, arginine, tyrosine, and tryptophan, were determined. The total free amino acid (TAA) contents ranged from 1462.6 mg/100 g in B. craspedius to 13,106.2 mg/100 g in T. microcarpus. The different species showed distinct free amino acid profiles. The ratio of total essential amino acids (EAA) to TAA was 0.13-0.41. All of the analyzed species showed high contents of hydrophobic amino acids, at 33%-54% of TAA. Alanine, cysteine, glutamine, and glutamic acid were among the most abundant amino acids present in all species. The results showed that the analyzed mushrooms possessed significant free amino acid contents, which may be important compounds contributing to the typical mushroom taste, nutritional value, and potent antioxidant properties of these wild edible mushrooms. Furthermore, the principal component analysis (PCA) showed that the accumulative variance contribution rate of the first four principal components reached 94.39%. Cluster analysis revealed EAA composition and content might be an important parameter to separate the mushroom species, and T. microcarpus and A. hemibapha showed remarkable EAA content among the 13 species.

An Evolutionary View on Disulfide Bond Connectivities Prediction Using Phylogenetic Trees and a Simple Cysteine Mutation Model.

Disulfide bonds are crucial for many structural and functional aspects of proteins. They have a stabilizing role during folding, can regulate enzymatic activity and can trigger allosteric changes in the protein structure. Moreover, knowledge of the topology of the disulfide connectivity can be relevant in genomic annotation tasks and can provide long range constraints for ab-initio protein structure predictors. In this paper we describe PhyloCys, a novel unsupervised predictor of disulfide bond connectivity from known cysteine oxidation states. For each query protein, PhyloCys retrieves and aligns homologs with HHblits and builds a phylogenetic tree using ClustalW. A simplified model of cysteine co-evolution is then applied to the tree in order to hypothesize the presence of oxidized cysteines in the inner nodes of the tree, which represent ancestral protein sequences. The tree is then traversed from the leaves to the root and the putative disulfide connectivity is inferred by observing repeated patterns of tandem mutations between a sequence and its ancestors. A final correction is applied using the Edmonds-Gabow maximum weight perfect matching algorithm. The evolutionary approach applied in PhyloCys results in disulfide bond predictions equivalent to Sephiroth, another approach that takes whole sequence information into account, and is 26-29% better than state of the art methods based on cysteine covariance patterns in multiple sequence alignments, while requiring one order of magnitude fewer homologous sequences (10(3) instead of 10(4)), thus extending its range of applicability. The software described in this article and the datasets used are available at http://ibsquare.be/phylocys.

Clustering-based model of cysteine co-evolution improves disulfide bond connectivity prediction and reduces homologous sequence requirements.

MOTIVATION: Cysteine residues have particular structural and functional relevance in proteins because of their ability to form covalent disulfide bonds. Bioinformatics tools that can accurately predict cysteine bonding states are already available, whereas it remains challenging to infer the disulfide connectivity pattern of unknown protein sequences. Improving accuracy in this area is highly relevant for the structural and functional annotation of proteins. RESULTS: We predict the intra-chain disulfide bond connectivity patterns starting from known cysteine bonding states with an evolutionary-based unsupervised approach called Sephiroth that relies on high-quality alignments obtained with HHblits and is based on a coarse-grained cluster-based modelization of tandem cysteine mutations within a protein family. We compared our method with state-of-the-art unsupervised predictors and achieve a performance improvement of 25-27% while requiring an order of magnitude less of aligned homologous sequences (∼10(3) instead of ∼10(4)). AVAILABILITY AND IMPLEMENTATION: The software described in this article and the datasets used are available at http://ibsquare.be/sephiroth. CONTACT: wvranken@vub.ac.be SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

Dinosolve: a protein disulfide bonding prediction server using context-based features to enhance prediction accuracy.

BACKGROUND: Disulfide bonds play an important role in protein folding and structure stability. Accurately predicting disulfide bonds from protein sequences is important for modeling the structural and functional characteristics of many proteins. METHODS: In this work, we introduce an approach of enhancing disulfide bonding prediction accuracy by taking advantage of context-based features. We firstly derive the first-order and second-order mean-force potentials according to the amino acid environment around the cysteine residues from large number of cysteine samples. The mean-force potentials are integrated as context-based scores to estimate the favorability of a cysteine residue in disulfide bonding state as well as a cysteine pair in disulfide bond connectivity. These context-based scores are then incorporated as features together with other sequence and evolutionary information to train neural networks for disulfide bonding state prediction and connectivity prediction. RESULTS: The 10-fold cross validated accuracy is 90.8% at residue-level and 85.6% at protein-level in classifying an individual cysteine residue as bonded or free, which is around 2% accuracy improvement. The average accuracy for disulfide bonding connectivity prediction is also improved, which yields overall sensitivity of 73.42% and specificity of 91.61%. CONCLUSIONS: Our computational results have shown that the context-based scores are effective features to enhance the prediction accuracies of both disulfide bonding state prediction and connectivity prediction. Our disulfide prediction algorithm is implemented on a web server named "Dinosolve" available at: http://hpcr.cs.odu.edu/dinosolve.

DiANNA 1.1: an extension of the DiANNA web server for ternary cysteine classification.

DiANNA is a recent state-of-the-art artificial neural network and web server, which determines the cysteine oxidation state and disulfide connectivity of a protein, given only its amino acid sequence. Version 1.0 of DiANNA uses a feed-forward neural network to determine which cysteines are involved in a disulfide bond, and employs a novel architecture neural network to predict which half-cystines are covalently bound to which other half-cystines. In version 1.1 of DiANNA, described here, we extend functionality by applying a support vector machine with spectrum kernel for the cysteine classification problem-to determine whether a cysteine is reduced (free in sulfhydryl state), half-cystine (involved in a disulfide bond) or bound to a metallic ligand. In the latter case, DiANNA predicts the ligand among iron, zinc, cadmium and carbon. Available at: http://bioinformatics.bc.edu/clotelab/DiANNA/.

Cysteinyl-leukotrienes are released from astrocytes and increase astrocyte proliferation and glial fibrillary acidic protein via cys-LT1 receptors and mitogen-activated protein kinase pathway.

Cysteinyl-leukotrienes (cys-LTs), potent mediators in inflammatory diseases, are produced by nervous tissue, but their cellular source and role in the brain are not very well known. In this report we have demonstrated that rat cultured astrocytes express the enzymes (5'-lipoxygenase and LTC(4) synthase) required for cys-LT production, and release cys-LTs in resting condition and, to a greater extent, in response to calcium ionophore A23187, 1 h combined oxygen-glucose deprivation or 2-methyl-thioATP, a selective P2Y(1)/ATP receptor agonist. MK-886, a LT synthesis inhibitor, prevented basal and evoked cys-LT release. In addition, 2-methyl-thioATP-induced cys-LT release was abolished by suramin, a P2 receptor antagonist, or by inhibitors of ATP binding cassette proteins involved in cys-LT release. We also showed that astrocytes express cys-LT(1) and not cys-LT(2) receptors. The stimulation of these receptors by LTD(4) activated the mitogen-activated protein kinase (MAPK) pathway. This effect was: (i) insensitive to inhibitors of receptor-coupled Gi protein (pertussis toxin) or tyrosine kinase receptors (genistein); (ii) abolished by MK-571, a cys-LT(1) selective receptor antagonist, or PD98059, a MAPK inhibitor; (iii) reduced by inhibitors of calcium/calmodulin-dependent kinase II (KN-93), Ca(2+)-dependent and -independent (GF102903X) or Ca(2+)-dependent (Gö6976) protein kinase C isoforms. LTD(4) also increased astrocyte proliferation and glial fibrillary acidic protein content, which are considered hallmarks of reactive astrogliosis. Both effects were counteracted by cell pretreatment with MK-571 or PD98059. Thus, cys-LTs released from astrocytes might play an autocrine role in the induction of reactive astrogliosis that, in brain injuries, contributes to the formation of a reparative glial scar.