Symmetric Key Structural Residues in Symmetric Proteins with Beta-Trefoil Fold

To understand how symmetric structures of many proteins are formed from asymmetric sequences, the proteins with two repeated beta-trefoil domains in Plant Cytotoxin B-chain family and all presently known beta-trefoil proteins are analyzed by structure-based multi-sequence alignments. The results show that all these proteins have similar key structural residues that are distributed symmetrically in their structures. These symmetric key structural residues are further analyzed in terms of inter-residues interaction numbers and B-factors. It is found that they can be distinguished from other residues and have significant propensities for structural framework. This indicates that these key structural residues may conduct the formation of symmetric structures although the sequences are asymmetric

Sequencing and Analysis of a Miraculin Homolog from Two Ragweed Species

Our lab has previously isolated the gene for miraculin, a taste-modifying protein, from the pollen of Ambrosia trifida. This weedy plant, commonly known as Giant Ragweed, is a major producer of pollen that causes hay fever. The cDNA sequence of this gene was used to design PCR primers for the amplification and sequencing of genomic DNA from this species, as well as a related species, common ragweed (Ambrosia artemisiifolia). These genomic sequences were analyzed in terms of intron structure, phylogenetics, and protein structure and function. These gene sequences provide novel insights into the possible roles of proteases in plants

Highly Accurate Fragment Library for Protein Fold Recognition

Proteins play a crucial role in living organisms as they perform many vital tasks in every living cell. Knowledge of protein folding has a deep impact on understanding the heterogeneity and molecular functions of proteins. Such information leads to crucial advances in drug design and disease understanding. Fold recognition is a key step in the protein structure discovery process, especially when traditional computational methods fail to yield convincing structural homologies. In this work, we present a new protein fold recognition approach using machine learning and data mining methodologies. First, we identify a protein structural fragment library (Frag-K) composed of a set of backbone fragments ranging from 4 to 20 residues as the structural “keywords” that can effectively distinguish between major protein folds. We firstly apply randomized spectral clustering and random forest algorithms to construct representative and sensitive protein fragment libraries from a large-scale of high-quality, non-homologous protein structures available in PDB. We analyze the impacts of clustering cut-offs on the performance of the fragment libraries. Then, the Frag-K fragments are employed as structural features to classify protein structures in major protein folds defined by SCOP (Structural Classification of Proteins). Our results show that a structural dictionary with ~400 4- to 20-residue Frag-K fragments is capable of classifying major SCOP folds with high accuracy. Then, based on Frag-k, we design a novel deep learning architecture, so-called DeepFrag-k, which identifies fold discriminative features to improve the accuracy of protein fold recognition. DeepFrag-k is composed of two stages: the first stage employs a multimodal Deep Belief Network (DBN) to predict the potential structural fragments given a sequence, represented as a fragment vector, and then the second stage uses a deep convolution neural network (CNN) to classify the fragment vectors into the corresponding folds. Our results show that DeepFrag-k yields 92.98% accuracy in predicting the top-100 most popular fragments, which can be used to generate discriminative fragment feature vectors to improve protein fold recognition

Tracing the molecular and evolutionary determinants of novel functions in protein families

This thesis explores the limits of homology-based inference of protein function and evolution, where overall similarity between sequences can be a poor indicator of functional similarity or evolutionary relationships. Each case presented has undergone different patterns of evolutionary change due to differing selective pressures. Surface adaptations and regulatory (e.g., gene expression) divergence are examined as molecular determinants of novel functions whose patterns are easily missed by assessments of overall sequence similarity. Following this, internal repeats and mosaic sequences are investigated as cases in which key evolutionary events involving fragments of protein sequences are masked by overall comparison. Lastly, virulence factors, which cannot be unified based on sequence, are predicted by analysis of elevated host-mimicry patterns in pathogenic versus non-pathogenic bacterial genomes. These patterns have resulted from unique co-evolutionary pressures that apply to bacterial pathogens, but may be lacking in their close relatives. A recurring theme in the proteins/genes/genomes analyzed is an involvement in microbial pathogenesis or pathogen-defense. Due to the ongoing "evolutionary arms race" between hosts and pathogens, virulence and defense proteins have undergone—and will likely continue to generate—evolutionary novelties. Thus, they demonstrate the necessity to look beyond overall sequence comparison, and assess multiple dimensions of functional innovation in proteins

SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

<p>Abstract</p> <p>Background</p> <p>Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction.</p> <p>Results</p> <p>SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors.</p> <p>Conclusion</p> <p>The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are capable of separating the structural classes in spite of their low dimensionality. We also demonstrate that the SCPRED's predictions can be successfully used as a post-processing filter to improve performance of modern fold classification methods.</p

The antiviral potential of algal lectins

Algae have emerged as fascinating subjects of study due to their vast potential as sources of valuable metabolites with diverse biotechnological applications, including their use as fertilizers, feed, food, and even pharmaceutical precursors. Among the numerous compounds found in algae, lectins have garnered special attention for their unique structures and carbohydrate specificities, distinguishing them from lectins derived from other sources. Here, a comprehensive overview of the latest scientific and technological advancements in the realm of algal lectins with a particular focus on their antiviral properties is provided. These lectins have displayed remarkable effectiveness against a wide range of viruses, thereby holding great promise for various antiviral applications. It is worth noting that several alga species have already been successfully commercialized for their antiviral potential. However, the discovery of a diverse array of lectins with potent antiviral capabilities suggests that the field holds immense untapped potential for further expansion. In conclusion, algae stand as a valuable and versatile resource, and their lectins offer an exciting avenue for developing novel antiviral agents, which may lead to the development of cutting-edge antiviral therapies.info:eu-repo/semantics/publishedVersio

Rational use of dietary enzymes and lipids to improve broiler performance and meat quality

Tese de Doutoramento em Ciências Veterinárias. Especialidade de Ciências Biológicas e Biomédicas.The importance of carbohydrate-binding modules (CBMs) and the use of novel enzymes with specific catalytic activities to improve the nutritive value of barley based diets for broilers and the effectiveness of a lipidic supplementation to improve the levels of benefic fatty acids in broilers meat remain to be investigated. In this work we studied the importance of a β-glucan binding domain (CBM11) when appended to three different enzymes (GH26GH5 and GH16, belonging to Clostridium thermocellum, and GH5, belonging to Celvibrio mixtus) to improve the nutritional quality of barley-based diets for broilers. In addition, the crystal structure and biochemical properties of a family 42 carbohydrate binding module (CBM) from Clostridium thermocellum, termed CtCBM42A, were investigated. Data presented here revealed that CBM11 has an important target effect in directing the appended catalytic modules to their target substrates, resulting in an improvement in broiler performance. However, this effect seems to be dependent on the level of supplementation. In addition, barley composition, namely its endogenous β-glucanase activity, influences the response to enzyme supplementation. Thus, exogenous enzymes were shown to be ineffective when used to supplement barleys expressing high endogenous β- glucanase activity. CtCBM42A revealed to be a type C CBM with three subdomains (α, β and γ), with affinity for arabinoxylan (arabinose side chains) and arabinan. The γ subdomain seems to dominate ligand recognition for arabinoxylan while the β and γ subdomains cooperate in arabinan recognition. Thus, CtCBM42A is potentially a good candidate for strategies aimed at improving the nutritive value of wheat-based diets for broilers. In order to improve the fatty acid profile of poultry meat, two different lipidic sources, extruded linseed and a subproduct of a marine alga (DHA goldTM), were used to supplement broiler diets. This experiment allowed the evaluation of the metabolic rates of the biosynthetic pathway of long-chain ómega 3 polyunsaturated fatty acids (LC n-3 PUFA).The supplementation of broiler diets with DHA goldTM and extruded linseed showed that conversion of linolenic acid in LC n-3 PUFA is not effective and, consequently, direct supplementation with LC n-3 PUFA seems to be the best option to enrich and improve LC n-3 PUFA in broilers meat. However, higher incorporation dosages of DHA goldTM could affect meat quality.RESUMO - Efeito da suplementação enzimática e lipídica de dietas para frangos no desempenho produtivo e na qualidade da carne - Uma melhor adequação da qualidade dos produtos animais, em concreto da carne de frango, às necessidades nutricionais dos consumidores, associada a uma maior eficiência de transformação dos alimentos para animais em produtos edíveis, são aspectos da maior importância prática na avicultura moderna e suscitam uma análise científica detalhada. Neste trabalho estudou-se a aplicação de um módulo de ligação ao β-glucano (CBM11), acoplado a três enzimas diferentes (GH26GH5 e GH16, ambas pertencentes ao Clostridium thermocellum, e a GH5, pertencente ao Celvibrio mixtus) na melhoria do valor nutritivo de dietas à base de cevada para frangos de carne. Foram também determinadas as propriedades bioquímicas e a estrutura cristalográfica do CBM da família 42 do Clostridium thermocellum, CtCBM42A. Os resultados demonstraram que o CBM11 tem um efeito importante no direccionamento do módulo catalítico das enzimas ao substrato, que resulta num aumento da performance zootécnica dos frangos de carne. No entanto, esse efeito parece estar dependente da dose enzimática aplicada. Demonstrou-se também que a composição das cevadas, principalmente a actividade endo-β-glucanásica, influencia o efeito da suplementação enzimática. Em cevadas com actividade endo-β-glucanásica alta a suplementação enzimática tem um efeito redundante não se obtendo melhoria da performance dos frangos de carne. O estudo do CBM42 revelou que se trata dum CBM do tipo C, com três subdomínios (α, β e γ), com afinidade para o arabinoxilano (nas suas cadeias laterais de arabinose) e arabinano. O subdomínio γ parece ser o responsável pela afinidade ao arabinoxilano enquanto o subdomínio β juntamente com o γ parecem interagir pela afinidade ao arabinano, revelando-se como um módulo potencialmente interessante para uma futura utilização na suplementação enzimática de dietas à base de trigo para frangos. Foram efectuados ensaios com frangos de carne cujas dietas foram suplementadas com semente de linho extrudida e um subproduto de algas marinhas (DHA goldTM) para estudar os seus efeitos no perfil dos ácidos gordos da carne e na qualidade da carne. Também se avaliou a extensão da bioconversão dos percursores ácidos linoleico (LA) e linolénico (LNA) nos seus homólogos de cadeia longa. Os resultados mostraram que a conversão dos ácidos gordos não é eficiente e por isso a suplementação directa com uma fonte de ácidos gordos de cadeia longa parece ser a melhor opção para melhorar o conteúdo de ácidos gordos ómega-3 de cadeia longa. No entanto, a qualidade da carne pode estar afectada negativamente em doses de incorporação elevadas de DHA goldTM.This work was funded by Fundação para a Ciência e a Tecnologia, grant SFRH/BD/32321/2006, and co-funded by POCI 2010 and FSE from Ministério da Ciência, Tecnologia e Ensino Superio

Phobalysin: fisheye view of membrane perforation, repair, chemotaxis and adhesion

Phobalysin P (PhlyP, for photobacterial lysin encoded on a plasmid) is a recently described small %26beta;-pore forming toxin of Photobacterium damselae subsp. damselae (Pdd). This organism, belonging to the family of Vibrionaceae, is an emerging pathogen of fish and various marine animals, which occasionally causes life-threatening soft tissue infections and septicemia in humans. By using genetically modified Pdd strains, PhlyP was found to be an important virulence factor. More recently, in vitro studies with purified PhlyP elucidated some basic consequences of pore formation. Being the first bacterial small %26beta;-pore forming toxin shown to trigger calcium-influx dependent membrane repair, PhlyP has advanced to a revealing model toxin to study this important cellular function. Further, results from co-culture experiments employing various Pdd strains and epithelial cells together with data on other bacterial toxins indicate that limited membrane damage may generally enhance the association of bacteria with target cells. Thereby, remodeling of plasma membrane and cytoskeleton during membrane repair could be involved. In addition, a chemotaxis-dependent attack-and track mechanism influenced by environmental factors like salinity may contribute to PhlyP-dependent association of Pdd with cells. Obviously, a synoptic approach is required to capture the regulatory links governing the interaction of Pdd with target cells. The characterization of Pdd%26rsquo;s secretome may hold additional clues because it may lead to the identification of proteases activating PhlyP%26rsquo;s pro-form. Current findings on PhlyP support the notion that pore forming toxins are not just killer proteins but serve bacteria to fulfill more subtle functions, like accessing their host

Integrated mining of feature spaces for bioinformatics domain discovery

One of the major challenges in the field of bioinformatics is the elucidation of protein folding for the functional annotation of proteins. The factors that govern protein folding include the chemical, physical, and environmental conditions of the protein\u27s surroundings, which can be measured and exploited for computational discovery purposes. These conditions enable the protein to transform from a sequence of amino acids to a globular three-dimensional structure. Information concerning the folded state of a protein has significant potential to explain biochemical pathways and their involvement in disorders and diseases. This information impacts the ways in which genetic diseases are characterized and cured and in which designer drugs are created. With the exponential growth of protein databases and the limitations of experimental protein structure determination, sophisticated computational methods have been developed and applied to search for, detect, and compare protein homology. Most computational tools developed for protein structure prediction are primarily based on sequence similarity searches. These approaches have improved the prediction accuracy of high sequence similarity proteins but have failed to perform well with proteins of low sequence similarity. Data mining offers unique algorithmic computational approaches that have been used widely in the development of automatic protein structure classification and prediction. In this dissertation, we present a novel approach for the integration of physico-chemical properties and effective feature extraction techniques for the classification of proteins. Our approaches overcome one of the major obstacles of data mining in protein databases, the encapsulation of different hydrophobicity residue properties into a much reduced feature space that possess high degrees of specificity and sensitivity in protein structure classification. We have developed three unique computational algorithms for coherent feature extraction on selected scale properties of the protein sequence. When plagued by the problem of the unequal cardinality of proteins, our proposed integration scheme effectively handles the varied sizes of proteins and scales well with increasing dimensionality of these sequences. We also detail a two-fold methodology for protein functional annotation. First, we exhibit our success in creating an algorithm that provides a means to integrate multiple physico-chemical properties in the form of a multi-layered abstract feature space, with each layer corresponding to a physico-chemical property. Second, we discuss a wavelet-based segmentation approach that efficiently detects regions of property conservation across all layers of the created feature space. Finally, we present a unique graph-theory based algorithmic framework for the identification of conserved hydrophobic residue interaction patterns using identified scales of hydrophobicity. We report that these discriminatory features are specific to a family of proteins, which consist of conserved hydrophobic residues that are then used for structural classification. We also present our rigorously tested validation schemes, which report significant degrees of accuracy to show that homologous proteins exhibit the conservation of physico-chemical properties along the protein backbone. We conclude our discussion by summarizing our results and contributions and by listing our goals for future research

Effect of dietary algal polysaccharides on immune-related gene expression in zebrafish (Danio rerio) mucosal tissues

Masteroppgave i genomikk - Nord universitet 202