94 results about "Protein network" patented technology

The invention provides fibrin-based, biocompatible materials useful in promoting cell growth, wound healing, and tissue regeneration. These materials are provided as part of several cell and tissue scaffolding structures that provide particular application for use in wound-healing and tissue regenerating. Methods for preparing these compositions and using them are also disclosed as part of the invention. A variety of peptides may be used in conjunction with the practice of the invention, in particular, the peptide IKVAV, and variants thereof. Generally, the compositions may be described as comprising a protein network (e.g., fibrin) and a peptide having an amino acid sequence that comprises a transglutaminase substrate domain (e.g., a factor XIIIa substrate domain) and a bioactive factor (e.g., a peptide or protein, such as a polypeptide growth factor), the peptide being covalently bound to the protein network. Other applications of the technology include their use on implantable devices (e.g., vascular graphs), tissue and cell scaffolding. Other applications include use in surgical adhesive or sealant, as well as in peripheral nerve regeneration and angiogenesis.

A molecular classification procedure based on activity levels of modules in protein networks, wherein the proteins are biomarkers for chronic lymphocytic leukemia (CLL), and method for use of the subnetworks to distinguish between patients at low or high risk of progression of their disease.

The invention provides a method of identifying protein compounds by using a fruit fly optimization method. The method comprises the steps of converting a protein-protein interaction network into a undirected graph, performing pretreatment on the edges and nodes of the protein-protein interaction network, establishing a dynamic protein-protein interaction network, setting parameters, forming fruit fly positions, matching fruit flies with the protein-protein interaction network, determining initialization fruit fly positions, determining the fruit fly odor concentration, updating the fruit fly positions, generating a protein compound, and filtering the protein compound. The method gives full consideration to the dynamic nature of the protein network, the protein compound inner core-attachment structure and the locality and wholeness of the protein-protein interaction network and can identify protein compounds accurately. The results of simulation experiments show that the performance of the indexes such as the accuracy and the recall ratio are excellent. Compared with other clustering methods, the method, based on the characteristics of the protein network and the protein compounds, realizes the protein compound identification process and improves the protein compound identification accuracy.

Methods for producing biocompatible heterogeneous proteinaceous networks crosslinked with a heterogeneous crosslinking agent, and novel heterogeneous crosslinked networks. Preferred heterogeneous crosslinking agents are silicone-based.

The invention discloses a method for predicting miRNA [micro-RNA (ribonucleic acid)] target proteins of miRNA regulation protein interaction networks. The method includes steps of building three sub-networks including a human protein-protein interaction network on the basis of HIPPIE, a miRNA-target protein network on the basis of mirTARbase and a miRNA-miRNA network on the basis of target protein overlapping structures; combining the three sub-networks with one another according to acquisition numbers of proteins and ID (identification) numbers of miRNA molecules in miRbase databases and building fusion miRNA-target protein association relationship networks; representing miRNA-target protein association features on the basis of guilt-by-association principles, building classification prediction models by the aid of random forest and predicting potential interaction association relationships between miRNA and the target proteins. The method has the advantages that research on many-to-many relationships of the miRNA regulation target proteins can be effectively carried out, and accordingly the method has high application value.

The invention relates to a miRNA function recognition method based on multi-genomics, and relates to a miRNA function recognition method based on multi-genome. The present invention aims to solve theproblem of low accuracy of miRNA function recognition in the prior art. The method of the invention comprises: analyzing differentially expressed genes through gene expression profiles; constructing disease-or drug-related protein networks; selecting functional modules of the constructed protein network related to disease or drug; performing enrichment analysis of the selected functional modules to determine the key genes in the functional modules; analyzing differentially expressed miRNAs by miRNA expression profiles to predict the target genes of differentially expressed miRNAs; performing construction of miRNA-regulated disease-or drug-related protein networks; performing functional enrichment analysis on the nodal genes of the constructed miRNA-regulated disease or drug-related proteinnetwork to identify the function of miRNA. The invention is used in the field of bioinformatics.

Systems and methods are for generating a weighted relatedness protein network. The method includes steps of obtaining a protein network; generating training data; generating a weighting function derived from the training data values; and applying the weighting function to a protein network, thereby generating a weighted relatedness protein network. The protein network may be applied for prediction of protein properties by detection of relatedness with annotated sequences.

The invention discloses a method for recognizing a protein network compound based on a semantic density, which is specifically implemented at the steps that all the protein properties in a network data set are searched in a gene ontology base GO as for a protein-protein interaction network data set without a weight; based on searching results, similarity of connected proteins in the network data set is calculated by a semantic similarity calculation method based on gene ontology; according to obtained similarity results, the given protein-protein interaction network data set is converted into an undirected network data set with a weight, wherein nodes represent the proteins, borders stand for interactions among the proteins, and the similarity among the proteins is the weight of the border; and the protein compound can be recognized from a protein-protein interaction network, wherein recognition accuracy is high and time complexity is low.

Gluten-free baked products, particularly gluten-free bread products, which exhibit properties comparable those made with wheat flour. Compositions and methods for preparation of gluten-free baked products. A conditioned protein or protein composite which functions to replace gluten in gluten-free flour. The conditioned protein comprises one more non-wheat cereal storage proteins, particularly prolamins, optionally, but preferably in combination with one or more co-proteins which function to facilitate formation and stabilize formation of a protein network, e.g., β-sheet network, that facilitates retention of CO₂ for leavening. More specifically, protein composites of zein and or other non-wheat prolamins with co-proteins including casein, elastin or mixtures thereof conditioned at temperatures above the glass transition temperature of the protein mixture provide improved ingredients for preparation of gluten-free breads and other baked products. Sorghum or maize mutant flours with prolamins available for viscoelastic protein formation may also be used.

A new fractionation device shows desirable features for exploratory screening and biomarker discovery. The constituent MSCs may be tailored for desired pore sizes and surface properties and for the sequestration and enrichment of extremely low abundant protein and peptides in desired ranges of the mass/charge spectrum. The MSCs are effective in yielding reproducible extracts from complex biological samples as small as 10 μl in a time as short as 30 minutes. They are inexpensive to manufacture, and allow for scaled up production to attain the simultaneous processing of a large number of samples. The MSCs are multiplexed, label-free diagnostic tools with the potential of biological recognition moiety modification for enhanced specificity. The MSCs may store, protect and stabilize biological fluids, enabling the simplified and cost-effective collection and transportation of clinical samples. The MSC-based device may serve as a diagnostic tool to complement histopathology, imaging, and other conventional clinical techniques. The MSCs mediated identification of disease-specific protein signatures may help in the selection of personalized therapeutic combinations, in the real-time assessment of therapeutic efficacy and toxicity, and in the rational modulation of therapy based on the changes in the protein networks associated with the prognosis and the drug resistance of the disease.

The invention discloses a protein function module recognizing method based on a multi-target optimization algorithm. The protein function module recognizing method includes the steps that a network protein node is judged to be a nonoverlapping protein node and a candidate overlapping protein node, the problem of protein network module function mining is solved through hybrid coding, particle swarm initialization and particle swarm evolution. The monotony problem of protein network function module combinations can be solved, multiple module combinations are provided for a user to select, and therefore function module mining accuracy and effectiveness are improved.

The invention provides a protein composite identifying method based on a graph model; a protein interactive network of a given species is regarded as a network drawing =(V, E), wherein V is combining point of protein, and E is set of protein-protein interaction side; the self-connecting side and the repeat side in a network are removed from the set of all sides; the method includes steps of firstly acquiring a nuclear protein vertex set of the protein composite, and then expanding a first-order neighbor of the edge combining point thereof, and forming a graph model; judging its connectivity according to characteristics of the graph model, and finding out all dense subgraphs, namely, protein composite. The method provided by the invention can regard the graph model as the nucleus of the protein composite; through investigating and expanding the first-order neighbor combining point of the graph model, the protein composite is identified; an algorithm provided by the invention is applied to the known yeast protein network; the experimental result indicates that the algorithm can identify multiple protein composites with biological significance, and the algorithm is not sensitive to the input parameter.

A method for annotating a protein sequence or a subsequence thereof includes the steps of providing an input protein sequence or a subsequence thereof. The subsequence is defined as a central node of a graph or protein network. A subgraph of the graph is calculated including the central node, according to a predefined radius and weights and/or resistances of edges of the subgraph are also calculated. Annotated nodes in the subgraph are identified. Resistance values between the central nodes and each of the annotated nodes in the subgraph are calculated and a list of annotated nodes is outputted. Each of the annotated nodes has a characteristic calculated resistance value to the central node of the input protein sequence.

The invention discloses a method for identifying a key protein by adopting an improved flower pollination algorithm. A protein-protein interaction network is converted into an undirected graph, previous Q proteins having the largest choice degree serve as pollen individuals, the PeC centrality is measured, compound information is determined, subcellular localization information is determined, pollen significance is evaluated, a pollen position is updated, and an identified key protein is output. The topological attributes of the protein network are considered and the biological characteristicsof the protein network are fused during evaluation of the pollen significance, and the key protein can be accurately identified. A simulation experiment result shows that the index properties such asaccuracy, specificity and sensitivity are more excellent. Compared with other key protein identifying methods, the key protein identifying process is achieved by combining with the topological attributes and biological characteristics of the protein network, and the key protein identifying accuracy is improved.

The invention provides a method of adopting a firework algorithm to recognize a protein complex. The method includes following steps: converting a protein interaction network into an undirected graph; pre-treating sides and nodes of the protein interaction network; building a dynamic protein interaction network; setting parameters; initializing positions of fireworks; simulating firework explosion to generate spark; selecting part of good points from the spark to serve as fireworks, wherein all fireworks form a class; filtering undesirable classes; outputting a finally-acquired class. The method takes dynamic nature of a protein network and locality and globality of a core-accessory structure inside the protein complex and the protein interaction network into consideration, and the protein complex can be recognized accurately. Simulation experiment results show that performance of indexes like accuracy and recall ratio is excellent. Compared with other clustering methods, the method has the advantages that characteristics of the protein interaction network and the protein complex are combined to recognize the protein complex, and recognition accuracy of the protein complex is improved.

The invention discloses a key protein identification method use principle in a network based on dynamic weighting interaction. A key protein identification method comprises the steps of calculating aprotein activity time point and a protein activity probability, constructing a dynamic PPI network, then calculating a protein interaction weight according to the protein activity probability, and constructing a dynamic weighted PPI network; based on the established dynamic weighted PPI network, according to the topological characteristic and biological attribute of the protein network, calculating an edge clustering coefficient, a gene body similarity and a Pearson correlation coefficient between the interaction protein pairs; afterwards, obtaining an importance score, and performing arrangement according to the score value from largest to lowest, and outputting k proteins which correspond with the scores as a final result. The method according to the invention improves key protein identification efficiency and expands application range and practicability of the technique in a bioinformation field.

The invention discloses a monoclonal antibody of 1-aminocyclopropane-1-carboxylate oxidase. A heavy chain variable region sequence of the monoclonal antibody contains an amino acid sequence of CDR shown as SEQ ID NO. 2, 4, and 6; and a light chain variable region sequence of the monoclonal antibody contains an amino acid sequence of CDR shown as SEQ ID NO. 9, 11, and 13. A polypeptide of the enzyme is used as an immunogen to immunize a mouse to obtain lymphocyte, a fusion cell is prepared by a hybridoma cell fusion technique, and a cell strain which can generate high affinity and high specificity and is used for detecting and identifying the enzyme is obtained. The monoclonal antibody of 1-aminocyclopropane-1-carboxylate oxidase can be used for detecting the content change of the 1-aminocyclopropane-1-carboxylate oxidase in different developmental stages of fruits. The monoclonal antibody disclosed by the invention provides a tool for targeted protein study, so that deeper study on protein expression changes and protein network maps of different plant types, different strains or different development stages is realized.

The invention discloses a method for preparing chitosan soluble gauze rich in growth factors. The method comprises the steps of S1, weighing auxiliary materials according to a weight ratio to prepare blood platelet pretreatment liquid; S2, preparing blood platelet freeze-dried buffer solution; S3, preparing pretreated blood platelet rich plasma; S4, preparing carboxymethyl chitosan solution; and S5, preparing the chitosan soluble gauze rich in growth factors. After chitosan is gelated, the blood platelet rich plasma is added to obtain the chitosan soluble gauze rich in growth factors, pore diameter is 130-160 [mu]m, a large number of growth factors and abundant fibrous protein networks are contained and can be absorbed by the human body easily, no toxic and side effects can be caused, growth of various bacteria and fungi can be restrained, more structure space is provided for cell growth, in-vivo and in-vitro release of various growth factors can be achieved, tissue repair and vascular remodeling can be achieved easily, antibacterial performance is excellent, and the chitosan soluble gauze is a novel medical and biologically hemostatic material with high cost performance.

The invention discloses a method for constructing a novel visual dynamic protein network reflecting proteome changes, which comprises the steps of: (1) correcting the expression quantity numerical value of a differential protein in a dynamic proteomic research at each time point: S=(Vtest-Vcontrol)/Vcontrol; (2) uploading an inlet number of a protein database and an S numerical value to a MetaCore network analyzing server and drawing an original protein network map; (3) adjusting the positions of nodes superposed in the original protein network map; (4) activating n data documents sequentially to obtain n network maps with protein expression information at time points, respectively; (5) organizing the network maps in form of flash documents through a time shaft to form a dynamic network map; and (6) adding a cell contour and a popup information frame to the dynamic network map. The method has a significant application value to displaying and researching proteome changes in various biological processes.

The invention provides a method for screening tumor protein markers on the basis of a multilayer complex network. According to the method, node betweennesses of a random forest mode and of a complex network are combined to provide a new visual angle to discover the tumor pathogenic factors and diagnosis markers. Through bioinformatics and mathematic statistical analysis, the correlation of multilayer protein network data is established and a screening method which is more convenient and higher in correctness is disclosed, so that more valuable reference is provided or the cancer diagnosis and drug discovery.