Set Of Human Proteins Research Articles

Computational methods for recognition of cancer protein markers in saliva.

In recent years, many studies have supported that cancer tissues can make disease-specific changes in some salivary proteins through some mediators in the pathogenesis of systemic diseases. These salivary proteins have the potential to become cancer-specific biomarkers in the early diagnosis stage. How to effectively identify these potential markers is one of the challenging issues. In this paper, we propose novel machine learning methods for recognition cancer biomarkers in saliva by two stages. In the first stage, salivary secreted proteins are recognized which are considered as candidate biomarkers of cancers. We picked up 557 salivary secretory proteins from 20379 human proteins by public databases and published literatures. Then, we present a training set construction strategy to solve the imbalance problem in order to make the classification methods get better accuracy. From all human protein set, the proteins belonging to the same families as salivary secretory proteins are removed. After that, we use SVC-KM method to cluster the remaining proteins, and select negative samples from each cluster in proportion. Next, the features of proteins are calculated by tools. We collect 24 protein properties such as sequence, structure and physicochemical properties, a total of 1087 features. An innovative procedure based on the local samples is proposed for selecting the appropriate features, in order to further improve the performance of SVM classifier. Experimental results show that the average sensitivity, specificity and accuracy of salivary secretory protein recognition using selected 32 features in training set are 97.09%, 98.10%, 97.61%, respectively. The use of these methods can improve the accuracy of recognition by solving the problems of unbalanced sample size and uneven distribution in training set. In the second stage, we apply the best model to dig out the salivary secreted proteins from 58 reported cancer markers, and get a total of 42 proteins which are considered to be used for salivary diagnosis. We analyze the gene expression data of three types of cancer, and predict that 33 genes will appear in saliva after they are translated into proteins. This study provides an important computational tool to help biologists and researchers reduce the number of candidate proteins and the cost of research. So as to further accelerate the discovery of cancer biomarkers in saliva and promote the development of saliva diagnosis.

A computational method for prediction of saliva-secretory proteins and its application to identification of head and neck cancer biomarkers for salivary diagnosis.

Human saliva is rich in proteins, which have been used for disease detection such as oral diseases and systematic diseases. In this paper, we present a computational method for predicting secretory proteins in human saliva based on two sets of human proteins from published literatures and public databases. One set contains known proteins which can be secreted into saliva, and the other contains the proteins that are deemed to be not extracellular secretion. The protein features with discerning power between two sets were firstly gathered. Then a classifier was trained based on the identified features to predict whether a protein was saliva-secretory one or not. The average values of the sensitivity, specificity, precision, accuracy, and Matthews correlation coefficient value by 10-fold cross validation repeated 100 times were 80.67%, 90.56%, 90.09%, 85.53%, and 0.7168, respectively. These results indicated that our selected features are informative. We applied the classifier for prediction saliva-secretory proteins out of all human proteins, if a known biomarker was likely to enter into saliva, and the potential salivary biomarkers for head and neck squamous cell carcinoma. We also compared the top 1000 proteins predicted by computational methods in different kind of fluids. This work provided a useful tool for effectively identifying the salivary biomarkers for various human diseases and facilitate the development of salivary diagnosis.

The centrality of cancer proteins in human protein-protein interaction network: a revisit.

Topological analysis of protein-protein interaction (PPI) networks has been widely applied to the investigation on cancer mechanisms. However, there is still a debate on whether cancer proteins exhibit more topological centrality compared to the other proteins in the human PPI network. To resolve this debate, we first identified four sets of human proteins, and then mapped these proteins into the yeast PPI network by homologous genes. Finally, we compared these proteins' properties in human and yeast PPI networks. Experiments over two real datasets demonstrated that cancer proteins tend to have higher degree and smaller clustering coefficient than non-cancer proteins. Experimental results also validated that cancer proteins have larger betweenness centrality compared to the other proteins on the STRING dataset. However, on the BioGRID dataset, the average betweenness centrality of cancer proteins is larger than that of disease and control proteins, but smaller than that of essential proteins.

A Novel Biclustering Approach to Association Rule Mining for Predicting HIV-1–Human Protein Interactions

Identification of potential viral-host protein interactions is a vital and useful approach towards development of new drugs targeting those interactions. In recent days, computational tools are being utilized for predicting viral-host interactions. Recently a database containing records of experimentally validated interactions between a set of HIV-1 proteins and a set of human proteins has been published. The problem of predicting new interactions based on this database is usually posed as a classification problem. However, posing the problem as a classification one suffers from the lack of biologically validated negative interactions. Therefore it will be beneficial to use the existing database for predicting new viral-host interactions without the need of negative samples. Motivated by this, in this article, the HIV-1–human protein interaction database has been analyzed using association rule mining. The main objective is to identify a set of association rules both among the HIV-1 proteins and among the human proteins, and use these rules for predicting new interactions. In this regard, a novel association rule mining technique based on biclustering has been proposed for discovering frequent closed itemsets followed by the association rules from the adjacency matrix of the HIV-1–human interaction network. Novel HIV-1–human interactions have been predicted based on the discovered association rules and tested for biological significance. For validation of the predicted new interactions, gene ontology-based and pathway-based studies have been performed. These studies show that the human proteins which are predicted to interact with a particular viral protein share many common biological activities. Moreover, literature survey has been used for validation purpose to identify some predicted interactions that are already validated experimentally but not present in the database. Comparison with other prediction methods is also discussed.

Prestige Antibodies™ - Monospecific Antibodies Designed for Immunohistochemical Analysis

Well-characterized antibodies are essential tools for protein studies, global proteomics analysis, as well as for clinical diagnostics. However, although production of antibodies is a well-established process and a large number of antibodies are commercially available through many vendors, specific antibodies still do not exist for the majority of human proteins. An underlying factor limiting the available antibody repertoire is that commercial production tends to focus on popular targets. The current needs of the proteomics community demand a far more global approach (Blow, Nature, Vol. 447, 2007, 741742; Editorial, Nature Methods, Vol. 1, 2007, 1-2). A second significant issue is the lack of a universally defined standard for antibody quality. This makes it difficult to compare antibodies of various sources without committing resources to using the antibody in its final application. Initial standardized testing for specificity and sensitivity followed by a thorough characterization would be of great interest to the end user, but this is a costly endeavor and most efficiently accomplished on a larger scale. At present, there are a handful of large-scale high-throughput antibody production efforts initiated around the world (Persson et al, Curr. Opin. Mol. Ther., Vol. 8, 2006, 185190). One such initiative is the Swedish Human Protein Atlas (HPA) program (Uhlen et al, Mol. Cell Proteomics, Vol. 4, 2005, 1920-1932). The aim of this program is to explore the entire human proteome using an antibody-based proteomics approach (Kampf et al, Clin. Proteomics, Vol.1, 2004, 285300; Uhlen and Ponten, Mol. Cell Proteomics, Vol. 4, 2005, 384-393). Specifically, the HPA program generates protein expression profiles of the non-redundant set of human proteins, presented as immunohistological images from the majority of human tissues. All images are annotated and made publicly available via an open access database, the Human Protein Atlas (www.proteinatlas.org). An ambitious and thorough quality-control process has been developed by which all antibodies have to pass a set of criteria prior to being tested by immunohistochemistry (IHC) and other methods (Hober and Uhlen, Curr. Opin. Biotech., Vol. 19, 2007, 1-6).

An informatics search for the low-molecular weight chromium-binding peptide

BackgroundThe amino acid composition of a low molecular weight chromium binding peptide (LMWCr), isolated from bovine liver, is reportedly E:G:C:D::4:2:2:2, though its sequence has not been discovered. There is some controversy surrounding the exact biochemical forms and the action of Cr(III) in biological systems; the topic has been the subject of many experimental reports and continues to be investigated. Clarification of Cr-protein interactions will further understanding Cr(III) biochemistry and provide a basis for novel therapies based on metallocomplexes or small molecules.ResultsA genomic search of the non-redundant database for all possible decapeptides of the reported composition yields three exact matches, EDGEECDCGE, DGEECDCGEE and CEGGCEEDDE. The first two sequences are found in ADAM 19 (A Disintegrin and Metalloproteinase domain 19) proteins in man and mouse; the last is found in a protein kinase in rice (Oryza sativa). A broader search for pentameric sequences (and assuming a disulfide dimer) corresponding to the stoichiometric ratio E:D:G:C::2:1:1:1, within the set of human proteins and the set of proteins in, or related to, the insulin signaling pathway, yields a match at an acidic region in the α-subunit of the insulin receptor (-EECGD-, residues 175–184). A synthetic peptide derived from this sequence binds chromium(III) and forms a metal-peptide complex that has properties matching those reported for isolated LMWCr and Cr(III)-containing peptide fractions.ConclusionThe search for an acidic decameric sequence indicates that LMWCr may not be a contiguous sequence. The identification of a distinct pentameric sequence in a significant insulin-signaling pathway protein suggests a possible identity for the LMWCr peptide. This identification clarifies directions for further investigation of LMWCr peptide fractions, chromium bio-coordination chemistry and a possible role in the insulin signaling pathway. Implications for models of chromium action in the insulin-signaling pathway are discussed.

A survey of canine expressed sequence tags and a display of their annotations through a flexible web-based interface.

We have initially sequenced approximately 8,000 canine expressed sequence tags (ESTs) from several complementary DNA (cDNA) libraries: testes, whole brain, and Madin-Darby canine kidney (MDCK) cells. Analysis of these sequences shows that they provide partial sequence information for about 5%-10% of the canine genes. An analysis pipeline has been created to cluster the ESTs and to map individual ESTs as well as clustered ESTs to both the human genome and the human proteome. Gene ontology (GO) terms have been assigned to the ESTs and clusters based on their top matches to the International Protein Index (IPI) set of human proteins. The data generated is stored in a MySQL relational database for analysis and display. A Web-based Perl script has been written to display the analyzed data to the scientific community.

A Joint Prediction of the Folding Types of 1490 Human Proteins from their Genetic Codons

The codon usages for 1490 human proteins have been published by Wada et al. (1990). Based on these data, the frequencies of occurrence of 20 amino acids for each of the 1490 proteins have been calculated according to the genetic codes. Proteins are generally classified into five folding types, i.e. the α, β, α + β, α/β and ζ (irregular) types. The folding type of a protein is correlated to its amino acid composition. By means of three methods established by different investigators, the folding type for each of the 1490 human proteins has been predicted. It has been demonstrated that the accuracy of prediction for the 1490 human proteins is at least 80% by examining the predicted results of some structurally known proteins with these methods. There are only six proteins for which there is uncertainty about their folding types as completely inconsistent results were obtained when predicted with the three different methods. For the remaining 1484 human proteins the numbers of α, β, α + β, α/β, and ζ folding type proteins were found to be 128, 235, 169, 933 and 19, respectively, suggesting that the α/β type proteins would predominate in this set of human proteins. The occurrence frequencies of bases in the first, second and third codon position for each folding type of protein have been calculated. It is shown that the folding type of a protein is strongly dependent on the ratio of frequency of base G in the first codon position with that in the second codon position. The biological implication of the results has been discussed.