Biomedical subjects

Ovidiu Ivanciuc

Publications and source records attributed to Ovidiu Ivanciuc.

8 recordsLinked to original sources

PubMed2006

Modeling the bioconcentration factors and bioaccumulation factors of polychlorinated biphenyls with posetic quantitative super-structure/activity relationships (QSSAR).

During bioconcentration, chemical pollutants from water are absorbed by aquatic animals via the skin or a respiratory surface, while the entry routes of chemicals during bioaccumulation are both directly from the environment (skin or a respiratory surface) and indirectly from food. The bioconcentration factor (BCF) and the bioaccumulation factor (BAF) for a particular chemical compound are defined as the ratio of the concentration of a chemical inside an organism to the concentration in the surrounding environment. Because the experimental determination of BAF and BCF is time-consuming and expensive, it is efficacious to develop models to provide reliable activity predictions for a large number of chemical compounds. Polychlorinated biphenyls (PCBs) released from industrial activities are persistent pollutants of the environment that produce widespread contamination of water and soil. PCBs can bioaccumulate in the food chain, constituting a potential source of exposure for the general population. To predict the bioconcentration and bioaccumulation factors for PCBs we make use of the biphenyl substitution-reaction network for the sequential substitution of H-atoms by Cl-atoms. Each PCB structure then occurs as a node of this reaction network, which is some sort of super-structure, turning out mathematically to be a partially ordered set (poset). Rather than dealing with the molecular structure via ordinary QSAR we use only this poset, making different quantitative super-structure/activity relationships (QSSAR). Thence we developed cluster expansion and splinoid QSSARs for PCB bioconcentration and bioaccumulation factors. The predictive ability of the BAF and BCF models generated for 20 data sets (representing different conditions and fish species) was evaluated with the leave-one-out cross-validation, which shows that the splinoid QSSAR (r between 0.903 and 0.935) are better than models computed with the cluster expansion (r between 0.745 and 0.887). The splinoid QSSAR models for BAF and BCF yield predictions for the missing PCBs in the investigated data sets.

Animals↗

PubMed2005

Common physical-chemical properties correlate with similar structure of the IgE epitopes of peanut allergens.

Although many sequences and linear IgE epitopes of allergenic proteins have been identified and archived in databases, structural and physicochemical discriminators that define their specific properties are lacking. Current bioinformatics tools for predicting the potential allergenicity of a novel protein use methods that were not designed to compare peptides. Novel tools to determine the quantitative sequence and three-dimensional (3D) relationships between IgE epitopes of major allergens from peanut and other foods have been implemented in the Structural Database of Allergenic Proteins (SDAP; http://fermi.utmb.edu/SDAP/). These peptide comparison tools are based on five-dimensional physicochemical property (PCP) vectors. Sequences from SDAP proteins similar in their physicochemical properties to known epitopes of Ara h 1 and Ara h 2 were identified by calculating property distance (PD) values. A 3D model of Ara h 1 was generated to visualize the 3D structure and surface exposure of the epitope regions and peptides with a low PD value to them. Many sequences similar to the known epitopes were identified in related nut allergens, and others were within the sequences of Ara h 1 and Ara h 2. Some of the sequences with low PD values correspond to other known epitopes. Regions with low PD values to one another in Ara h 1 had similar predicted structure, on opposite sides of the internal dimer axis. The PD scale detected epitope pairs that are similar in structure and/or reactivity with patient IgE. The high immunogenicity and IgE reactivity of peanut allergen proteins might be due to the proteins' arrays of similar antigenic regions on opposite sides of a single protein structure.

2S Albumins, Plant↗

PubMed2004

Using property based sequence motifs and 3D modeling to determine structure and functional regions of proteins.

Homology modeling has become an essential tool for studying proteins that are targets for medical drug design. This paper describes the approach we developed that combines sequence decomposition techniques with distance geometry algorithms for homology modeling to determine functionally important regions of proteins. We show here the application of these techniques to targets of medical interest chosen from those included in the CASP5 (Critical Assessment of Techniques for Protein Structure Prediction) competition, including the dihydroneopterin aldolase from Mycobacterium tuberculosis, RNase III of Thermobacteria maritima, and the NO-transporter nitrophorin from saliva of the bedbug Cimex lectularius. Physical chemical property (PCP) motifs, identified in aligned sequences with our MASIA program, can be used to select among different alignments returned by fold recognition servers. They can also be used to suggest functions for hypothetical proteins, as we illustrate for target T188. Once a suitable alignment has been made with the template, our modeling suite MPACK generates a series of possible models. The models can then be selected according to their match in areas known to be conserved in protein families. Alignments based on motifs can improve the structural matching of residues in the active site. The quality of the local structure of our 3D models near active sites or epitopes makes them useful aids for drug and vaccine design. Further, the PCP motif approach, when combined with a structural filter, can be a potent way to detect areas involved in activity and to suggest function for novel genome sequences.

Aldehyde-Lyases↗

PubMed2003

Detecting potential IgE-reactive sites on food proteins using a sequence and structure database, SDAP-food.

The high incidence of food allergies, including oral allergy syndrome, represent major considerations when introducing new crops and foods. A new structural database of allergenic proteins, SDAP-Food, http://fermi.utmb.edu/SDAP/, has been developed to aid in predicting the IgE-binding potential of novel food proteins and cross-reactivities among known allergens. The site is designed to facilitate the first steps of a decision tree approach to determine the allergenicity of a given protein, based on the sequence and structural similarity to known allergens and their IgE binding sites. Immunological tests can then be used to confirm the predictions. A hierarchical procedure for identifying potential allergens, using a physical property-based sequence similarity index, has been designed to identify regions that resemble known IgE binding sites. As an example, SDAP tools were used to find food allergen sequences similar to an IgE binding site of the Jun a 3 allergen from mountain cedar pollen. The SDAP sequence similarity search matched the Jun a 3 epitope to regions in several food allergens, including cherry (Pru av 2), apple (Mal d 2) and pepper (Cap a 1), which are, like Jun a 3, members of the plant pathogenesis-related (PR-5) protein family. Homology modeling, using our EXDIS/DIAMOD/FANTOM program suite, indicated a similar surface location and structure for the potential epitope region on all of these allergens. The quantitative approach presented here can be used as part of a screening process for potential allergenicity of recombinant food products.

Allergens↗

PubMed2003

SDAP: database and computational tools for allergenic proteins.

SDAP (Structural Database of Allergenic Proteins) is a web server that provides rapid, cross-referenced access to the sequences, structures and IgE epitopes of allergenic proteins. The SDAP core is a series of CGI scripts that process the user queries, interrogate the database, perform various computations related to protein allergenic determinants and prepare the output HTML pages. The database component of SDAP contains information about the allergen name, source, sequence, structure, IgE epitopes and literature references and easy links to the major protein (PDB, SWISS-PROT/TrEMBL, PIR-ALN, NCBI Taxonomy Browser) and literature (PubMed, MEDLINE) on-line servers. The computational component in SDAP uses an original algorithm based on conserved properties of amino acid side chains to identify regions of known allergens similar to user-supplied peptides or selected from the SDAP database of IgE epitopes. This and other bioinformatics tools can be used to rapidly determine potential cross-reactivities between allergens and to screen novel proteins for the presence of IgE epitopes they may share with known allergens. SDAP is available via the World Wide Web at http://fermi.utmb.edu/SDAP/.

Allergens↗

PubMed2002

Data mining of sequences and 3D structures of allergenic proteins.

MOTIVATION: Many sequences, and in some cases structures, of proteins that induce an allergic response in atopic individuals have been determined in recent years. This data indicates that allergens, regardless of source, fall into discreet protein families. Similarities in the sequence may explain clinically observed cross-reactivities between different biological triggers. However, previously available allergy databases group allergens according to their biological sources, or observed clinical cross-reactivities, without providing data about the proteins. A computer-aided data mining system is needed to compare the sequential and structural details of known allergens. This information will aid in predicting allergenic cross-responses and eventually in determining possible common characteristics of IgE recognition. RESULTS: The new web-based Structural Database of Allergenic Proteins (SDAP) permits the user to quickly compare the sequence and structure of allergenic proteins. Data from literature sources and previously existing lists of allergens are combined in a MySQL interactive database with a wide selection of bioinformatics applications. SDAP can be used to rapidly determine the relationship between allergens and to screen novel proteins for the presence of IgE or T-cell epitopes they may share with known allergens. Further, our novel similarity search method, based on five dimensional descriptors of amino acid properties, can be used to scan the SDAP entries with a peptide sequence. For example, when a known IgE binding epitope from shrimp tropomyosin was used as a query, the method rapidly identified a similar sequence in known shellfish and insect allergens. This prediction of cross-reactivity between allergens is consistent with clinical observations. AVAILABILITY: SDAP is available on the web at http://fermi.utmb.edu/SDAP/index.html

Allergens↗

PubMedDate not supplied

Computing wiener-type indices for virtual combinatorial libraries generated from heteroatom-containing building blocks.

The expensive and time-consuming process of drug lead discovery is significantly accelerated by efficiently screening molecular libraries with a high structural diversity and selecting subsets of molecules according to their similarity toward specific collections of active compounds. To characterize the molecular similarity/diversity or to quantify the drug-like character of compounds the process of screening virtual and synthetic combinatorial libraries uses various classes of structural descriptors, such as structure keys, fingerprints, graph invariants, and various topological indices computed from atomic connectivities or graph distances. In this paper we present efficient algorithms for the computation of several distance-based topological indices of a molecular graph from the distance invariants of its subgraphs. The procedures utilize vertex- and edge-weighted molecular graphs representing organic compounds containing heteroatoms and multiple bonds. These equations offer an effective way to compute for weighted molecular graphs the Wiener index, even/odd Wiener index, and resistance-distance index. The proposed algorithms are especially efficient in computing distance-based structural descriptors in combinatorial libraries without actually generating the compounds, because only distance-based indices of the building blocks are needed to generate the topological indices of any compound assembled from the building blocks.

Journal Article↗

PubMedDate not supplied

Posetic quantitative superstructure/activity relationships (QSSARs) for chlorobenzenes.

As a result of the widespread industrial use of polychlorinated hydrocarbons, they have accumulated in nearly all types of environmental compartments, especially in aquatic systems. Particularly, chloroaromatics are among the most undesirable industrial effluents because of their persistence and toxicity. To predict chlorobenzene (CB) toxicities, we make use of a novel scheme that looks beyond simple molecular structure to the manner in which such a structure embeds in an overall reaction network. Thence, a resultant modeling gives a quantitative superstructure/activity relationship (QSSAR) with the (chloro-substitution) reaction network viewed mathematically as a partially ordered set (or poset). Different numerical fittings to the overall poset lead to different QSSAR models, of which we investigate three: average poset, cluster expansion, and splinoid poset QSSAR models for the CBs' toxicities against various species (Poecilia reticulata,Pimephales promelas, Daphnia magna, Rana japonica, etc). Excellent results are obtained for all QSSAR toxicity models. On the basis of the poset reaction diagram, all three of these QSSAR models reflect, in distinct ways, the topology of the network that describes the interconversion of chemical species. Although in the majority of investigated datasets all poset QSSAR models give very good predictions, in some cases, they complement each other. These differences show that more reliable predictions can be obtained by using a consensus prediction that combines data from the three posetic models.

Chlorobenzenes↗