Biomedical subjects

M Karthikeyan

Publications and source records attributed to M Karthikeyan.

5 recordsLinked to original sources

PubMed2005

Induction of resistance in host against the infection of leaf blight pathogen (Alternaria palandui) in onion (Allium cepa var aggregatum).

The Pseudomonas fluorescens isolate Pfl was found to inhibit the growth of pathogen Alternaria palandui, in vitro. In the present study, foliar application of a talc-based formulation of Pfl significantly reduced the incidence of leaf blight of onion, caused by A. palandui. Induction of defense-related proteins viz., chitinase, beta-1,3 glucanase, peroxidase (PO) and polyphenol oxidase (PPO) by application of Pfl, was studied against A. palandui infection in resistant (IHR 56) and susceptible (MDUI) onion cultivars. Chitinase in both cultivars, with or without challenge-inoculation of A. palandui revealed changes in the isoform pattern. The Native-PAGE of PO showed induction of PO2 isoform in both the cultivars, in response to inoculation of pathogen. Isoform analysis of PPO also exhibited induction in the Pfl-treated plants challenged with pathogen. Similarly, the activity of beta-1,3-glucanase was greatly induced in Pfl-treated plants, challenged with pathogen as compared to controls. Thus, the P. fluorescens-treated plants showed significant increase in the levels of the defense enzymes, in comparison to the plants challenged with the pathogen.

Catechol Oxidase↗

PubMed2002

Effect of water washing of shark (Scoliodon laticaudus) meat on the properties of proteins with special reference to gelation.

The effect of water washing of shark meat on the properties of proteins has been investigated. The contents of low-molecular-weight proteins and urea were reduced significantly with three washing cycles. The gel forming ability showed marked improvement with the number of washing cycles. The dynamic viscoelastic behavior of washed and unwashed meat revealed a structure build-up reaction that was more pronounced in the washed meat. The concentration of myosin heavy chain of washed meat increased as revealed by Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). Addition of urea at biological concentration (approximately 250 mM) to the washed meat reduced the gel forming ability significantly as compared to unwashed meat. The emulsion capacity showed an increase with the number of washing cycles.

Animals↗

PubMedDate not supplied

Encoding and decoding graphical chemical structures as two-dimensional (PDF417) barcodes.

A wide range of molecular representations exist today, ranging from human-readable structural diagrams over line notations such as Wiswesser Line Notation (WLN) and SMILES to several dozen computer-readable file formats. Still, to encode molecular structures in a computer-readable way for inputting structures in computer systems those formats are not the method of choice since they are not easily and faultlessly readable via optical recognition. In the present study a two-dimensional (PDF417) barcode representation of molecular structures in SMILES format is explored that enables the user to read and input molecular structures into computer systems in a fully automated fashion. A Lempel-Ziv-Welch (LZW) based compressed version of SMILES is suggested for cases where the size of the structure exceeds the storage capacity of PDF417 barcodes. Alternatively, the compact ACS format may be employed as a structural representation. The input via barcodes is fast, practically error free due to the 2D barcodes used which employ error correction and fully automatic. A Web application interface is developed which is able to interpret these barcodes and export them as optimized 3D chemical structures. Applications of this representation range from keeping automated storage systems to Web-based tracking systems of molecular samples. The National Chemical Laboratory, Pune, employs 2D barcode encoded structures for in-house repository management, where barcodes can also be used for querying the database for similar or substructures of the query structure.

Journal Article↗

PubMedDate not supplied

General melting point prediction based on a diverse compound data set and artificial neural networks.

We report the development of a robust and general model for the prediction of melting points. It is based on a diverse data set of 4173 compounds and employs a large number of 2D and 3D descriptors to capture molecular physicochemical and other graph-based properties. Dimensionality reduction is performed by principal component analysis, while a fully connected feed-forward back-propagation artificial neural network is employed for model generation. The melting point is a fundamental physicochemical property of a molecule that is controlled by both single-molecule properties and intermolecular interactions due to packing in the solid state. Thus, it is difficult to predict, and previously only melting point models for clearly defined and smaller compound sets have been developed. Here we derive the first general model that covers a comparatively large and relevant part of organic chemical space. The final model is based on 2D descriptors, which are found to contain more relevant information than the 3D descriptors calculated. Internal random validation of the model achieves a correlation coefficient of R(2) = 0.661 with an average absolute error of 37.6 degrees C. The model is internally consistent with a correlation coefficient of the test set of Q(2) = 0.658 (average absolute error 38.2 degrees C) and a correlation coefficient of the internal validation set of Q(2) = 0.645 (average absolute error 39.8 degrees C). Additional validation was performed on an external drug data set consisting of 277 compounds. On this external data set a correlation coefficient of Q(2) = 0.662 (average absolute error 32.6 degrees C) was achieved, showing ability of the model to generalize. Compared to an earlier model for the prediction of melting points of druglike compounds our model exhibits slightly improved performance, despite the much larger chemical space covered. The remaining model error is due to molecular properties that are not captured using single-molecule based descriptors, namely both inter- and intramolecular interactions and crystal packing, for which examples of and reasons for outliers are given.

Journal Article↗

PubMedDate not supplied

Harvesting chemical information from the Internet using a distributed approach: ChemXtreme.

The Internet is a comprehensive resource of chemical information which is at the same time largely unstructured. It provides a wealth of scientific information such as experimental data and requires a suitable automated data mining and analysis tool for its meaningful exploration. The Java based software presented here, ChemXtreme, is developed for harvesting chemical information from the Internet employing the Google API in combination with a distributed client/server text analysis architecture based on JavaRMI. It represents the first and until now the only toolkit for automated structured data retrieval from the Internet which is itself open source. ChemXtreme employs the "search the search engine" strategy, where the URLs returned from the search engine are analyzed further via textual pattern analysis. This process resembles the manual analysis of the hit list, where relevant data are captured and, by means of human intervention, are mined into a format suitable for further analysis. ChemXtreme on the other hand transforms chemical information automatically into a structured format suitable for storage in databases and further analysis and also provides links to the original information source. The query data retrieved from the search engine by the server is encoded, encrypted, and compressed and then sent to all the participating active clients in the network for parsing. Relevant information identified by the clients on the retrieved Web sites is sent back to the server, verified, and added to the database for data mining and further analysis. The distributed further analysis of URLs in a client/server architecture scales very favorably, thus producing only minimal overhead.

Chemistry↗