Bioinformatics technologies

Solving modern biological problems requires advanced computational methods. This book describes the application of well-established concepts and techniques from areas like data mining, machine learning, database technologies, and visualization techniques to problems like protein data analysis, genome analysis, and sequence databases. Chen has collected contributions from leading researchers in each area.

Using bioinformatics techniques for gene identification in drug discovery and development

As more and more evidence has become available, the link between gene and emergent disease has been made including cancer, heart disease and parkinsonism. Analyzing the diseases and designing drugs with respect to the gene and protein level obviously help to find the underlying causes of the diseases, and to improve their rate of cure. The development of modern molecular biology, biochemistry, data collection and analysis techniques provides the scientists with a large amount of gene data. To draw a link between genes and their relation to disease outcomes and drug discovery is a big challenge: How to analyze large datasets and extract useful knowledge? Combining bioinformatics with drug discovery is a promising method to tackle this issue. Most techniques of bioinformatics are used in the first two phases of drug discovery to extract interesting information and find important genes and/or proteins for speeding the process of drug discovery, enhancing the accuracy of analysis and reducing the cost. Gene identification is a very fundamental and important technique among them. In this paper, we have reviewed gene identification algorithms and discussed their usage, relationships and challenges in drug discovery and development.

Identifying targets for drug discovery using bioinformatics

Drug discovery is the process of discovering and designing drugs, which includes target identification, target validation, lead identification, lead optimization and introduction of the new drugs to the public. This process is very important, involving analyzing the causes of the diseases and finding ways to tackle them. Objective: The problems we must face include: i) that this process is so long and expensive that it might cost millions of dollars and take a dozen years; and ii) the accuracy of identification of targets is not good enough, which in turn delays the process. Introducing bioinformatics into the drug discovery process could contribute much to it. Bioinformatics is a booming subject combining biology with computer science. It can explore the causes of diseases at the molecular level, explain the phenomena of the diseases from the angle of the gene and make use of computer techniques, such as data mining, machine learning and so on, to decrease the scope of analysis and enhance the accuracy of the results so as to reduce the cost and time. Methods: Here we describe recent studies about how to apply bioinformatics techniques in the four phases of drug discovery, how these techniques improve the drug discovery process and some possible difficulties that should be dealt with. Results: We conclude that combining bioinformatics with drug discovery is a very promising method although it faces many problems currently.

Bioinformatics based structural characterization of glucose dehydrogenase (gdh) gene and growth promoting activity of Leclercia sp. QAU-66

Glucose dehydrogenase (GDH; EC 1.1. 5.2) is the member of quinoproteins group that use the redox cofactor pyrroloquinoline quinoine, calcium ions and glucose as substrate for its activity. In present study, Leclercia sp. QAU-66, isolated from rhizosphere of Vigna mungo, was characterized for phosphate solubilization and the role of GDH in plant growth promotion of Phaseolus vulgaris. The strain QAU-66 had ability to solubilize phosphorus and significantly (p ≤ 0.05) promoted the shoot and root lengths of Phaseolus vulgaris. The structural determination of GDH protein was carried out using bioinformatics tools like Pfam, InterProScan, I-TASSER and COFACTOR. These tools predicted the structural based functional homology of pyrroloquinoline quinone domains in GDH. GDH of Leclercia sp. QAU-66 is one of the main factor that involved in plant growth promotion and provides a solid background for further research in plant growth promoting activities.