Bioinformatics using

Bioinformatics uses computers to create and maintain large electronic databases on genomes, protein sequences, and proteomes. With the help of protein prediction software, the computer analysis of genome sequences is producing thousands of new proteins of unknown structure and function. These proteins are called hypothetical proteins because they are predicted from the gene sequence. To know if they really exist would require that they be isolated, purified, and subjected to X-ray crystallography or... 

Distribution of data Bioinformatics uses over 500 data resources and analysis tools found all over the Internet [5]. They often have Web interfaces through which biologists enter data for analysis, cut and paste results to new Web resources, or explore results through rich annotations with cross-links [21... 

Bioinformatics is a relatively new discipline that is concerned with the collection, organisatic and analysis of biological data. It is beyond our scope to provide a comprehensive overvie of this discipline a few textbooks and reviews that serve this purpose are now available (s the suggestions for further reading). However, we will discuss some of the main rnethoc that are particularly useful when trying to predict the three-dimensional structure and fum tion of a protein. To help with this. Appendix 10.1 contains a limited selection of some of tf common abbreviations and acronyms used in bioinformatics and Appendix 10.2 lists sorr of the most widely used databases and other resources. 

Appendix 10,1 Some Common Abbreviations and Acronyrris Used in Bioinformatics... 

It is well known that the resources available on the Internet are in constant flux, with new sites appearing on a daily basis and established sites disappearing almost as frequently. This also holds true for the dedicated tools used in biochemical and biophysical studies. New tools are constantly becoming available, and established tools, obsolete. Such rapid change makes it difficult to stay current with the state-of-the-art technologies in the areas of bioinformatics and computational biochemistry and biophysics. 

This branch of bioinformatics is concerned with computational approaches to predict and analyse the spatial structure of proteins and nucleic acids. Whereas in many cases the primary sequence uniquely specifies the 3D structure, the specific rules are not well understood, and the protein folding problem remains largely unsolved. Some aspects of protein structure can already be predicted from amino acid content. Secondary structure can be deduced from the primary sequence with statistics or neural networks. When using a multiple sequence alignment, secondary structure can be predicted with an accuracy above 70%. 

A Hidden Markov Model (HMM) is a general probabilistic model for sequences of symbols. In a Markov chain, the probability of each symbol depends only on the preceding one. Hidden Markov models are widely used in bioinformatics, most notably to replace sequence profile in the calculation of sequence alignments. 

The aim of the fust dimension breadth is to reveal sequence-function relationships by comparing protein sequences by sequence similarity. Simple bioinformatic algorithms can be used to compare a pair of related proteins or for sequence similarity searches e.g., BLAST (Basic Local Alignment Search Tool). Improved algorithms allow multiple alignments of larger number of proteins and extraction of consensus sequence pattern and sequence profiles or structural templates, which can be related to some functions, see e.g., under http //www. expasy.ch/tools/ similarity. 

In general the relevance of predictions of structure-function relationships based on molecular modeling and structural bioinformatics are threefold. First they can be used to answer the question of which partners (proteins) could interact. Second, predictions generate new hypotheses about binding site, about molecular mechanisms of activation and interaction between two partners, and can lead to new ideas for pharmacological intervention. The third aim is to use the predictions for structure-based drug design. 

Although manual extraction of information from herbal texts is straightforward (Fig. 4.3A), the work is labor intense and requires many areas of expertise (Fig. 4.3B). Historians must provide context for the language. Botanists are necessary to update the names and correctly identify the plants discussed. Physicians and biomedical scientists are required to extrapolate the potential pharmacological function of the plant compounds used to treat a certain disorder in the text. Luckily, the use of bioinformatics to extract this information can be more efficient than manual extraction [7]. 

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