Profile search

Building sequence profiles or Hidden Markov Models to perform more sensitive homology searches. A sequence profile contains information about the variability of every sequence position, improving structure prediction methods (secondary structure prediction). Sequence profile searches have become readily available through the introduction of PsiBLAST [4]... 

Altschul, S. F.andKoonin, E. V.(1998), Iterated profile searches with PSI-LAST - a tool for discovery in protein databases, Trends Biochem. Set ( Computer Corner ), 23, 444-446. 

ExPASy Proteomics tools (http //expasy.org/tools/), tools and online programs for protein identification and characterization, similarity searches, pattern and profile searches, posttranslational modification prediction, topology prediction, primary structure analysis, or secondary and tertiary structure prediction. 

Another computational approach for detecting /1-solenoid sequences is implemented in a program called BETAWRAP (Bradley et al., 2001). This approach aims to identify /1-solenoid sequences by using hydrophobic-residue sequence patterns of strand-turn-strand regions that were learned from non-/l-solenoid structures. This method also takes into consideration the repetitive character of these patterns in /1-solenoids. Unlike the sequence profile approaches, BETAWRAP can make ab initio predictions of /1-solenoid domains. However, it is less sensitive than the profile search and, sometimes, cannot distinguish /1-solenoids from other solenoids (A. V. K, unpublished observation) such as, for example, LRR proteins (Kobe and Deisenhofer, 1994 Kobe and Kajava, 2001). The latest modification of BETAWRAP algorithm, which is called BETAWRAPPRO (McDonnell et al., 2006), employs additional data provided by sequence profiles and this improves the results of /1-solenoid predictions. 

Searching through Databases Applications of network BLAST server, 266, 131 Entrez molecular biology database and retrieval system, 266, 141 applying motif and profile searches,... 

Fujibuchi, W., Kiseleva, L., Taniguchi, T., Harada, H. and Horton, P. (2007) CellMon-tage Similar Expression Profile Search Server, Bioinformatics 2Z, 3103-3104. 

Figure 11,4. ExPASy Proteomic tools. ExPASy server provides various tools for proteomic analysis which can be accessed from ExPASy Proteomic tools. These tools (locals or hyperlinks) include Protein identification and characterization, Translation from DNA sequences to protein sequences. Similarity searches, Pattern and profile searches, Post-translational modification prediction, Primary structure analysis, Secondary structure prediction, Tertiary structure inference, Transmembrane region detection, and Sequence alignment.

One of the interesting questions is why this approach has not been reported to have been used to successfully identify new members of other families of cytokines, such as the four helix bundle family which includes IL-2, IL-4, IL-5, etc. One problem for these families is that the defining features are not so apparent (for example the positions of the disulfide bonds are not always conserved). Also, the majority of the members of these cytokine families are only finally confirmed once their three-dimensional structures have been solved. It may be that when more sophisticated versions of such techniques as Profile searching can be used will this then open up new cytokines for more classical families. Such Profiles would have to include amino acid similarities, as well as secondary structure propensity. Even so, the current rate of success is not expected to be as high as for the chemokine area (see, for example, ref. 15). 

Algorithmically, profile searching simply uses the dynamic programming alignment algorithm for aligning a sequence to a profile on each sequence... 

Other successful structure prediction methods based on sequence alone are FASTA [15, 18], hidden Markov models (HMMs) [25, 26, 28, 179], intermediate sequence search [182], and iterative profile search [29]. Sequence analysis methods are discussed in detail in Chapter 2. The results of such methods in the CASP experiment are described in [157, 183, 184],... 

It is clear that the databases described above do not cover all the aspects of interest for proteomics researchers. There are databases that use the sequence databases to perform calculation and analysis, such as sequence clustering, phylogeny, or profile searching, and thus create added-value databases. Other databases report results from functional studies and mutational experiments, or from 3-D structure determination, or describe metabolic pathways. Although it is impossible to list them all here, it is of interest to know that they exist. Some of them are permanently updated, some of them have only a short existence, some of them are not publicly available. The existence of databases in proteomics is following a dynamic and continuously developing model, and simply reflects the dramatic evolution the field of proteomics is witnessing. 

Finally, if no matching structures can be found for your target sequence using these methods (sequence or profile search), there is another group of methods, namely fold recognition methods, such as threading. 

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