Profile matching method

For each fold one searches for the best alignment of the target sequence that would be compatible with the fold the core should comprise hydrophobic residues and polar residues should be on the outside, predicted helical and strand regions should be aligned to corresponding secondary structure elements in the fold, and so on. In order to match a sequence alignment to a fold, Eisenberg developed a rapid method called the 3D profile method. The environment of each residue position in the known 3D structure is characterized on the basis of three properties (1) the area of the side chain that is buried by other protein atoms, (2) the fraction of side chain area that is covered by polar atoms, and (3) the secondary stmcture, which is classified in three states helix, sheet, and coil. The residue positions are rather arbitrarily divided into six classes by properties 1 and 2, which in combination with property 3 yields 18 environmental classes. This classification of environments enables a protein structure to be coded by a sequence in an 18-letter alphabet, in which each letter represents the environmental class of a residue position. 

One of the first applications of the HPLC method was the investigation of differences in toxin profiles between shellfish species from various localities ( ). It became apparent immediately that there were vast differences in these toxin profiles even among shellfish from the same beach. There were subtle differences between the various shellfish species, and butter clams had a completely different suite of toxins than the other clams and mussels. It was presumed that all of the shellfish fed on the same dinoflagellate population, so there must have been other factors influencing toxin profiles such as differences in toxin uptake, release, or metabolism. These presumptions were strengthened when toxin profiles in the littleneck clam (Prototheca Staminea) were examined. It was found that, in this species, none of the toxin peaks in the HPLC chromatogram had retention times that matched the normal PSP toxins. It was evident that some alteration in toxin structure had occurred that was unique in this particular shellfish species. 

Use of correlation coefficients between amino acid profile data in order to efficiently quantify the degree of similarity between unknown samples and reference proteinaceous materials [7]. The method processes quantitative amino acid concentrations either as per cent relative content (pg g 1 %) or per cent molar content (mol %). A match of more than 0.9 is necessary to ensure a reliable identification of the proteinaceous binder. Table 9.3 reports results obtained on samples coming from the Tintori Collection analysed with two different analytical procedures showing a good correlation with the egg-based binder [96]. 

Prosite is perhaps the best known of the domain databases (Hofmann et al., 1999). The Prosite database is a good source of high quality annotation for protein domain families. Prosite documentation includes a section on the functional meaning of a match to the entry and a list of example members of the family. Prosite documentation also includes literature references and cross links to other databases such as the PDB collection of protein structures (Bernstein et al., 1977). For each Prosite document, there is a Prosite pattern, profile, or both to detect the domain family. The profiles are the most sensitive detection method in Prosite. The Prosite profiles provide Zscores for matches allowing statistical evaluation of the match to a new protein. Profiles are now available for many of the common protein domains. Prosite profiles use the generalized profile software (Bucher et al., 1996). 

The final round scores from iterative profile methods do not reflect the real significance of the match to the query sequence. The significance says how likely the protein segment matches to the profile constructed in the previous round. For example, if a false-positive match with an E... 

All methods that build a profile have a limitation in the amount of information they can model. For large divergent protein families, it can become impossible to construct a single profile-HMM capable of matching all known examples. This is particularly true for shorter motifs that have less information. In these cases sequences that are in the initial alignment used to construct the profile are not matched by the profile in some cases the alignment can be altered to improve this, but this is not always the case. In large families it may be necessary to build multiple profiles for the family to detect all the members of the family. 

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