Query pattern

The refinement procedure utilises the fact that if some query node Q(X) has another node Q(fV) at some specific distance ) ( and/or angle), and if some database node D(Z) matches with Q(W), then there must also be some node D(Y) at the appropriate distance(s) from D(Z) which matches with Q(X) this is a necessary, but not sufficient, condition for a subgraph isomorphism to be present (except in the limiting case of all the query nodes having been matched, when the condition is both necessary and sufficient). The refinement procedure is called before each possible assignment of a database node to a query node and the matched substructure is increased by one node if, and only if, the condition holds for all nodes W, X, Y and Z. The basic algorithm terminates once a match has been detected or until a mismatch has been confirmed [70] it is easy to extend the algorithm to enable the detection of all matches between a query pattern and a database structure, as is required for applications such as those discussed here. 

Thus, in our current implementation of the method, we represent the position of a sidechain by the two pseudo-atoms, and the relative orientations of pairs of sidechains by the distances between them. Specifically, for each pair of sidechains in a query pattern (or in a database structure), five distances are calculated, these being the SS, SE, EE, ES and MM distances as illustrated schematically in Fig. 3, which shows a pattern of three residues and the associated inter-atomic distances. Although these five distances (or a user-defined subset of them) provide a very simple way of defining the orientation of a pair of sidechains, our investigations (as detailed below) indicate that the distances are extremely effective in detecting similarities (both known and previously unknown) between sidechain arrangements. 

Above we have described the use of graph theoretical algorithms to identify all occurrences of a user-defined pattern of residues in a database of protein structures. The residues in a protein or a query pattern are represented in a highly simplified form that consists of two pseudo-atoms and the relative orientations of pairs of sidechains are defined by the distances between pairs of these pseudo-atoms. Despite the simplicity of the representation, our tests with a variety of patterns demonstrate the usefulness of the methodology. 

Low-resolution descriptors are less appropriate for algorithms relying on the comparison of numerical values. This applies, for instance, to pattern-matching algorithms, where the query pattern contains real distances, whereas the descriptor contains interpolated maxima. 

The fact that a profile or HMM can pick out new sequences also related to the given family suggests that these should be used to update the profile or HMM used as search pattern. This idea leads to iterative search algorithms where the database is searched repeatedly, each time updating the query pattern with some or all of the newly identified sequences. Psi-Blast [101] is a very successful implementation of this idea. It starts with a single... 

Type the query pattern in the pattern field of the FUZZNUC box (see Note 12). 

Improved performance by maintaining the reference data in a form optimized for local usage and query patterns... 

If all these tests are satisfied, the conformations of the passing molecules are regenerated, with the query pattern acting as a further restraint to speed up the calculation. Each low energy conformer that conforms to the pharmacophore is then superimposed on the query using a least-squares fit and is written out to a results database. The relative speeds of each part of the search process are shown in Table 3. 

For this query motif, the ranking search is redundant since the normal POSSUM search using tolerances of 35° and 50% mid-point distances retrieves just the three p-barrels that were identified at the top of the ranked search. This is because the query pattern is very distinctive. 

The first two structures at the top of the ranking are hits from 4LDH. The 4LDH hit placed first in the ranking corresponds exactly to the query pattern, containing as it does the set of helices and strands from which the query pattern is derived. The 4LDH hit ranked second is the same as the first hit, except that an extension of helix X is found instead of X itself. 

Dietance Ranfe/Angetroms TNC2 query pattern ---- ICLN structure... 

SPARQL works on the principle of matching query patterns over an RDF data graph. Listing 3.2 shows a basic query that remms aU the suppliers of mo Engl. 

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