Three-Dimensional Structure Search Methods

The first task was the aeation of large 3D chemical structure databases. By devising so-called fast Automatic 3D model builder, software such as the CORINA [27, 28] and CONCORD [29, 30] programs resulted in a boom in 3D database development (see Section 2.9 in this book and Chapter II, Section 7.1 in the Handbook). A subsequent step was the development of fast 

Full structure search can be developed by using similar approaches to those employed in the case of 2D structure search. Thus, some topological indices can be modified in such a way that they include geometrical information. For example, the global index given by Eq. (4) can be modified to Eq. (11), where are real interatomic distances. 

The RDF code discussed in Chapter VIll, Sections 2 and 3 of the Handbook) also can be used to this end. 

3D similarity search methods are quite well developed. Thus, methods which attempt to find overlapping parts (atoms and functional groups) of the molecular moieties studied were reported first [31]. As discussed above for the case of 2D searching, these methods are of combinatorial complexity. To reduce this complexity some field-based methods have been introduced. In this case, the overlap of the fields of two structures is considered as a similarity measure. 

3D substructure search is usually known as pharmacophore searching in QSAR. Generally speaking, there are two major approaches to it topological and chemical function queries. These two techniques are based on a slighfly different philosophy and usually provide different results [31]. 

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Stereochemistry Representation and Manipulation Structural Similarity Measures for Database Searching Structure and Substructure Searching Structure Databases Structure Representation Three-dimensional Structure Generation Automation Three-dimensional Structure Searching Topological Indices Topological Methods in Chemical Structure and Bonding. 

The development of RNA three-dimensional structure determination methods requires three essential components. First is a computer representation, or a data structure, of RNA three-dimensional structural knowledge and data. Second is an RNA conformational search space that includes three-dimensional structures consistent with the computer representation. The implementation of a conformational search space includes the following tasks (i) the creation of a set of operators to manipulate the RNA three-dimensional structures (ii) the definition of a metric to evaluate RNA three-dimensional structures (iii) the design of an efficient method for applying the chosen metric and (iv) the design of an efficient method for generating the next three-dimensional structure to consider. Third is an inference engine which searches the conformational search space for three-dimensional structures that fit input descriptions. 

I J, J C Cole, J P M Lommerse, R S Rowland, R Taylor and M L Verdonk 1997. Isostar A Libraij )f Information about Nonbonded Interactions. Journal of Computer-Aided Molecular Design 11 525-531. g G, W C Guida and W C Still 1989. An Internal Coordinate Monte Carlo Method for Searching lonformational Space. Journal of the American Chemical Scociety 111 4379-4386. leld C and A J Collins 1980. Introduction to Multivariate Analysis. London, Chapman Hall, ig C-W, R M Cooke, A E I Proudfoot and T N C Wells 1995. The Three-dimensional Structure of 1 ANTES. Biochemistry 34 9307-9314. 

Molecular databases and the associated data banks require the development of a conceptual structure for the information stored about the molecules, descriptive language representing the data, and methods for analysis enabling molecular modeling, similarity searches, classification, visualization, or other uses of the database.320 Currendy, the Protein Data Bank (PDB http 7www.rcsb.org/pdb/) is one of the best known examples of a molecular database. The PDB is a worldwide archive of three-dimensional structural data of biological macromolecules.321 The PDB is a common accentor to many structural databases.322 The success of... 

For these reasons, it is important to focus on the most divergent set of superfamily members that can be identified. Although a variety of new methods have recently been developed for identification of distantly related protein sequences [see, for example, Psi-Blast (Altschul et al, 1997), methods based on Hidden Markov Models such as SAMT98 (Kar-plus et al., 1998), the Intermediate Sequence Search algorithm of Park et al., (Park et al., 1997), or the simple congruence method, Shotgun (Pegg and Babbitt, 1999)], confirmation of these relationships can be technically difficult. In some cases, three-dimensional structural information or experimental structure-function analysis will be required to pro-... 

Tbrner, D.B., Willett, R, Ferguson, A.M. and Heritage, T.W. (1995). Similarity Searching in Files of Three-Dimensional Structures Evaluation of Similarity Coefficients and Standardization Methods for Field-Based Similarity Searching. SAR QSAR EnvironRes., 3,101-130. 

Pharmacophorlc Pattern Searching - Once a pharmacophorlc pattern has been proposed. It would be useful to look for that pattern In other drug molecules - In order to test and refine the pattern to Identify available compounds which should be tested for the bloactlvlty of Interest and to provide feedback to chemists synthesizing new potential drugs. Such a pattern searching procedure requires a three-dimensional structure for the molecules to be examined, and a method of discovering the presence of the pattern In those molecules. 

Heritage, TW (1995) Similarity searching in files of three-dimensional structures evaluation of similarity coefficients and standardization methods for field-based similarity searching. SAR el QSAR Environ. Res., 3, 101-130. 

Although a theoretical method to predict the three-dimensional structure of a protein from its sequence alone is not in sight yet, researchers have uncovered a multitude of connections between the primary sequence on one hand and various functional features of proteins on the other hand. Among the success stories are the recognition of transmembrane proteins or the classification of proteins into classes of similar function based on sequence similarity. The latter achievement uses the observation that proteins that are similar in sequence are likely to share similar functional features. This is also giving rise to the enormous utility of similarity searches in sequence data bases. This Chapter will deal with the kinds of analysis and predictions based on primary sequence alone and the algorithms used in this field. 

While atomic coordinates form the fundamental structure of a molecule, many methods prefer to represent a three-dimensional structure as a surface or a shape. Of course, these are ultimately computed from the atomic coordinates and perhaps atomic partial charges. It may be possible to represent these molecular surfaces or shapes as an array of three-dimensional coordinates. These could be stored as a column in the database analogous to the array of atomic coordinates. It might be necessary to create another data type, perhaps a composite data type, to store molecular surfaces or shapes. Once these representations are stored, they can be used in new SQL functions to assist in searching based on molecular surface or shape. 

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