Knowledge-based structural

Computational efficiency. The efficiency of inference is enhanced by program and knowledge-base structure and by machine speed. Also, heuristic procedures, as used by experts, can augment the deductive procedures of conventional inference. 

The definition of the higher grade Knowledge Base structure is the primary goal during the Knowledge Elicitation phase. 

The definition of this model will be used to design the best higher grade Knowledge Base structure. If this structure matchs with a commercial expert system developing tool characteristics, it can be selected and used allowing a minor effort to build the final system. 

The knowledge based structural prediction (Blundell et al, 1987) depends on analogies between a biomacromolecule of known sequence and other biomacromolecules of the same class with known 3D structure at all levels in the hierarchy of biomacromolecular organization. In the numerically based statistical methods, the structural rules and parameters (conformational propensities) for each residue are extracted by statistical analyses of the structural database and used to predict the structure along the sequence of the macromolecule. Examples of the statistical methods that are commonly applied to predict secondary structure and folding preference of proteins will be illustrated. 

A final research challenge is to address the black-box phenomenon of KBE development and use [9] supporting the integration of code and documentation generation [5] as well as explicitly linking knowledge base structure and meaningful content with the KBE application elements and code. 

The classical computer tomography (CT), including the medical one, has already been demonstrated its efficiency in many practical applications. At the same time, the request of the all-round survey of the object, which is usually unattainable, makes it important to find alternative approaches with less rigid restrictions to the number of projections and accessible views for observation. In the last time, it was understood that one effective way to withstand the extreme lack of data is to introduce a priori knowledge based upon classical inverse theory (including Maximum Entropy Method (MEM)) of the solution of ill-posed problems [1-6]. As shown in [6] for objects with binary structure, the necessary number of projections to get the quality of image restoration compared to that of CT using multistep reconstruction (MSR) method did not exceed seven and eould be reduced even further. 

Correlations between structure and mass spectra were established on the basis of multivariate analysis of the spectra, database searching, or the development of knowledge-based systems, some including explicit management of chemical reactions. 

The most recent version of EROS has a clearcut separation of the system proper, which performs all the manipulations on chemical structures and reactions, from the knowledge base, which defines the scope of it.s application (Figure 10.3-7). 

Ithough knowledge-based potentials are most popular, it is also possible to use other types potential function. Some of these are more firmly rooted in the fundamental physics of iteratomic interactions whereas others do not necessarily have any physical interpretation all but are able to discriminate the correct fold from decoy structures. These decoy ructures are generated so as to satisfy the basic principles of protein structure such as a ose-packed, hydrophobic core [Park and Levitt 1996]. The fold library is also clearly nportant in threading. For practical purposes the library should obviously not be too irge, but it should be as representative of the different protein folds as possible. To erive a fold database one would typically first use a relatively fast sequence comparison lethod in conjunction with cluster analysis to identify families of homologues, which are ssumed to have the same fold. A sequence identity threshold of about 30% is commonly... 

Barton G J 1998. Protein Sequence Aligrunent Techniques. Acta Crystallographica 054 1139-1146. Blundell T L, B L Sibanda, M J E Sterbnerg and J M Thornton. Knowledge-based Prediction of Prote Structures and the Design of Novel Molecules. Nature 326 347-352. 

M J1990. Calculation of Conformational Ensembles from Potentials of Mean Force. An Approach o the Knowledge-Based Prediction of Local Structures in Globular Proteins. Journal of Molecular Siology 213 859-883. 

TL Blundell, BL Sibanda, MJE Sternberg, JM Thornton. Knowledge-based prediction of protein structures and the design of novel molecules. Nature 326 347-352, 1987. 

MJ Sutcliffe, I Haneef, D Carney, TL Blundell. Knowledge based modelling of homologous proteins. Part I Three dimensional frameworks derived from the simultaneous superposition of multiple structures. Protein Eng 1 377-384, 1987. 

MI Sippl. Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge-based prediction of local structures m globular proteins. I Mol Biol 213 859-883, 1990. 

For each of the 500 or so different domain structures that have so far been observed, we might at best know about a dozen of these different possible sequences. It is not trivial to recognize the general sequence patterns that are common to specific domain structures from such a limited knowledge base. 

Natural mutation of amino acids in the core of a protein can stabilize the same fold with different complementary amino acid types, but they can also cause a different fold of that particular portion. If the sequence identity is lower than 30% it is much more difficult to identify a homologous structure. Other strategies like secondary structure predictions combined with knowledge-based rules about reciprocal exchange of residues are necessary. If there is a reliable assumption for common fold then it is possible to identify intra- and intermolecular interacting residues by search for correlated complementary mutations of residues by correlated mutation analysis, CMA (see e.g., http //www.fmp-berlin.de/SSFA). 

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