Knowledge-based structural prediction

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. 

Payne, M.P and Walsh, P.T., Structure-activity relationships for skin sensitization potential development of structural alerts for use in knowledge-based toxicity prediction systems, J. Chem. Inf. Comput. Sci., 34, 154-161, 1994. 

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. 

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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. 

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). 

Knowledge-based methods are those based on the application of certain rules to describe the metabolism. These rules could be defined as chemical reactions relating structure and biotransformations to predict the metabolic fate of a query chemical structure, as in the Meteor approach [26], or alternatively they could be obtained by fragment analysis of a metabolic database as performed in the SPORCalc (Substrate Product Occurrence Ratio Calculator) system [27]. 

In this sense, expert systems for the prediction of chemcial reactions for the design of organic syntheses, for the prediction of physical data, for structure elucidation, and for QSAR can be founded on the knowledge base comprized by the models presented here. 

METEOR S biotransformation rules are generic reaction descriptors, and the versatile structural representation used in the system allows each atom or bond to have specific physicochemical properties. This approach provides more details than simple hard-coded functional group descriptors (313), but this flexibility also can give rise to an avalanche of data. METEOR manages the amount of data by predicting which metabolites are to be formed rather than all the possible outcomes (310,312,314,315). At high certainty levels, when chosen, only the more likely biotransformations are requested. At lower likelihood levels, the more common metabolites are also selected for examination. Currently, METEOR knowledge-based biotransformations are exclusively for mammalian biotransformations (phase I and phase II) (314,315). 

Perkins, R., Anson, J., Blair, R., Branham, W.S., Dial, S., Fang, H., Hass, B.S., Moland, C., Shi, L., Tong, W., Welsh, W., Walker, J.D., and Sheehan, D.M., The endocrine disruptor knowledge base (EDKB), a prototype toxicological knowledge base for endocrine disrupting chemicals, in Handbook on Quantitative Structure Activity Relationships (QSARs) for Predicting Chemical Endocrine Disruption Potentials, Walker, J.D., Ed., SETAC Press, Pensacola, FL, 2003 (in press). 

Knowledge-Based Prediction of Protein Structures and the Design of Novel Molecules. 

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