Knowledge-based potential function

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... 

With this database in hand, a simple question is asked [29] How different is a knowledge-based potential derived from this lattice database compared to the actual energy function used to construct the database If statistical errors are negligible and the knowledge-based method is perfect, the answer is expected to be They are exactly the same. ... 

Yang CY, Wang RX, Wang SM. M-score a knowledge-based potential scoring function accounting for protein atom mobility. J. Med. Chem. 2006 49 5903-5911. 

Besides differences in the functional form and reference state, from a more practical point of view, the knowledge-based potentials differ also with respect to scope of atom type definitions and the amount of structural data used for their derivation. The number of different atom types ranges from 17 in Drug-Score to 40 nonmetal atom types in BLEEP. In all cases, the Protein Data Bank (321) was the source of the solved crystal structures. For BEEEP 351 selected complexes were used, whereas the PMF function was extracted from 697 complexes, and DrugScore was derived using 1376 complexes. In the latter case, the data have been extracted from Relibase (322,323). 

It can be seen from Eq. (18) that the improvement for the potentials Ujj(r) depends only on the difference between the predicted and experimentally observed pair distribution functions instead of any properties related to the reference state. Therefore, the iterative method does not face the reference state problem encountered by traditional mean-force/knowledge-based scoring functions. 

Huang, S.-Y., Zou, X. An iterative knowledge-based scoring function to predict protein-ligand interactions I. Derivation of interaction potentials. J. Comput. Chem. 2006, 27,1865-75. 

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