Knowledge-based potential of mean forc

A. Babajide, I.L. Hofacker, M.J. Sippl, and P.F. Stadler. Neutral networks in protein space - a computational study based on knowledge-based potentials of mean force. Folding Design, 2 261-269,1997. 

Nobeli, I., Mitchell, J.B.O., Alex, A., Thornton, J.M. Evaluation of a knowledge-based potential of mean force for scoring docked protein-ligand complexes. J. Comput. Chem. 2001, 22, 673-88. 

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. 

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. 

Unlike the simple monatomic system in which the p can be exactly obtained by randomizing all the atoms in the system, the reference state p j r) for the complicated protein system is inaccessible due to the effects of connectivity, excluded volume, composition, etc. [45]. Therefore, the pair potentials defined in Eq. (5) are not the exact potentials of mean force in physics. Also, similar to the potential of mean force, the knowledge-based potentials of Eq. (5) are not the true interaction potentials, either. 

Knowledge-based functions are based on the derivation of statistical preferences in the form of potentials for protein ligand atom pair interactions. Similar to potentials derived for protein folding and protein structure evaluation (e.g., Ref. 148), pair potentials akin to potentials of mean force (PMFs) are derived for various protein and ligand atom types using the PDB as a knowledge base. The PMF scoring function [118]... 

Potentials of Mean Force. An Approach to the Knowledge-Based Prediction of Local Structures in Globular Proteins. 

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. 

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