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How do potentials derived from structural databases relate to “true” potentials?
Author(s) -
Zhang Li,
Skolnick Jeffrey
Publication year - 1998
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1002/pro.5560070112
Subject(s) - stability (learning theory) , constraint (computer aided design) , protein folding , folding (dsp implementation) , statistical physics , boltzmann constant , range (aeronautics) , boltzmann distribution , set (abstract data type) , physics , inverse , statistical potential , protein structure , database , chemistry , biological system , computer science , mathematics , protein structure prediction , thermodynamics , materials science , geometry , biology , nuclear magnetic resonance , machine learning , electrical engineering , composite material , programming language , engineering
Abstract Knowledge‐based potentials are used widely in protein folding and inverse folding algorithms. Two kinds of derivation methods are used. (1) The interactions in a database of known protein structures are assumed to obey a Boltzmann distribution. (2) The stability of the native folds relative to a manifold of misfolded structures is optimized. Here, a set of previously derived contact and secondary structure propensity potentials, taken as the “true” potentials, are employed to construct an artificial protein structural database from protein fragments. Then, new sets of potentials are derived to see how they are related to the true potentials. Using the Boltzmann distribution method, when the stability of the structures in the database lies within a certain range, both contact potentials and secondary structure propensities can be derived separately with remarkable accuracy. In general, the optimization method was found to be less accurate due to errors in the “excess energy” contribution. When the excess energy terms are kept as a constraint, the true potentials are recovered exactly.