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Optimizing physical energy functions for protein folding
Author(s) -
Fujitsuka Yoshimi,
Takada Shoji,
LutheySchulten Zaida A.,
Wolynes Peter G.
Publication year - 2003
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.10429
Subject(s) - decoy , protein structure prediction , benchmark (surveying) , folding (dsp implementation) , stability (learning theory) , energy (signal processing) , computer science , energy landscape , monte carlo method , function (biology) , sampling (signal processing) , root mean square , algorithm , protein structure , statistics , mathematics , physics , machine learning , chemistry , engineering , biology , filter (signal processing) , receptor , biochemistry , geodesy , quantum mechanics , evolutionary biology , computer vision , thermodynamics , nuclear magnetic resonance , electrical engineering , geography
We optimize a physical energy function for proteins with the use of the available structural database and perform three benchmark tests of the performance: (1) recognition of native structures in the background of predefined decoy sets of Levitt, (2) de novo structure prediction using fragment assembly sampling, and (3) molecular dynamics simulations. The energy parameter optimization is based on the energy landscape theory and uses a Monte Carlo search to find a set of parameters that seeks the largest ratio δ E s /Δ E for all proteins in a training set simultaneously. Here, δ E s is the stability gap between the native and the average in the denatured states and Δ E is the energy fluctuation among these states. Some of the energy parameters optimized are found to show significant correlation with experimentally observed quantities: (1) In the recognition test, the optimized function assigns the lowest energy to either the native or a near‐native structure among many decoy structures for all the proteins studied. (2) Structure prediction with the fragment assembly sampling gives structure models with root mean square deviation less than 6 Å in one of the top five cluster centers for five of six proteins studied. (3) Structure prediction using molecular dynamics simulation gives poorer performance, implying the importance of having a more precise description of local structures. The physical energy function solely inferred from a structural database neither utilizes sequence information from the family of the target nor the outcome of the secondary structure prediction but can produce the correct native fold for many small proteins. Proteins 2004;54:000–000. © 2003 Wiley‐Liss, Inc.