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• W2784948614 abstract "A knowledge-based method for configurational entropy prediction of proteins is presented; this methodology is extremely fast, compared to previous approaches, because it does not involve any type of configurational sampling. Instead, the configurational entropy of a query fold is estimated by evaluating an artificial neural network, which was trained on molecular-dynamics simulations of ∼1000 proteins. The predicted entropy can be incorporated into a large class of protein software based on cost-function minimization/evaluation, in which configurational entropy is currently neglected for performance reasons. Software of this type is used for all major protein tasks such as structure predictions, proteins design, NMR and X-ray refinement, docking, and mutation effect predictions. Integrating the predicted entropy can yield a significant accuracy increase as we show exemplarily for native-state identification with the prominent protein software FoldX. The method has been termed Popcoen for Prediction of Protein Configurational Entropy. An implementation is freely available at http://fmc.ub.edu/popcoen/ ." @default.
• W2784948614 created "2018-02-02" @default.
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• W2784948614 date "2018-01-19" @default.
• W2784948614 modified "2023-10-16" @default.
• W2784948614 title "Prediction of Protein Configurational Entropy (Popcoen)" @default.
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• W2784948614 doi "https://doi.org/10.1021/acs.jctc.7b01079" @default.
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