Title | Prediction of Protein Configurational Entropy (Popcoen). |
Publication Type | Journal Article |
Year of Publication | 2018 |
Authors | Goethe M, Gleixner J, Fita I, J Rubi M |
Journal | J Chem Theory Comput |
Volume | 14 |
Issue | 3 |
Pagination | 1811-1819 |
Date Published | 2018 Mar 13 |
ISSN | 1549-9626 |
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/ . |
DOI | 10.1021/acs.jctc.7b01079 |
Alternate Journal | J Chem Theory Comput |
PubMed ID | 29351717 |