Estimating absolute configurational entropies of macromolecules: the minimally coupled subspace approach.
We develop a general minimally coupled subspace approach (MCSA) to compute absolute entropies of macromolecules, such as proteins, from computer generated canonical ensembles. Our approach overcomes limitations of current estimates such as the quasi-harmonic approximation which neglects non-linear and higher-order correlations as well as multi-minima characteristics of protein energy landscapes. Here, Full Correlation Analysis, adaptive kernel density estimation, and mutual information expansions are combined and high accuracy is demonstrated for a number of test systems ranging from alkanes to a 14 residue peptide. We further computed the configurational entropy for the full 67-residue cofactor of the TATA box binding protein illustrating that MCSA yields improved results also for large macromolecular systems.
Hensen
Ulf
U
Department of Theoretical and Computational Biophysics, Max-Planck Institute for Biophysical Chemistry, Göttingen, Germany.
Lange
Oliver F
OF
Grubmüller
Helmut
H
D000465:Algorithms; D003198:Computer Simulation; D019277:Entropy; D046911:Macromolecular Substances / Q000737:chemistry*; D008956:Models, Chemical; D008958:Models, Molecular; D010455:Peptides / Q000737:chemistry; D011485:Protein Binding; D011487:Protein Conformation; D017510:Protein Folding; D035181:TATA-Box Binding Protein / Q000737:chemistry; D013816:Thermodynamics
D046911:Macromolecular Substances; D010455:Peptides; D035181:TATA-Box Binding Protein