PMID- 19063551
OWN - NLM
STAT- MEDLINE
DCOM- 20090202
LR  - 20131121
IS  - 1089-7690 (Electronic)
IS  - 0021-9606 (Linking)
VI  - 129
IP  - 21
DP  - 2008 Dec 7
TI  - A Bayesian statistics approach to multiscale coarse graining.
PG  - 214114
LID - 10.1063/1.3033218 [doi]
AB  - Coarse-grained (CG) modeling provides a promising way to investigate many 
      important physical and biological phenomena over large spatial and temporal 
      scales. The multiscale coarse-graining (MS-CG) method has been proven to be a 
      thermodynamically consistent way to systematically derive a CG model from 
      atomistic force information, as shown in a variety of systems, ranging from 
      simple liquids to proteins embedded in lipid bilayers. In the present work, 
      Bayes' theorem, an advanced statistical tool widely used in signal processing and 
      pattern recognition, is adopted to further improve the MS-CG force field obtained 
      from the CG modeling. This approach can regularize the linear equation resulting 
      from the underlying force-matching methodology, therefore substantially improving 
      the quality of the MS-CG force field, especially for the regions with limited 
      sampling. Moreover, this Bayesian approach can naturally provide an error 
      estimation for each force field parameter, from which one can know the extent the 
      results can be trusted. The robustness and accuracy of the Bayesian MS-CG 
      algorithm is demonstrated for three different systems, including simple liquid 
      methanol, polyalanine peptide solvated in explicit water, and a much more 
      complicated peptide assembly with 32 NNQQNY hexapeptides.
FAU - Liu, Pu
AU  - Liu P
AD  - Center for Biophysical Modeling and Simulation and Department of Chemistry, 
      University of Utah, 315 S. 1400 E. Rm. 2020, Salt Lake City, Utah 84112-0850, 
      USA.
FAU - Shi, Qiang
AU  - Shi Q
FAU - Daume, Hal 3rd
AU  - Daume H 3rd
FAU - Voth, Gregory A
AU  - Voth GA
LA  - eng
PT  - Journal Article
PT  - Research Support, U.S. Gov't, Non-P.H.S.
PL  - United States
TA  - J Chem Phys
JT  - The Journal of chemical physics
JID - 0375360
RN  - 0 (Peptides)
RN  - 25191-17-7 (polyalanine)
RN  - Y4S76JWI15 (Methanol)
SB  - IM
MH  - Algorithms
MH  - Amino Acid Sequence
MH  - *Bayes Theorem
MH  - Methanol/chemistry
MH  - *Models, Molecular
MH  - Peptides/chemistry
MH  - Protein Binding
MH  - Protein Conformation
EDAT- 2008/12/10 09:00
MHDA- 2009/02/03 09:00
CRDT- 2008/12/10 09:00
PHST- 2008/12/10 09:00 [pubmed]
PHST- 2009/02/03 09:00 [medline]
PHST- 2008/12/10 09:00 [entrez]
AID - 10.1063/1.3033218 [doi]
PST - ppublish
SO  - J Chem Phys. 2008 Dec 7;129(21):214114. doi: 10.1063/1.3033218.