Document Detail


Mixture models for protein structure ensembles.
MedLine Citation:
PMID:  18662925     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
MOTIVATION: Protein structure ensembles provide important insight into the dynamics and function of a protein and contain information that is not captured with a single static structure. However, it is not clear a priori to what extent the variability within an ensemble is caused by internal structural changes. Additional variability results from overall translations and rotations of the molecule. And most experimental data do not provide information to relate the structures to a common reference frame. To report meaningful values of intrinsic dynamics, structural precision, conformational entropy, etc., it is therefore important to disentangle local from global conformational heterogeneity. RESULTS: We consider the task of disentangling local from global heterogeneity as an inference problem. We use probabilistic methods to infer from the protein ensemble missing information on reference frames and stable conformational sub-states. To this end, we model a protein ensemble as a mixture of Gaussian probability distributions of either entire conformations or structural segments. We learn these models from a protein ensemble using the expectation-maximization algorithm. Our first model can be used to find multiple conformers in a structure ensemble. The second model partitions the protein chain into locally stable structural segments or core elements and less structured regions typically found in loops. Both models are simple to implement and contain only a single free parameter: the number of conformers or structural segments. Our models can be used to analyse experimental ensembles, molecular dynamics trajectories and conformational change in proteins. AVAILABILITY: The Python source code for protein ensemble analysis is available from the authors upon request.
Authors:
Michael Hirsch; Michael Habeck
Related Documents :
4463965 - Analysis of code relating sequences to conformation in globular prtoeins. theory and ap...
15053515 - Study of succinylated food proteins by raman spectroscopy.
18205315 - Gm1-induced structural changes of bovine serum albumin after chemical and thermal disru...
16013055 - Isotope-labeled vibrational circular dichroism studies of calmodulin and its interactio...
17055245 - Biopharmaceutical drug discovery using novel protein scaffolds.
8271975 - Production of monoclonal anti-idiotype antibodies which mimic an m-like protein of stre...
Publication Detail:
Type:  Journal Article     Date:  2008-07-28
Journal Detail:
Title:  Bioinformatics (Oxford, England)     Volume:  24     ISSN:  1367-4811     ISO Abbreviation:  Bioinformatics     Publication Date:  2008 Oct 
Date Detail:
Created Date:  2008-09-29     Completed Date:  2008-11-18     Revised Date:  2009-11-04    
Medline Journal Info:
Nlm Unique ID:  9808944     Medline TA:  Bioinformatics     Country:  England    
Other Details:
Languages:  eng     Pagination:  2184-92     Citation Subset:  IM    
Affiliation:
Department of Empirical Inference, Max-Planck-Institute for Biological Cybernetics, Spemannstrasse 38, 72076 Tübingen, Germany.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Algorithms*
Computer Simulation
Models, Molecular
Protein Conformation*
Proteins / chemistry
Chemical
Reg. No./Substance:
0/Proteins

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  ExactFDR: exact computation of false discovery rate estimate in case-control association studies.
Next Document:  Site-specific evolutionary rates in proteins are better modeled as non-independent and strictly rela...