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Coarse-grain modelling of protein-protein interactions
- Source :
- Current Opinion in Structural Biology, Current Opinion in Structural Biology, Elsevier, 2013, pp.878-86, Current Opinion in Structural Biology, 23(6), 878-886
- Publication Year :
- 2013
- Publisher :
- HAL CCSD, 2013.
-
Abstract
- International audience; Here, we review recent advances towards the modelling of protein-protein interactions (PPI) at the coarse-grained (CG) level, a technique that is now widely used to understand protein affinity, aggregation and self-assembly behaviour. PPI models of soluble proteins and membrane proteins are separately described, but we note the parallel development that is present in both research fields with three important themes: firstly, combining CG modelling with knowledge-based approaches to predict and refine protein-protein complexes; secondly, using physics-based CG models for de novo prediction of protein-protein complexes; and thirdly modelling of large scale protein aggregates.
- Subjects :
- Models, Molecular
MOLECULAR-DYNAMICS SIMULATIONS
Self association
LIPID INTERACTIONS
Computational biology
Protein aggregation
01 natural sciences
Force field (chemistry)
Protein–protein interaction
COMPUTER-SIMULATION
03 medical and health sciences
Structural Biology
0103 physical sciences
Protein Interaction Mapping
TRANSMEMBRANE HELIX
DOCKING
Molecular Biology
030304 developmental biology
Physics
0303 health sciences
010304 chemical physics
MEMBRANE-PROTEINS
Membrane Proteins
[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
SELF-ASSOCIATION
Membrane protein
Solubility
Docking (molecular)
Biophysics
FORCE-FIELD
HYDRODYNAMIC INTERACTIONS
Protein–protein interaction prediction
COMPLEXES
Subjects
Details
- Language :
- English
- ISSN :
- 0959440X
- Database :
- OpenAIRE
- Journal :
- Current Opinion in Structural Biology, Current Opinion in Structural Biology, Elsevier, 2013, pp.878-86, Current Opinion in Structural Biology, 23(6), 878-886
- Accession number :
- edsair.doi.dedup.....8c1c9ff2516de02b646e6363b2c2f4f3