Characterizing protein energy landscape by self-learning multiscale simulations: application to a designed β-hairpin.
about
Frustration, specific sequence dependence, and nonlinearity in large-amplitude fluctuations of allosteric proteins.Proximal distributions from angular correlations: a measure of the onset of coarse-graining.Perspective: Coarse-grained models for biomolecular systems.Binding modes of Bcl-2 homology 3 (BH3) peptides with anti-apoptotic protein A1 and redesign of peptide inhibitors: a computational study.Metal cofactor modulated folding and target recognition of HIV-1 NCp7.
P2860
Characterizing protein energy landscape by self-learning multiscale simulations: application to a designed β-hairpin.
description
2010 nî lūn-bûn
@nan
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
2010年论文
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2010年论文
@zh-cn
name
Characterizing protein energy ...... ation to a designed β-hairpin.
@en
Characterizing protein energy ...... ation to a designed β-hairpin.
@nl
type
label
Characterizing protein energy ...... ation to a designed β-hairpin.
@en
Characterizing protein energy ...... ation to a designed β-hairpin.
@nl
prefLabel
Characterizing protein energy ...... ation to a designed β-hairpin.
@en
Characterizing protein energy ...... ation to a designed β-hairpin.
@nl
P2860
P1433
P1476
Characterizing protein energy ...... ation to a designed β-hairpin.
@en
P2093
Shoji Takada
P2860
P304
P356
10.1016/J.BPJ.2010.08.041
P407
P50
P577
2010-11-01T00:00:00Z